human/dist/human.esm.js

48837 lines
2.0 MiB

/*
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: () => js,
Acos: () => Vo,
Acosh: () => Wo,
AdadeltaOptimizer: () => ep,
AdagradOptimizer: () => tp,
AdamOptimizer: () => rp,
AdamaxOptimizer: () => op,
Add: () => io,
AddN: () => Uo,
All: () => Go,
Any: () => Ho,
ArgMax: () => Xs,
ArgMin: () => Ys,
Asin: () => Ko,
Asinh: () => qo,
Atan: () => jo,
Atan2: () => Yo,
Atanh: () => Xo,
AvgPool: () => Qo,
AvgPool3D: () => Qs,
AvgPool3DGrad: () => Ei,
AvgPoolGrad: () => $i,
BackendWasm: () => am,
BatchMatMul: () => Zo,
BatchToSpaceND: () => Zs,
Bincount: () => Jo,
BitwiseAnd: () => Ha,
BroadcastArgs: () => Js,
BroadcastTo: () => _ce,
Cast: () => yo,
Ceil: () => en,
ClipByValue: () => bo,
Complex: () => Ri,
ComplexAbs: () => Di,
Concat: () => ea,
Conv2D: () => tn,
Conv2DBackpropFilter: () => Ai,
Conv2DBackpropInput: () => rn,
Conv3D: () => on,
Conv3DBackpropFilterV2: () => Ka,
Conv3DBackpropInputV2: () => nn,
Cos: () => sn,
Cosh: () => an,
CropAndResize: () => cn,
Cumprod: () => un,
Cumsum: () => pn,
DataStorage: () => Bo,
DenseBincount: () => ta,
DepthToSpace: () => ln,
DepthwiseConv2dNative: () => mn,
DepthwiseConv2dNativeBackpropFilter: () => Fi,
DepthwiseConv2dNativeBackpropInput: () => Pi,
Diag: () => ra,
Dilation2D: () => dn,
Dilation2DBackpropFilter: () => Mi,
Dilation2DBackpropInput: () => Oi,
Draw: () => qm,
ENV: () => QC,
Einsum: () => Li,
Elu: () => hn,
EluGrad: () => qa,
Environment: () => ll,
Equal: () => xn,
Erf: () => gn,
Exp: () => yn,
ExpandDims: () => oa,
Expm1: () => bn,
FFT: () => Bi,
Fill: () => na,
FlipLeftRight: () => Cn,
Floor: () => wn,
FloorDiv: () => Sn,
FromPixels: () => Au,
FusedBatchNorm: () => In,
FusedConv2D: () => Io,
FusedDepthwiseConv2D: () => vo,
GPGPUContext: () => wp,
GatherNd: () => vn,
GatherV2: () => sa,
GraphModel: () => Ol,
Greater: () => kn,
GreaterEqual: () => Nn,
IFFT: () => zi,
Identity: () => Co,
Imag: () => Vi,
IsFinite: () => Tn,
IsInf: () => _n,
IsNan: () => $n,
KernelBackend: () => so,
LRN: () => Bn,
LRNGrad: () => ja,
LeakyRelu: () => En,
Less: () => Rn,
LessEqual: () => Dn,
LinSpace: () => An,
Log: () => Fn,
Log1p: () => Pn,
LogSoftmax: () => $ce,
LogicalAnd: () => On,
LogicalNot: () => Mn,
LogicalOr: () => Ln,
LogicalXor: () => w0,
LowerBound: () => Ece,
MathBackendCPU: () => hu,
MathBackendWebGL: () => bu,
MatrixBandPart: () => Rce,
Max: () => zn,
MaxPool: () => Wn,
MaxPool3D: () => aa,
MaxPool3DGrad: () => Ui,
MaxPoolGrad: () => Wi,
MaxPoolWithArgmax: () => ia,
Maximum: () => Vn,
Mean: () => Un,
Min: () => Gn,
Minimum: () => Hn,
MirrorPad: () => Kn,
Mod: () => qn,
MomentumOptimizer: () => np,
Multinomial: () => jn,
Multiply: () => Xn,
Neg: () => ua,
NonMaxSuppressionV3: () => Qn,
NonMaxSuppressionV4: () => Xa,
NonMaxSuppressionV5: () => Zn,
NotEqual: () => Yn,
OP_SCOPE_SUFFIX: () => yw,
OneHot: () => Jn,
OnesLike: () => pa,
Optimizer: () => Nr,
OptimizerConstructors: () => Dl,
Pack: () => ca,
PadV2: () => es,
Pool: () => Dce,
Pow: () => ts,
Prelu: () => rs,
Prod: () => os,
RMSPropOptimizer: () => sp,
RaggedGather: () => jp,
RaggedRange: () => Xp,
RaggedTensorToTensor: () => Yp,
Range: () => la,
Rank: () => uw,
Real: () => Gi,
RealDiv: () => fn,
Reciprocal: () => ns,
Reduction: () => Et,
Relu: () => ss,
Relu6: () => us,
Reshape: () => ma,
ResizeBilinear: () => is,
ResizeBilinearGrad: () => Qa,
ResizeNearestNeighbor: () => as,
ResizeNearestNeighborGrad: () => Ya,
Reverse: () => ps,
RotateWithOffset: () => Ds,
Round: () => cs,
Rsqrt: () => ls,
SGDOptimizer: () => ci,
ScatterNd: () => ms,
SearchSorted: () => fs,
Select: () => da,
Selu: () => hs,
Sigmoid: () => bs,
Sign: () => ys,
Sin: () => gs,
Sinh: () => xs,
Slice: () => fa,
Softmax: () => Is,
Softplus: () => Cs,
SpaceToBatchND: () => ha,
SparseFillEmptyRows: () => Hi,
SparseReshape: () => Za,
SparseSegmentMean: () => Ki,
SparseSegmentSum: () => qi,
SparseToDense: () => vs,
SplitV: () => ga,
Sqrt: () => ws,
Square: () => ji,
SquaredDifference: () => ks,
StaticRegexReplace: () => Du,
Step: () => wo,
StridedSlice: () => Ns,
StringNGrams: () => xa,
StringSplit: () => Xi,
StringToHashBucketFast: () => Yi,
Sub: () => Ts,
Sum: () => Ss,
Tan: () => _s,
Tanh: () => $s,
Tensor: () => ut,
TensorBuffer: () => tt,
TensorScatterUpdate: () => ds,
Tile: () => uo,
TopK: () => Es,
Transform: () => Rs,
Transpose: () => po,
Unique: () => Qi,
Unpack: () => ya,
UnsortedSegmentSum: () => Zi,
UpperBound: () => Ace,
Variable: () => ei,
WebGPUBackend: () => vu,
ZerosLike: () => ba,
_FusedMatMul: () => So,
abs: () => Jt,
acos: () => xk,
acosh: () => yk,
add: () => Ce,
addN: () => bk,
all: () => Ck,
any: () => wk,
argMax: () => Sk,
argMin: () => Ik,
asin: () => vk,
asinh: () => kk,
atan: () => Nk,
atan2: () => Tk,
atanh: () => _k,
avgPool: () => md,
avgPool3d: () => Rk,
backend: () => dde,
backend_util: () => w,
basicLSTMCell: () => Dk,
batchNorm: () => su,
batchNorm2d: () => Fk,
batchNorm3d: () => Pk,
batchNorm4d: () => Ok,
batchToSpaceND: () => dd,
bincount: () => fd,
bitwiseAnd: () => Mk,
booleanMaskAsync: () => b6,
broadcastArgs: () => Lk,
broadcastTo: () => au,
broadcast_util: () => Ir,
browser: () => XN,
buffer: () => me,
cast: () => qe,
ceil: () => Bk,
clipByValue: () => zk,
clone: () => Ur,
complex: () => Er,
concat: () => bt,
concat1d: () => Vk,
concat2d: () => Wk,
concat3d: () => Uk,
concat4d: () => Gk,
conv1d: () => Hk,
conv2d: () => iu,
conv2dTranspose: () => Kk,
conv3d: () => qk,
conv3dTranspose: () => Xk,
copyRegisteredKernels: () => Vce,
cos: () => Yk,
cosh: () => Qk,
cosineWindow: () => _l,
cumprod: () => Zk,
cumsum: () => Jk,
customGrad: () => vr,
denseBincount: () => e2,
deprecationWarn: () => _w,
depthToSpace: () => t2,
depthwiseConv2d: () => ic,
deregisterOp: () => b5,
device_util: () => ru,
diag: () => r2,
dilation2d: () => o2,
disableDeprecationWarnings: () => rde,
dispose: () => Ot,
disposeVariables: () => ode,
div: () => je,
divNoNan: () => s2,
dot: () => a2,
dropout: () => R6,
einsum: () => i2,
elu: () => yd,
enableDebugMode: () => tde,
enableProdMode: () => ede,
enclosingPowerOfTwo: () => Gw,
engine: () => ur,
ensureShape: () => u2,
env: () => A,
equal: () => xd,
erf: () => p2,
euclideanNorm: () => m2,
exp: () => _o,
expandDims: () => ai,
expm1: () => d2,
eye: () => bd,
fft: () => cc,
fill: () => Ta,
findBackend: () => lde,
findBackendFactory: () => mde,
floor: () => Cd,
floorDiv: () => ld,
forceHalfFloat: () => $D,
fused: () => Hw,
gather: () => wd,
gatherND: () => $6,
gather_util: () => nf,
getBackend: () => pde,
getGradient: () => ew,
getKernel: () => fl,
getKernelsForBackend: () => jm,
getThreadsCount: () => Lse,
gpgpu_util: () => rv,
grad: () => SK,
grads: () => IK,
greater: () => Uu,
greaterEqual: () => Sd,
ifft: () => Xu,
imag: () => pu,
image: () => Fj,
inTopKAsync: () => A6,
io: () => mi,
irfft: () => Gd,
isFinite: () => f2,
isInf: () => h2,
isNaN: () => g2,
keep: () => Rr,
kernel_impls: () => Wt,
leakyRelu: () => Id,
less: () => kl,
lessEqual: () => uc,
linalg: () => Pj,
linspace: () => x2,
loadGraphModel: () => h8,
loadGraphModelSync: () => g8,
localResponseNormalization: () => y2,
log: () => ii,
log1p: () => vd,
logSigmoid: () => b2,
logSoftmax: () => C2,
logSumExp: () => Td,
logicalAnd: () => Gu,
logicalNot: () => _d,
logicalOr: () => $d,
logicalXor: () => w2,
losses: () => Oj,
lowerBound: () => S2,
matMul: () => Ze,
math: () => HN,
max: () => _a,
maxPool: () => Rd,
maxPool3d: () => I2,
maxPoolWithArgmax: () => v2,
maximum: () => Dd,
mean: () => Hu,
memory: () => nde,
meshgrid: () => k2,
min: () => vl,
minimum: () => Ku,
mirrorPad: () => N2,
mod: () => T2,
moments: () => _2,
movingAverage: () => S6,
mul: () => se,
multiRNNCell: () => $2,
multinomial: () => E2,
neg: () => pr,
nextFrame: () => tS,
norm: () => Wu,
notEqual: () => Ad,
oneHot: () => Tl,
ones: () => $a,
onesLike: () => R2,
op: () => N,
outerProduct: () => D2,
pad: () => Ea,
pad1d: () => A2,
pad2d: () => F2,
pad3d: () => P2,
pad4d: () => O2,
pool: () => M2,
pow: () => si,
prelu: () => Pd,
print: () => cd,
prod: () => L2,
profile: () => sde,
raggedGather: () => B2,
raggedRange: () => z2,
raggedTensorToTensor: () => V2,
rand: () => W2,
randomGamma: () => p1,
randomNormal: () => Vd,
randomStandardNormal: () => c1,
randomUniform: () => pc,
randomUniformInt: () => l1,
range: () => cu,
ready: () => ude,
real: () => ui,
reciprocal: () => m1,
registerBackend: () => nu,
registerGradient: () => Lce,
registerKernel: () => Ja,
registerOp: () => y5,
relu: () => lu,
relu6: () => Wd,
removeBackend: () => cde,
reshape: () => W,
reverse: () => lo,
reverse1d: () => d1,
reverse2d: () => f1,
reverse3d: () => h1,
reverse4d: () => g1,
rfft: () => lc,
round: () => Ud,
rsqrt: () => x1,
scalar: () => ke,
scatterND: () => v6,
scatter_util: () => du,
searchSorted: () => Nl,
selu: () => y1,
separableConv2d: () => b1,
serialization: () => FN,
setBackend: () => ide,
setPlatform: () => fde,
setThreadsCount: () => Mse,
setWasmPath: () => Pse,
setWasmPaths: () => Ose,
setWebGLContext: () => gI,
setdiff1dAsync: () => C1,
shared: () => Ic,
sigmoid: () => Na,
sign: () => w1,
signal: () => Aj,
sin: () => S1,
sinh: () => I1,
slice: () => Xe,
slice1d: () => v1,
slice2d: () => k1,
slice3d: () => N1,
slice4d: () => T1,
slice_util: () => ct,
softmax: () => _1,
softplus: () => Nd,
spaceToBatchND: () => Fd,
sparse: () => Mj,
sparseToDense: () => T6,
spectral: () => Dj,
split: () => pi,
sqrt: () => Dr,
square: () => er,
squaredDifference: () => Hd,
squeeze: () => mc,
stack: () => kr,
step: () => Kd,
stridedSlice: () => $1,
string: () => Lj,
sub: () => Te,
sum: () => ot,
sumOutType: () => ti,
tan: () => E1,
tanh: () => Il,
tensor: () => ir,
tensor1d: () => xr,
tensor2d: () => mu,
tensor3d: () => qd,
tensor4d: () => R1,
tensor5d: () => D1,
tensor6d: () => A1,
tensorScatterUpdate: () => P1,
tensor_util: () => K0,
test_util: () => u1,
tidy: () => De,
tile: () => uu,
time: () => ade,
topk: () => O1,
train: () => uGe,
transpose: () => fc,
truncatedNormal: () => M1,
unique: () => L1,
unregisterGradient: () => zce,
unregisterKernel: () => Bce,
unsortedSegmentSum: () => B1,
unstack: () => mo,
upcastType: () => dt,
upperBound: () => z1,
util: () => y,
valueAndGrad: () => vK,
valueAndGrads: () => kK,
variable: () => V1,
variableGrads: () => Aw,
version: () => Sce,
version_converter: () => y8,
version_core: () => fX,
version_cpu: () => Y8,
version_wasm: () => Bse,
version_webgl: () => HZ,
webgl: () => tat,
webgl_util: () => $c,
webgpu_util: () => Wv,
where: () => co,
whereAsync: () => Xd,
zeros: () => Gr,
zerosLike: () => Ht
});
var cG = Object.create;
var GC = Object.defineProperty;
var lG = Object.getOwnPropertyDescriptor;
var mG = Object.getOwnPropertyNames;
var dG = Object.getPrototypeOf;
var fG = Object.prototype.hasOwnProperty;
var qt = (r, e) => () => (e || r((e = { exports: {} }).exports, e), e.exports);
var Ke = (r, e) => {
for (var t8 in e)
GC(r, t8, { get: e[t8], enumerable: true });
};
var hG = (r, e, t8, o) => {
if (e && typeof e == "object" || typeof e == "function")
for (let n of mG(e))
!fG.call(r, n) && n !== t8 && GC(r, n, { get: () => e[n], enumerable: !(o = lG(e, n)) || o.enumerable });
return r;
};
var Up = (r, e, t8) => (t8 = r != null ? cG(dG(r)) : {}, hG(e || !r || !r.__esModule ? GC(t8, "default", { value: r, enumerable: true }) : t8, r));
var A0 = qt((Gce, D0) => {
D0.exports = kt;
var ko = null;
try {
ko = 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 kt(r, e, t8) {
this.low = r | 0, this.high = e | 0, this.unsigned = !!t8;
}
kt.prototype.__isLong__;
Object.defineProperty(kt.prototype, "__isLong__", { value: true });
function Wr(r) {
return (r && r.__isLong__) === true;
}
kt.isLong = Wr;
var I0 = {}, v0 = {};
function Pu(r, e) {
var t8, o, n;
return e ? (r >>>= 0, (n = 0 <= r && r < 256) && (o = v0[r], o) ? o : (t8 = Nt(r, (r | 0) < 0 ? -1 : 0, true), n && (v0[r] = t8), t8)) : (r |= 0, (n = -128 <= r && r < 128) && (o = I0[r], o) ? o : (t8 = Nt(r, r < 0 ? -1 : 0, false), n && (I0[r] = t8), t8));
}
kt.fromInt = Pu;
function No(r, e) {
if (isNaN(r))
return e ? Fu : To;
if (e) {
if (r < 0)
return Fu;
if (r >= _0)
return R0;
} else {
if (r <= -N0)
return Vr;
if (r + 1 >= N0)
return E0;
}
return r < 0 ? No(-r, e).neg() : Nt(r % Jp | 0, r / Jp | 0, e);
}
kt.fromNumber = No;
function Nt(r, e, t8) {
return new kt(r, e, t8);
}
kt.fromBits = Nt;
var Ym = Math.pow;
function ow(r, e, t8) {
if (r.length === 0)
throw Error("empty string");
if (r === "NaN" || r === "Infinity" || r === "+Infinity" || r === "-Infinity")
return To;
if (typeof e == "number" ? (t8 = e, e = false) : e = !!e, t8 = t8 || 10, t8 < 2 || 36 < t8)
throw RangeError("radix");
var o;
if ((o = r.indexOf("-")) > 0)
throw Error("interior hyphen");
if (o === 0)
return ow(r.substring(1), e, t8).neg();
for (var n = No(Ym(t8, 8)), s = To, a = 0; a < r.length; a += 8) {
var i = Math.min(8, r.length - a), p = parseInt(r.substring(a, a + i), t8);
if (i < 8) {
var u = No(Ym(t8, i));
s = s.mul(u).add(No(p));
} else
s = s.mul(n), s = s.add(No(p));
}
return s.unsigned = e, s;
}
kt.fromString = ow;
function As(r, e) {
return typeof r == "number" ? No(r, e) : typeof r == "string" ? ow(r, e) : Nt(r.low, r.high, typeof e == "boolean" ? e : r.unsigned);
}
kt.fromValue = As;
var k0 = 65536, LG = 1 << 24, Jp = k0 * k0, _0 = Jp * Jp, N0 = _0 / 2, T0 = Pu(LG), To = Pu(0);
kt.ZERO = To;
var Fu = Pu(0, true);
kt.UZERO = Fu;
var Zp = Pu(1);
kt.ONE = Zp;
var $0 = Pu(1, true);
kt.UONE = $0;
var rw = Pu(-1);
kt.NEG_ONE = rw;
var E0 = Nt(-1, 2147483647, false);
kt.MAX_VALUE = E0;
var R0 = Nt(-1, -1, true);
kt.MAX_UNSIGNED_VALUE = R0;
var Vr = Nt(0, -2147483648, false);
kt.MIN_VALUE = Vr;
var de = kt.prototype;
de.toInt = function() {
return this.unsigned ? this.low >>> 0 : this.low;
};
de.toNumber = function() {
return this.unsigned ? (this.high >>> 0) * Jp + (this.low >>> 0) : this.high * Jp + (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(Vr)) {
var t8 = No(e), o = this.div(t8), n = o.mul(t8).sub(this);
return o.toString(e) + n.toInt().toString(e);
} else
return "-" + this.neg().toString(e);
for (var s = No(Ym(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(Vr) ? 64 : this.neg().getNumBitsAbs();
for (var e = this.high != 0 ? this.high : this.low, t8 = 31; t8 > 0 && !(e & 1 << t8); t8--)
;
return this.high != 0 ? t8 + 33 : t8 + 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 Wr(e) || (e = As(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 (Wr(e) || (e = As(e)), this.eq(e))
return 0;
var t8 = this.isNegative(), o = e.isNegative();
return t8 && !o ? -1 : !t8 && 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(Vr) ? Vr : this.not().add(Zp);
};
de.neg = de.negate;
de.add = function(e) {
Wr(e) || (e = As(e));
var t8 = 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 += t8 + a, c &= 65535, Nt(m << 16 | d, c << 16 | l, this.unsigned);
};
de.subtract = function(e) {
return Wr(e) || (e = As(e)), this.add(e.neg());
};
de.sub = de.subtract;
de.multiply = function(e) {
if (this.isZero())
return To;
if (Wr(e) || (e = As(e)), ko) {
var t8 = ko.mul(this.low, this.high, e.low, e.high);
return Nt(t8, ko.get_high(), this.unsigned);
}
if (e.isZero())
return To;
if (this.eq(Vr))
return e.isOdd() ? Vr : To;
if (e.eq(Vr))
return this.isOdd() ? Vr : To;
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(T0) && e.lt(T0))
return No(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, Nt(d << 16 | f, l << 16 | m, this.unsigned);
};
de.mul = de.multiply;
de.divide = function(e) {
if (Wr(e) || (e = As(e)), e.isZero())
throw Error("division by zero");
if (ko) {
if (!this.unsigned && this.high === -2147483648 && e.low === -1 && e.high === -1)
return this;
var t8 = (this.unsigned ? ko.div_u : ko.div_s)(this.low, this.high, e.low, e.high);
return Nt(t8, ko.get_high(), this.unsigned);
}
if (this.isZero())
return this.unsigned ? Fu : To;
var o, n, s;
if (this.unsigned) {
if (e.unsigned || (e = e.toUnsigned()), e.gt(this))
return Fu;
if (e.gt(this.shru(1)))
return $0;
s = Fu;
} else {
if (this.eq(Vr)) {
if (e.eq(Zp) || e.eq(rw))
return Vr;
if (e.eq(Vr))
return Zp;
var a = this.shr(1);
return o = a.div(e).shl(1), o.eq(To) ? e.isNegative() ? Zp : rw : (n = this.sub(e.mul(o)), s = o.add(n.div(e)), s);
} else if (e.eq(Vr))
return this.unsigned ? Fu : To;
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 = To;
}
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 : Ym(2, i - 48), u = No(o), c = u.mul(e); c.isNegative() || c.gt(n); )
o -= p, u = No(o, this.unsigned), c = u.mul(e);
u.isZero() && (u = Zp), s = s.add(u), n = n.sub(c);
}
return s;
};
de.div = de.divide;
de.modulo = function(e) {
if (Wr(e) || (e = As(e)), ko) {
var t8 = (this.unsigned ? ko.rem_u : ko.rem_s)(this.low, this.high, e.low, e.high);
return Nt(t8, ko.get_high(), this.unsigned);
}
return this.sub(this.div(e).mul(e));
};
de.mod = de.modulo;
de.rem = de.modulo;
de.not = function() {
return Nt(~this.low, ~this.high, this.unsigned);
};
de.and = function(e) {
return Wr(e) || (e = As(e)), Nt(this.low & e.low, this.high & e.high, this.unsigned);
};
de.or = function(e) {
return Wr(e) || (e = As(e)), Nt(this.low | e.low, this.high | e.high, this.unsigned);
};
de.xor = function(e) {
return Wr(e) || (e = As(e)), Nt(this.low ^ e.low, this.high ^ e.high, this.unsigned);
};
de.shiftLeft = function(e) {
return Wr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? Nt(this.low << e, this.high << e | this.low >>> 32 - e, this.unsigned) : Nt(0, this.low << e - 32, this.unsigned);
};
de.shl = de.shiftLeft;
de.shiftRight = function(e) {
return Wr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? Nt(this.low >>> e | this.high << 32 - e, this.high >> e, this.unsigned) : Nt(this.high >> e - 32, this.high >= 0 ? 0 : -1, this.unsigned);
};
de.shr = de.shiftRight;
de.shiftRightUnsigned = function(e) {
if (Wr(e) && (e = e.toInt()), e &= 63, e === 0)
return this;
var t8 = this.high;
if (e < 32) {
var o = this.low;
return Nt(o >>> e | t8 << 32 - e, t8 >>> e, this.unsigned);
} else
return e === 32 ? Nt(t8, 0, this.unsigned) : Nt(t8 >>> e - 32, 0, this.unsigned);
};
de.shru = de.shiftRightUnsigned;
de.shr_u = de.shiftRightUnsigned;
de.toSigned = function() {
return this.unsigned ? Nt(this.low, this.high, false) : this;
};
de.toUnsigned = function() {
return this.unsigned ? this : Nt(this.low, this.high, true);
};
de.toBytes = function(e) {
return e ? this.toBytesLE() : this.toBytesBE();
};
de.toBytesLE = function() {
var e = this.high, t8 = this.low;
return [t8 & 255, t8 >>> 8 & 255, t8 >>> 16 & 255, t8 >>> 24, e & 255, e >>> 8 & 255, e >>> 16 & 255, e >>> 24];
};
de.toBytesBE = function() {
var e = this.high, t8 = this.low;
return [e >>> 24, e >>> 16 & 255, e >>> 8 & 255, e & 255, t8 >>> 24, t8 >>> 16 & 255, t8 >>> 8 & 255, t8 & 255];
};
kt.fromBytes = function(e, t8, o) {
return o ? kt.fromBytesLE(e, t8) : kt.fromBytesBE(e, t8);
};
kt.fromBytesLE = function(e, t8) {
return new kt(e[0] | e[1] << 8 | e[2] << 16 | e[3] << 24, e[4] | e[5] << 8 | e[6] << 16 | e[7] << 24, t8);
};
kt.fromBytesBE = function(e, t8) {
return new kt(e[4] << 24 | e[5] << 16 | e[6] << 8 | e[7], e[0] << 24 | e[1] << 16 | e[2] << 8 | e[3], t8);
};
});
var hk = qt(() => {
});
var gk = qt(() => {
});
var G2 = qt((U2, Fw) => {
(function(r, e, t8) {
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 : t8 && t8.amd ? t8(function() {
return s;
}) : this.alea = s;
})(U2, typeof Fw == "object" && Fw, typeof define == "function" && define);
});
var K2 = qt((H2, Pw) => {
(function(r, e, t8) {
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 : t8 && t8.amd ? t8(function() {
return s;
}) : this.xor128 = s;
})(H2, typeof Pw == "object" && Pw, typeof define == "function" && define);
});
var j2 = qt((q2, Ow) => {
(function(r, e, t8) {
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 : t8 && t8.amd ? t8(function() {
return s;
}) : this.xorwow = s;
})(q2, typeof Ow == "object" && Ow, typeof define == "function" && define);
});
var Y2 = qt((X2, Mw) => {
(function(r, e, t8) {
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 : t8 && t8.amd ? t8(function() {
return s;
}) : this.xorshift7 = s;
})(X2, typeof Mw == "object" && Mw, typeof define == "function" && define);
});
var Z2 = qt((Q2, Lw) => {
(function(r, e, t8) {
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 : t8 && t8.amd ? t8(function() {
return s;
}) : this.xor4096 = s;
})(Q2, typeof Lw == "object" && Lw, typeof define == "function" && define);
});
var e1 = qt((J2, Bw) => {
(function(r, e, t8) {
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 : t8 && t8.amd ? t8(function() {
return s;
}) : this.tychei = s;
})(J2, typeof Bw == "object" && Bw, typeof define == "function" && define);
});
var t1 = qt(() => {
});
var o1 = qt((r1, Od) => {
(function(r, e, t8) {
var o = 256, n = 6, s = 52, a = "random", i = t8.pow(o, n), p = t8.pow(2, s), u = p * 2, c = o - 1, l;
function m(C, S, k) {
var _ = [];
S = S == true ? { entropy: true } : S || {};
var E = g(h(S.entropy ? [C, b(e)] : C == null ? x() : C, 3), _), R = new d(_), D = function() {
for (var P = R.g(n), O = i, M = 0; P < p; )
P = (P + M) * o, O *= o, M = R.g(1);
for (; P >= u; )
P /= 2, O /= 2, M >>>= 1;
return (P + 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(P, O, M, L) {
return L && (L.S && f(L, R), P.state = function() {
return f(R, {});
}), M ? (t8[a] = P, O) : P;
})(D, E, "global" in S ? S.global : this == t8, S.state);
}
function d(C) {
var S, k = C.length, _ = this, E = 0, R = _.i = _.j = 0, D = _.S = [];
for (k || (C = [k++]); E < o; )
D[E] = E++;
for (E = 0; E < o; E++)
D[E] = D[R = c & R + C[E % k] + (S = D[E])], D[R] = S;
(_.g = function(P) {
for (var O, M = 0, L = _.i, B = _.j, z = _.S; P--; )
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(C, S) {
return S.i = C.i, S.j = C.j, S.S = C.S.slice(), S;
}
function h(C, S) {
var k = [], _ = typeof C, E;
if (S && _ == "object")
for (E in C)
try {
k.push(h(C[E], S - 1));
} catch (R) {
}
return k.length ? k : _ == "string" ? C : C + "\0";
}
function g(C, S) {
for (var k = C + "", _, E = 0; E < k.length; )
S[c & E] = c & (_ ^= S[c & E] * 19) + k.charCodeAt(E++);
return b(S);
}
function x() {
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F = IB();
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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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(x || b) && (b ? k = self.location.href : typeof document != "undefined" && document.currentScript && (k = document.currentScript.src), typeof r != "undefined" && r && (k = r), k.indexOf("blob:") !== 0 ? k = k.substr(0, k.replace(/[?#].*/, "").lastIndexOf("/") + 1) : k = "", C || (E = (F) => {
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V(Ee.response);
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C && (B = (F) => M.writeSync(1, F + `
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F || Nu(V);
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Le += String.fromCharCode((ge & 31) << 6 | Ne);
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var Ft = F.charCodeAt(++ge);
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break;
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if (ue + 3 >= Le)
break;
V[ue++ >>> 0] = 240 | Ne >> 18, V[ue++ >>> 0] = 128 | Ne >> 12 & 63, V[ue++ >>> 0] = 128 | Ne >> 6 & 63, V[ue++ >>> 0] = 128 | Ne & 63;
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function Lo() {
return ee;
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function Hs() {
if (u.preRun)
for (typeof u.preRun == "function" && (u.preRun = [u.preRun]); u.preRun.length; )
tl(u.preRun.shift());
nl(to);
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function Yt() {
Ba = true, !S && nl(ro);
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function za() {
if (!S) {
if (u.postRun)
for (typeof u.postRun == "function" && (u.postRun = [u.postRun]); u.postRun.length; )
o0(u.postRun.shift());
nl(fr);
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function tl(F) {
to.unshift(F);
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function rl(F) {
ro.unshift(F);
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function o0(F) {
fr.unshift(F);
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function dy(F) {
vi++, u.monitorRunDependencies && u.monitorRunDependencies(vi);
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function ym(F) {
if (vi--, u.monitorRunDependencies && u.monitorRunDependencies(vi), vi == 0 && (Op !== null && (clearInterval(Op), Op = null), Va)) {
var V = Va;
Va = null, V();
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function Nu(F) {
u.onAbort && u.onAbort(F), F = "Aborted(" + F + ")", j(F), le = true, be = 1, F += ". Build with -sASSERTIONS for more info.";
var V = new WebAssembly.RuntimeError(F);
throw l(V), V;
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var fy = "data:application/octet-stream;base64,";
function bm(F) {
return F.startsWith(fy);
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function Mp(F) {
return F.startsWith("file://");
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var hr;
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function Cm(F) {
try {
if (F == hr && ne)
return new Uint8Array(ne);
if (D)
return D(F);
throw "both async and sync fetching of the wasm failed";
} catch (V) {
Nu(V);
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}
function hy() {
if (!ne && (x || b)) {
if (typeof fetch == "function" && !Mp(hr))
return fetch(hr, { credentials: "same-origin" }).then(function(F) {
if (!F.ok)
throw "failed to load wasm binary file at '" + hr + "'";
return F.arrayBuffer();
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return Cm(hr);
});
if (R)
return new Promise(function(F, V) {
R(hr, function(ue) {
F(new Uint8Array(ue));
}, V);
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}
return Promise.resolve().then(function() {
return Cm(hr);
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function gy() {
var F = { env: Am, wasi_snapshot_preview1: Am };
function V(ge, Ne) {
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Me.unusedWorkers.forEach(function(Ua) {
Me.loadWasmModuleToWorker(Ua, function() {
--no || ym("wasm-instantiate");
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});
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}
S || dy("wasm-instantiate");
function ue(ge) {
V(ge.instance, ge.module);
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function Ee(ge) {
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return Ne;
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j("failed to asynchronously prepare wasm: " + Ne), Nu(Ne);
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function Be() {
return !ne && typeof WebAssembly.instantiateStreaming == "function" && !bm(hr) && !Mp(hr) && !C && typeof fetch == "function" ? fetch(hr, { credentials: "same-origin" }).then(function(ge) {
var Ne = WebAssembly.instantiateStreaming(ge, F);
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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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if (u.instantiateWasm)
try {
var Le = u.instantiateWasm(F, V);
return Le;
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j("Module.instantiateWasm callback failed with error: " + ge), l(ge);
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return Be().catch(l), {};
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var n0, s0, wm = {};
function Tu(F) {
this.name = "ExitStatus", this.message = "Program terminated with exit(" + F + ")", this.status = F;
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var V = Me.pthreads[F];
delete Me.pthreads[F], V.terminate(), VC(F), Me.runningWorkers.splice(Me.runningWorkers.indexOf(V), 1), V.pthread_ptr = 0;
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function yy(F) {
var V = Me.pthreads[F];
V.postMessage({ cmd: "cancel" });
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function ol(F) {
var V = Me.pthreads[F];
_e(V), Me.returnWorkerToPool(V);
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function by(F) {
var V = Me.getNewWorker();
if (!V)
return 6;
Me.runningWorkers.push(V), Me.pthreads[F.pthread_ptr] = V, V.pthread_ptr = F.pthread_ptr;
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return V.runPthread = () => {
C && V.ref(), V.postMessage(ue, F.transferList), delete V.runPthread;
}, V.loaded && V.runPthread(), 0;
}
var Sm = { varargs: void 0, get: function() {
Sm.varargs += 4;
var F = s()[Sm.varargs - 4 >>> 2];
return F;
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var V = Pe(F);
return V;
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function Im(F) {
if (S)
return ki(1, 1, F);
be = F, Lo() || (Me.terminateAllThreads(), u.onExit && u.onExit(F), le = true), g(F, new Tu(F));
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function Cy(F, V) {
if (be = F, !V && S)
throw km(F), "unwind";
Im(F);
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var vm = Cy;
function wy(F) {
if (F instanceof Tu || F == "unwind")
return be;
g(1, F);
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var Me = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], pthreads: {}, init: function() {
S ? Me.initWorker() : Me.initMainThread();
}, initMainThread: function() {
for (var F = 8; F--; )
Me.allocateUnusedWorker();
}, initWorker: function() {
ee = false;
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be = F;
}, terminateAllThreads: function() {
for (var F of Object.values(Me.pthreads))
Me.returnWorkerToPool(F);
for (var F of Me.unusedWorkers)
F.terminate();
Me.unusedWorkers = [];
}, returnWorkerToPool: function(F) {
var V = F.pthread_ptr;
delete Me.pthreads[V], Me.unusedWorkers.push(F), Me.runningWorkers.splice(Me.runningWorkers.indexOf(F), 1), F.pthread_ptr = 0, C && F.unref(), VC(V);
}, receiveObjectTransfer: function(F) {
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Me.tlsInitFunctions.forEach((F) => F());
}, loadWasmModuleToWorker: function(F, V) {
F.onmessage = (Le) => {
var ge = Le.data, Ne = ge.cmd;
if (F.pthread_ptr && (Me.currentProxiedOperationCallerThread = F.pthread_ptr), ge.targetThread && ge.targetThread != Bm()) {
var Ft = Me.pthreads[ge.targetThread];
Ft ? Ft.postMessage(ge, ge.transferList) : j('Internal error! Worker sent a message "' + Ne + '" to target pthread ' + ge.targetThread + ", but that thread no longer exists!"), Me.currentProxiedOperationCallerThread = void 0;
return;
}
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}, F.onerror = (Le) => {
var ge = "worker sent an error!";
throw j(ge + " " + Le.filename + ":" + Le.lineno + ": " + Le.message), Le;
}, C && (F.on("message", function(Le) {
F.onmessage({ data: Le });
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F.onerror(Le);
}), F.on("detachedExit", function() {
}));
var ue = [], Ee = ["onExit", "onAbort", "print", "printErr"];
for (var Be of Ee)
u.hasOwnProperty(Be) && ue.push(Be);
F.postMessage({ cmd: "load", handlers: ue, urlOrBlob: u.mainScriptUrlOrBlob || r, wasmMemory: oe, wasmModule: ie });
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var F, V = _("tfjs-backend-wasm-threaded-simd.worker.js");
F = new Worker(V), Me.unusedWorkers.push(F);
}, getNewWorker: function() {
return Me.unusedWorkers.length == 0 && (Me.allocateUnusedWorker(), Me.loadWasmModuleToWorker(Me.unusedWorkers[0])), Me.unusedWorkers.pop();
} };
u.PThread = Me;
function nl(F) {
for (; F.length > 0; )
F.shift()(u);
}
function Sy() {
var F = Bm(), V = s()[F + 52 >>> 2], ue = s()[F + 56 >>> 2], Ee = V - ue;
l0(V, Ee), zm(V);
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u.establishStackSpace = Sy;
function km(F) {
if (S)
return ki(2, 0, F);
try {
vm(F);
} catch (V) {
wy(V);
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}
var Lp = [];
function Iy(F) {
var V = Lp[F];
return V || (F >= Lp.length && (Lp.length = F + 1), Lp[F] = V = sr.get(F)), V;
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function vy(F, V) {
var ue = Iy(F)(V);
Lo() ? Me.setExitStatus(ue) : c0(ue);
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u.invokeEntryPoint = vy;
function ky(F) {
Me.tlsInitFunctions.push(F);
}
function Ny(F) {
i0(F, !b, 1, !x), Me.threadInitTLS();
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function Ty(F) {
S ? postMessage({ cmd: "cleanupThread", thread: F }) : ol(F);
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function Nm(F, V, ue, Ee) {
return S ? ki(3, 1, F, V, ue, Ee) : Tm(F, V, ue, Ee);
}
function Tm(F, V, ue, Ee) {
if (typeof SharedArrayBuffer == "undefined")
return j("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6;
var Be = [], Le = 0;
if (S && (Be.length === 0 || Le))
return Nm(F, V, ue, Ee);
if (Le)
return Le;
var ge = { startRoutine: ue, pthread_ptr: F, arg: Ee, transferList: Be };
return S ? (ge.cmd = "spawnThread", postMessage(ge, Be), 0) : by(ge);
}
function _y() {
return 65536;
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var $y = true;
function Ey() {
return $y;
}
function sl(F) {
Atomics.store(s(), F >> 2, 1), Bm() && p0(F), Atomics.compareExchange(s(), F >> 2, 1, 0);
}
u.executeNotifiedProxyingQueue = sl;
function Ry(F, V, ue, Ee) {
if (F == V)
setTimeout(() => sl(Ee));
else if (S)
postMessage({ targetThread: F, cmd: "processProxyingQueue", queue: Ee });
else {
var Be = Me.pthreads[F];
if (!Be)
return;
Be.postMessage({ cmd: "processProxyingQueue", queue: Ee });
}
return 1;
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function Dy(F, V, ue) {
return -1;
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function Ay() {
Nu("");
}
function _u(F) {
_u.shown || (_u.shown = {}), _u.shown[F] || (_u.shown[F] = 1, C && (F = "warning: " + F), j(F));
}
function Fy() {
C || b || _u("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
}
function Py() {
return Date.now();
}
function _m() {
return 4294901760;
}
function Oy() {
return _m();
}
var al;
C ? al = () => {
var F = process.hrtime();
return F[0] * 1e3 + F[1] / 1e6;
} : al = () => performance.timeOrigin + performance.now();
function My(F, V, ue) {
o().copyWithin(F >>> 0, V >>> 0, V + ue >>> 0);
}
function Ly() {
return C ? kB().cpus().length : navigator.hardwareConcurrency;
}
function By(F) {
var V = WC(), ue = F();
return zm(V), ue;
}
function ki(F, V) {
var ue = arguments.length - 2, Ee = arguments;
return By(() => {
for (var Be = ue, Le = Vm(Be * 8), ge = Le >> 3, Ne = 0; Ne < ue; Ne++) {
var Ft = Ee[2 + Ne];
p()[ge + Ne >>> 0] = Ft;
}
return u0(F, Be, Le, V);
});
}
var il = [];
function zy(F, V, ue) {
il.length = V;
for (var Ee = ue >> 3, Be = 0; Be < V; Be++)
il[Be] = p()[Ee + Be >>> 0];
var Le = F < 0, ge = Le ? wm[-F - 1] : Xy[F];
return ge.apply(null, il);
}
function Vy(F) {
try {
return oe.grow(F - Ue.byteLength + 65535 >>> 16), Tt(oe.buffer), 1;
} catch (V) {
}
}
function Wy(F) {
var V = o().length;
if (F = F >>> 0, F <= V)
return false;
var ue = _m();
if (F > ue)
return false;
let Ee = (Ft, no) => Ft + (no - Ft % no) % no;
for (var Be = 1; Be <= 4; Be *= 2) {
var Le = V * (1 + 0.2 / Be);
Le = Math.min(Le, F + 100663296);
var ge = Math.min(ue, Ee(Math.max(F, Le), 65536)), Ne = Vy(ge);
if (Ne)
return true;
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return false;
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function Uy() {
throw "unwind";
}
function $m(F) {
return S ? ki(4, 1, F) : 52;
}
function Em(F, V, ue, Ee, Be) {
return S ? ki(5, 1, F, V, ue, Ee, Be) : 70;
}
var Gy = [null, [], []];
function Hy(F, V) {
var ue = Gy[F];
V === 0 || V === 10 ? ((F === 1 ? U : j)(Fe(ue, 0)), ue.length = 0) : ue.push(V);
}
function Rm(F, V, ue, Ee) {
if (S)
return ki(6, 1, F, V, ue, Ee);
for (var Be = 0, Le = 0; Le < ue; Le++) {
var ge = a()[V >>> 2], Ne = a()[V + 4 >>> 2];
V += 8;
for (var Ft = 0; Ft < Ne; Ft++)
Hy(F, o()[ge + Ft >>> 0]);
Be += Ne;
}
return a()[Ee >>> 2] = Be, 0;
}
function Dm(F) {
var V = u["_" + F];
return V;
}
function Ky(F, V) {
t8().set(F, V >>> 0);
}
function qy(F, V, ue, Ee, Be) {
var Le = { string: (Br) => {
var Wp = 0;
if (Br != null && Br !== 0) {
var f0 = (Br.length << 2) + 1;
Wp = Vm(f0), lt(Br, Wp, f0);
}
return Wp;
}, array: (Br) => {
var Wp = Vm(Br.length);
return Ky(Br, Wp), Wp;
} };
function ge(Br) {
return V === "string" ? Pe(Br) : V === "boolean" ? !!Br : Br;
}
var Ne = Dm(F), Ft = [], no = 0;
if (Ee)
for (var Ua = 0; Ua < Ee.length; Ua++) {
var d0 = Le[ue[Ua]];
d0 ? (no === 0 && (no = WC()), Ft[Ua] = d0(Ee[Ua])) : Ft[Ua] = Ee[Ua];
}
var UC = Ne.apply(null, Ft);
function pG(Br) {
return no !== 0 && zm(no), ge(Br);
}
return UC = pG(UC), UC;
}
function jy(F, V, ue, Ee) {
ue = ue || [];
var Be = ue.every((ge) => ge === "number" || ge === "boolean"), Le = V !== "string";
return Le && Be && !Ee ? Dm(F) : function() {
return qy(F, V, ue, arguments, Ee);
};
}
Me.init();
var Xy = [null, Im, km, Nm, $m, Em, Rm], Am = { __emscripten_init_main_thread_js: Ny, __emscripten_thread_cleanup: Ty, __pthread_create_js: Tm, _emscripten_default_pthread_stack_size: _y, _emscripten_get_now_is_monotonic: Ey, _emscripten_notify_task_queue: Ry, _emscripten_set_offscreencanvas_size: Dy, abort: Ay, emscripten_check_blocking_allowed: Fy, emscripten_date_now: Py, emscripten_get_heap_max: Oy, emscripten_get_now: al, emscripten_memcpy_big: My, emscripten_num_logical_cores: Ly, emscripten_receive_on_main_thread_js: zy, emscripten_resize_heap: Wy, emscripten_unwind_to_js_event_loop: Uy, exit: vm, fd_close: $m, fd_seek: Em, fd_write: Rm, memory: oe || u.wasmMemory }, a0 = gy(), Yy = u.___wasm_call_ctors = function() {
return (Yy = u.___wasm_call_ctors = u.asm.__wasm_call_ctors).apply(null, arguments);
}, Qy = u._init = function() {
return (Qy = u._init = u.asm.init).apply(null, arguments);
}, Zy = u._init_with_threads_count = function() {
return (Zy = u._init_with_threads_count = u.asm.init_with_threads_count).apply(null, arguments);
}, Jy = u._get_threads_count = function() {
return (Jy = u._get_threads_count = u.asm.get_threads_count).apply(null, arguments);
}, eb = u._register_tensor = function() {
return (eb = u._register_tensor = u.asm.register_tensor).apply(null, arguments);
}, tb = u._dispose_data = function() {
return (tb = u._dispose_data = u.asm.dispose_data).apply(null, arguments);
}, rb = u._dispose = function() {
return (rb = u._dispose = u.asm.dispose).apply(null, arguments);
}, ob = u._Abs = function() {
return (ob = u._Abs = u.asm.Abs).apply(null, arguments);
}, nb = u._Acos = function() {
return (nb = u._Acos = u.asm.Acos).apply(null, arguments);
}, sb = u._Acosh = function() {
return (sb = u._Acosh = u.asm.Acosh).apply(null, arguments);
}, ab = u._Add = function() {
return (ab = u._Add = u.asm.Add).apply(null, arguments);
}, ib = u._AddN = function() {
return (ib = u._AddN = u.asm.AddN).apply(null, arguments);
}, ub = u._All = function() {
return (ub = u._All = u.asm.All).apply(null, arguments);
}, pb = u._Any = function() {
return (pb = u._Any = u.asm.Any).apply(null, arguments);
}, cb = u._ArgMax = function() {
return (cb = u._ArgMax = u.asm.ArgMax).apply(null, arguments);
}, lb = u._ArgMin = function() {
return (lb = u._ArgMin = u.asm.ArgMin).apply(null, arguments);
}, mb = u._Asin = function() {
return (mb = u._Asin = u.asm.Asin).apply(null, arguments);
}, db = u._Asinh = function() {
return (db = u._Asinh = u.asm.Asinh).apply(null, arguments);
}, fb = u._Atan = function() {
return (fb = u._Atan = u.asm.Atan).apply(null, arguments);
}, hb = u._Atan2 = function() {
return (hb = u._Atan2 = u.asm.Atan2).apply(null, arguments);
}, gb = u._Atanh = function() {
return (gb = u._Atanh = u.asm.Atanh).apply(null, arguments);
}, xb = u._AvgPool = function() {
return (xb = u._AvgPool = u.asm.AvgPool).apply(null, arguments);
}, yb = u._AvgPool3D = function() {
return (yb = u._AvgPool3D = u.asm.AvgPool3D).apply(null, arguments);
}, bb = u._AvgPool3DGrad = function() {
return (bb = u._AvgPool3DGrad = u.asm.AvgPool3DGrad).apply(null, arguments);
}, Cb = u._AvgPoolGrad = function() {
return (Cb = u._AvgPoolGrad = u.asm.AvgPoolGrad).apply(null, arguments);
}, wb = u._BatchMatMul = function() {
return (wb = u._BatchMatMul = u.asm.BatchMatMul).apply(null, arguments);
}, Sb = u._Bincount = function() {
return (Sb = u._Bincount = u.asm.Bincount).apply(null, arguments);
}, Ib = u._BitwiseAnd = function() {
return (Ib = u._BitwiseAnd = u.asm.BitwiseAnd).apply(null, arguments);
}, vb = u._Ceil = function() {
return (vb = u._Ceil = u.asm.Ceil).apply(null, arguments);
}, kb = u._ClipByValue = function() {
return (kb = u._ClipByValue = u.asm.ClipByValue).apply(null, arguments);
}, Nb = u._Conv2D = function() {
return (Nb = u._Conv2D = u.asm.Conv2D).apply(null, arguments);
}, Tb = u._Conv2DBackpropInput = function() {
return (Tb = u._Conv2DBackpropInput = u.asm.Conv2DBackpropInput).apply(null, arguments);
}, _b = u._Conv3D = function() {
return (_b = u._Conv3D = u.asm.Conv3D).apply(null, arguments);
}, $b = u._Conv3DBackpropFilterV2 = function() {
return ($b = u._Conv3DBackpropFilterV2 = u.asm.Conv3DBackpropFilterV2).apply(null, arguments);
}, Eb = u._Conv3DBackpropInputV2 = function() {
return (Eb = u._Conv3DBackpropInputV2 = u.asm.Conv3DBackpropInputV2).apply(null, arguments);
}, Rb = u._Cos = function() {
return (Rb = u._Cos = u.asm.Cos).apply(null, arguments);
}, Db = u._Cosh = function() {
return (Db = u._Cosh = u.asm.Cosh).apply(null, arguments);
}, Ab = u._CropAndResize = function() {
return (Ab = u._CropAndResize = u.asm.CropAndResize).apply(null, arguments);
}, Fb = u._Cumprod = function() {
return (Fb = u._Cumprod = u.asm.Cumprod).apply(null, arguments);
}, Pb = u._Cumsum = function() {
return (Pb = u._Cumsum = u.asm.Cumsum).apply(null, arguments);
}, Ob = u._DenseBincount = function() {
return (Ob = u._DenseBincount = u.asm.DenseBincount).apply(null, arguments);
}, Mb = u._DepthToSpace = function() {
return (Mb = u._DepthToSpace = u.asm.DepthToSpace).apply(null, arguments);
}, Lb = u._DepthwiseConv2dNative = function() {
return (Lb = u._DepthwiseConv2dNative = u.asm.DepthwiseConv2dNative).apply(null, arguments);
}, Bb = u._Diag = function() {
return (Bb = u._Diag = u.asm.Diag).apply(null, arguments);
}, zb = u._Dilation2D = function() {
return (zb = u._Dilation2D = u.asm.Dilation2D).apply(null, arguments);
}, Vb = u._Dilation2DBackpropFilter = function() {
return (Vb = u._Dilation2DBackpropFilter = u.asm.Dilation2DBackpropFilter).apply(null, arguments);
}, Wb = u._Dilation2DBackpropInput = function() {
return (Wb = u._Dilation2DBackpropInput = u.asm.Dilation2DBackpropInput).apply(null, arguments);
}, Ub = u._Elu = function() {
return (Ub = u._Elu = u.asm.Elu).apply(null, arguments);
}, Gb = u._EluGrad = function() {
return (Gb = u._EluGrad = u.asm.EluGrad).apply(null, arguments);
}, Hb = u._Equal = function() {
return (Hb = u._Equal = u.asm.Equal).apply(null, arguments);
}, Kb = u._Erf = function() {
return (Kb = u._Erf = u.asm.Erf).apply(null, arguments);
}, qb = u._Exp = function() {
return (qb = u._Exp = u.asm.Exp).apply(null, arguments);
}, jb = u._Expm1 = function() {
return (jb = u._Expm1 = u.asm.Expm1).apply(null, arguments);
}, Xb = u._FlipLeftRight = function() {
return (Xb = u._FlipLeftRight = u.asm.FlipLeftRight).apply(null, arguments);
}, Yb = u._Floor = function() {
return (Yb = u._Floor = u.asm.Floor).apply(null, arguments);
}, Qb = u._FloorDiv = function() {
return (Qb = u._FloorDiv = u.asm.FloorDiv).apply(null, arguments);
}, Zb = u._FusedBatchNorm = function() {
return (Zb = u._FusedBatchNorm = u.asm.FusedBatchNorm).apply(null, arguments);
}, Jb = u._FusedConv2D = function() {
return (Jb = u._FusedConv2D = u.asm.FusedConv2D).apply(null, arguments);
}, eC = u._FusedDepthwiseConv2D = function() {
return (eC = u._FusedDepthwiseConv2D = u.asm.FusedDepthwiseConv2D).apply(null, arguments);
}, tC = u._Gather = function() {
return (tC = u._Gather = u.asm.Gather).apply(null, arguments);
}, rC = u._GatherNd = function() {
return (rC = u._GatherNd = u.asm.GatherNd).apply(null, arguments);
}, oC = u._Greater = function() {
return (oC = u._Greater = u.asm.Greater).apply(null, arguments);
}, nC = u._GreaterEqual = function() {
return (nC = u._GreaterEqual = u.asm.GreaterEqual).apply(null, arguments);
}, sC = u._IsFinite = function() {
return (sC = u._IsFinite = u.asm.IsFinite).apply(null, arguments);
}, aC = u._IsInf = function() {
return (aC = u._IsInf = u.asm.IsInf).apply(null, arguments);
}, iC = u._IsNan = function() {
return (iC = u._IsNan = u.asm.IsNan).apply(null, arguments);
}, uC = u._LRN = function() {
return (uC = u._LRN = u.asm.LRN).apply(null, arguments);
}, pC = u._LRNGrad = function() {
return (pC = u._LRNGrad = u.asm.LRNGrad).apply(null, arguments);
}, cC = u._LeakyRelu = function() {
return (cC = u._LeakyRelu = u.asm.LeakyRelu).apply(null, arguments);
}, lC = u._Less = function() {
return (lC = u._Less = u.asm.Less).apply(null, arguments);
}, mC = u._LessEqual = function() {
return (mC = u._LessEqual = u.asm.LessEqual).apply(null, arguments);
}, dC = u._LinSpace = function() {
return (dC = u._LinSpace = u.asm.LinSpace).apply(null, arguments);
}, fC = u._Log = function() {
return (fC = u._Log = u.asm.Log).apply(null, arguments);
}, hC = u._Log1p = function() {
return (hC = u._Log1p = u.asm.Log1p).apply(null, arguments);
}, gC = u._LogicalAnd = function() {
return (gC = u._LogicalAnd = u.asm.LogicalAnd).apply(null, arguments);
}, xC = u._LogicalNot = function() {
return (xC = u._LogicalNot = u.asm.LogicalNot).apply(null, arguments);
}, yC = u._LogicalOr = function() {
return (yC = u._LogicalOr = u.asm.LogicalOr).apply(null, arguments);
}, bC = u._LogicalXor = function() {
return (bC = u._LogicalXor = u.asm.LogicalXor).apply(null, arguments);
}, CC = u._Max = function() {
return (CC = u._Max = u.asm.Max).apply(null, arguments);
}, wC = u._MaxPool = function() {
return (wC = u._MaxPool = u.asm.MaxPool).apply(null, arguments);
}, SC = u._MaxPool3D = function() {
return (SC = u._MaxPool3D = u.asm.MaxPool3D).apply(null, arguments);
}, IC = u._MaxPool3DGrad = function() {
return (IC = u._MaxPool3DGrad = u.asm.MaxPool3DGrad).apply(null, arguments);
}, vC = u._MaxPoolGrad = function() {
return (vC = u._MaxPoolGrad = u.asm.MaxPoolGrad).apply(null, arguments);
}, kC = u._MaxPoolWithArgmax = function() {
return (kC = u._MaxPoolWithArgmax = u.asm.MaxPoolWithArgmax).apply(null, arguments);
}, NC = u._Maximum = function() {
return (NC = u._Maximum = u.asm.Maximum).apply(null, arguments);
}, TC = u._Mean = function() {
return (TC = u._Mean = u.asm.Mean).apply(null, arguments);
}, _C = u._Min = function() {
return (_C = u._Min = u.asm.Min).apply(null, arguments);
}, $C = u._Minimum = function() {
return ($C = u._Minimum = u.asm.Minimum).apply(null, arguments);
}, EC = u._MirrorPad = function() {
return (EC = u._MirrorPad = u.asm.MirrorPad).apply(null, arguments);
}, RC = u._Mod = function() {
return (RC = u._Mod = u.asm.Mod).apply(null, arguments);
}, DC = u._Multinomial = function() {
return (DC = u._Multinomial = u.asm.Multinomial).apply(null, arguments);
}, AC = u._Multiply = function() {
return (AC = u._Multiply = u.asm.Multiply).apply(null, arguments);
}, FC = u._Neg = function() {
return (FC = u._Neg = u.asm.Neg).apply(null, arguments);
}, PC = u._NonMaxSuppressionV3 = function() {
return (PC = u._NonMaxSuppressionV3 = u.asm.NonMaxSuppressionV3).apply(null, arguments);
}, OC = u._NonMaxSuppressionV4 = function() {
return (OC = u._NonMaxSuppressionV4 = u.asm.NonMaxSuppressionV4).apply(null, arguments);
}, Fm = u._NonMaxSuppressionV5 = function() {
return (Fm = u._NonMaxSuppressionV5 = u.asm.NonMaxSuppressionV5).apply(null, arguments);
}, Pm = u._NotEqual = function() {
return (Pm = u._NotEqual = u.asm.NotEqual).apply(null, arguments);
}, ul = u._OneHot = function() {
return (ul = u._OneHot = u.asm.OneHot).apply(null, arguments);
}, MC = u._PadV2 = function() {
return (MC = u._PadV2 = u.asm.PadV2).apply(null, arguments);
}, LC = u._Pow = function() {
return (LC = u._Pow = u.asm.Pow).apply(null, arguments);
}, Bp = u._Prelu = function() {
return (Bp = u._Prelu = u.asm.Prelu).apply(null, arguments);
}, Om = u._Prod = function() {
return (Om = u._Prod = u.asm.Prod).apply(null, arguments);
}, zp = u._RealDiv = function() {
return (zp = u._RealDiv = u.asm.RealDiv).apply(null, arguments);
}, Vp = u._Reciprocal = function() {
return (Vp = u._Reciprocal = u.asm.Reciprocal).apply(null, arguments);
}, BC = u._Relu = function() {
return (BC = u._Relu = u.asm.Relu).apply(null, arguments);
}, G = u._Relu6 = function() {
return (G = u._Relu6 = u.asm.Relu6).apply(null, arguments);
}, ae = u._ResizeBilinear = function() {
return (ae = u._ResizeBilinear = u.asm.ResizeBilinear).apply(null, arguments);
}, $e = u._ResizeBilinearGrad = function() {
return ($e = u._ResizeBilinearGrad = u.asm.ResizeBilinearGrad).apply(null, arguments);
}, at = u._ResizeNearestNeighbor = function() {
return (at = u._ResizeNearestNeighbor = u.asm.ResizeNearestNeighbor).apply(null, arguments);
}, _t = u._ResizeNearestNeighborGrad = function() {
return (_t = u._ResizeNearestNeighborGrad = u.asm.ResizeNearestNeighborGrad).apply(null, arguments);
}, $t = u._Reverse = function() {
return ($t = u._Reverse = u.asm.Reverse).apply(null, arguments);
}, Qe = u._RotateWithOffset = function() {
return (Qe = u._RotateWithOffset = u.asm.RotateWithOffset).apply(null, arguments);
}, He = u._Round = function() {
return (He = u._Round = u.asm.Round).apply(null, arguments);
}, Gt = u._Rsqrt = function() {
return (Gt = u._Rsqrt = u.asm.Rsqrt).apply(null, arguments);
}, oo = u._ScatterNd = function() {
return (oo = u._ScatterNd = u.asm.ScatterNd).apply(null, arguments);
}, Wa = u._SearchSorted = function() {
return (Wa = u._SearchSorted = u.asm.SearchSorted).apply(null, arguments);
}, Mm = u._SelectV2 = function() {
return (Mm = u._SelectV2 = u.asm.SelectV2).apply(null, arguments);
}, pl = u._Selu = function() {
return (pl = u._Selu = u.asm.Selu).apply(null, arguments);
}, zC = u._Sigmoid = function() {
return (zC = u._Sigmoid = u.asm.Sigmoid).apply(null, arguments);
}, br = u._Sign = function() {
return (br = u._Sign = u.asm.Sign).apply(null, arguments);
}, Ni = u._Sin = function() {
return (Ni = u._Sin = u.asm.Sin).apply(null, arguments);
}, Lm = u._Sinh = function() {
return (Lm = u._Sinh = u.asm.Sinh).apply(null, arguments);
}, RU = u._Softmax = function() {
return (RU = u._Softmax = u.asm.Softmax).apply(null, arguments);
}, DU = u._Softplus = function() {
return (DU = u._Softplus = u.asm.Softplus).apply(null, arguments);
}, AU = u._SparseFillEmptyRows = function() {
return (AU = u._SparseFillEmptyRows = u.asm.SparseFillEmptyRows).apply(null, arguments);
}, FU = u._SparseReshape = function() {
return (FU = u._SparseReshape = u.asm.SparseReshape).apply(null, arguments);
}, PU = u._SparseSegmentReduction = function() {
return (PU = u._SparseSegmentReduction = u.asm.SparseSegmentReduction).apply(null, arguments);
}, OU = u._SparseToDense = function() {
return (OU = u._SparseToDense = u.asm.SparseToDense).apply(null, arguments);
}, MU = u._Sqrt = function() {
return (MU = u._Sqrt = u.asm.Sqrt).apply(null, arguments);
}, LU = u._Square = function() {
return (LU = u._Square = u.asm.Square).apply(null, arguments);
}, BU = u._SquaredDifference = function() {
return (BU = u._SquaredDifference = u.asm.SquaredDifference).apply(null, arguments);
}, zU = u._Step = function() {
return (zU = u._Step = u.asm.Step).apply(null, arguments);
}, VU = u._StridedSlice = function() {
return (VU = u._StridedSlice = u.asm.StridedSlice).apply(null, arguments);
}, WU = u._Sub = function() {
return (WU = u._Sub = u.asm.Sub).apply(null, arguments);
}, UU = u._Sum = function() {
return (UU = u._Sum = u.asm.Sum).apply(null, arguments);
}, GU = u._Tan = function() {
return (GU = u._Tan = u.asm.Tan).apply(null, arguments);
}, HU = u._Tanh = function() {
return (HU = u._Tanh = u.asm.Tanh).apply(null, arguments);
}, KU = u._TensorScatterUpdate = function() {
return (KU = u._TensorScatterUpdate = u.asm.TensorScatterUpdate).apply(null, arguments);
}, qU = u._Tile = function() {
return (qU = u._Tile = u.asm.Tile).apply(null, arguments);
}, jU = u._TopK = function() {
return (jU = u._TopK = u.asm.TopK).apply(null, arguments);
}, XU = u._Transform = function() {
return (XU = u._Transform = u.asm.Transform).apply(null, arguments);
}, YU = u._Transpose = function() {
return (YU = u._Transpose = u.asm.Transpose).apply(null, arguments);
}, QU = u.__FusedMatMul = function() {
return (QU = u.__FusedMatMul = u.asm._FusedMatMul).apply(null, arguments);
}, ZU = u._malloc = function() {
return (ZU = u._malloc = u.asm.malloc).apply(null, arguments);
}, JU = u._free = function() {
return (JU = u._free = u.asm.free).apply(null, arguments);
}, eG = u.__emscripten_tls_init = function() {
return (eG = u.__emscripten_tls_init = u.asm._emscripten_tls_init).apply(null, arguments);
}, Bm = u._pthread_self = function() {
return (Bm = u._pthread_self = u.asm.pthread_self).apply(null, arguments);
}, tG = u.___errno_location = function() {
return (tG = u.___errno_location = u.asm.__errno_location).apply(null, arguments);
}, i0 = u.__emscripten_thread_init = function() {
return (i0 = u.__emscripten_thread_init = u.asm._emscripten_thread_init).apply(null, arguments);
}, rG = u.__emscripten_thread_crashed = function() {
return (rG = u.__emscripten_thread_crashed = u.asm._emscripten_thread_crashed).apply(null, arguments);
}, oG = u._emscripten_main_thread_process_queued_calls = function() {
return (oG = u._emscripten_main_thread_process_queued_calls = u.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
}, nG = u._emscripten_main_browser_thread_id = function() {
return (nG = u._emscripten_main_browser_thread_id = u.asm.emscripten_main_browser_thread_id).apply(null, arguments);
}, u0 = u._emscripten_run_in_main_runtime_thread_js = function() {
return (u0 = u._emscripten_run_in_main_runtime_thread_js = u.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
}, sG = u._emscripten_dispatch_to_thread_ = function() {
return (sG = u._emscripten_dispatch_to_thread_ = u.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
}, p0 = u.__emscripten_proxy_execute_task_queue = function() {
return (p0 = u.__emscripten_proxy_execute_task_queue = u.asm._emscripten_proxy_execute_task_queue).apply(null, arguments);
}, VC = u.__emscripten_thread_free_data = function() {
return (VC = u.__emscripten_thread_free_data = u.asm._emscripten_thread_free_data).apply(null, arguments);
}, c0 = u.__emscripten_thread_exit = function() {
return (c0 = u.__emscripten_thread_exit = u.asm._emscripten_thread_exit).apply(null, arguments);
}, l0 = u._emscripten_stack_set_limits = function() {
return (l0 = u._emscripten_stack_set_limits = u.asm.emscripten_stack_set_limits).apply(null, arguments);
}, WC = u.stackSave = function() {
return (WC = u.stackSave = u.asm.stackSave).apply(null, arguments);
}, zm = u.stackRestore = function() {
return (zm = u.stackRestore = u.asm.stackRestore).apply(null, arguments);
}, Vm = u.stackAlloc = function() {
return (Vm = u.stackAlloc = u.asm.stackAlloc).apply(null, arguments);
}, aG = u.dynCall_iijjiiii = function() {
return (aG = u.dynCall_iijjiiii = u.asm.dynCall_iijjiiii).apply(null, arguments);
}, iG = u.dynCall_jiji = function() {
return (iG = u.dynCall_jiji = u.asm.dynCall_jiji).apply(null, arguments);
};
u.keepRuntimeAlive = Lo, u.wasmMemory = oe, u.cwrap = jy, u.ExitStatus = Tu, u.PThread = Me;
var Wm;
Va = function F() {
Wm || m0(), Wm || (Va = F);
};
function m0(F) {
if (F = F || f, vi > 0)
return;
if (S) {
c(u), Yt(), startWorker(u);
return;
}
if (Hs(), vi > 0)
return;
function V() {
Wm || (Wm = true, u.calledRun = true, !le && (Yt(), c(u), u.onRuntimeInitialized && u.onRuntimeInitialized(), za()));
}
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()();
m0();
var Um;
m && (Um = { uncaughtException: process.listeners("uncaughtException").filter(function(F) {
return !m.uncaughtException.indexOf(F) > -1;
}), unhandledRejection: process.listeners("unhandledRejection").filter(function(F) {
return !m.unhandledRejection.indexOf(F) > -1;
}) });
var Gm;
if (typeof WasmBackendModule != "undefined")
Gm = WasmBackendModule;
else if (typeof e != "undefined")
Gm = e;
else
throw new Error("Could not find wasm module in post.js");
if (Um) {
var uG = Gm._dispose;
Gm._dispose = function() {
uG(), Um.uncaughtException.forEach(function(F) {
process.removeListener("uncaughtException", F);
}), Um.unhandledRejection.forEach(function(F) {
process.removeListener("unhandledRejection", F);
});
};
}
return e.ready;
};
})();
typeof zg == "object" && typeof Fv == "object" ? Fv.exports = Av : typeof define == "function" && define.amd ? define([], function() {
return Av;
}) : typeof zg == "object" && (zg.WasmBackendModuleThreadedSimd = Av);
});
var _B = qt((vFt, TB) => {
TB.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 $B = qt((Vg, Ov) => {
var Pv = (() => {
var r = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0;
return typeof __filename != "undefined" && (r = r || __filename), function(e) {
e = e || {};
var t8 = typeof e != "undefined" ? e : {}, o, n;
t8.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({}, t8), 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 t8.locateFile ? t8.locateFile(G, d) : d + G;
}
var h, g, x, b;
function C(G) {
if (G instanceof Op)
return;
E("exiting due to exception: " + G);
}
if (m) {
var S = Rv(), k = Dv();
l ? d = k.dirname(d) + "/" : d = __dirname + "/", h = (G, ae) => (G = Hs(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 = Hs(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 Op))
throw G;
}), process.on("unhandledRejection", function(G) {
throw G;
}), u = (G, ae) => {
if (it())
throw process.exitCode = G, ae;
C(ae), process.exit(G);
}, t8.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 _ = t8.print || console.log.bind(console), E = t8.printErr || console.warn.bind(console);
Object.assign(t8, a), a = null, t8.arguments && (i = t8.arguments), t8.thisProgram && (p = t8.thisProgram), t8.quit && (u = t8.quit);
var R = 4, D;
t8.wasmBinary && (D = t8.wasmBinary);
var P = t8.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 Qe = G[ae++];
if (!(Qe & 128)) {
$t += String.fromCharCode(Qe);
continue;
}
var He = G[ae++] & 63;
if ((Qe & 224) == 192) {
$t += String.fromCharCode((Qe & 31) << 6 | He);
continue;
}
var Gt = G[ae++] & 63;
if ((Qe & 240) == 224 ? Qe = (Qe & 15) << 12 | He << 6 | Gt : Qe = (Qe & 7) << 18 | He << 12 | Gt << 6 | G[ae++] & 63, Qe < 65536)
$t += String.fromCharCode(Qe);
else {
var oo = Qe - 65536;
$t += String.fromCharCode(55296 | oo >> 10, 56320 | oo & 1023);
}
}
return $t;
}
function j(G, ae) {
return G >>>= 0, G ? U(ne, G, ae) : "";
}
function q(G, ae, $e, at) {
if ($e >>>= 0, !(at > 0))
return 0;
for (var _t = $e, $t = $e + at - 1, Qe = 0; Qe < G.length; ++Qe) {
var He = G.charCodeAt(Qe);
if (He >= 55296 && He <= 57343) {
var Gt = G.charCodeAt(++Qe);
He = 65536 + ((He & 1023) << 10) | Gt & 1023;
}
if (He <= 127) {
if ($e >= $t)
break;
ae[$e++ >>> 0] = He;
} else if (He <= 2047) {
if ($e + 1 >= $t)
break;
ae[$e++ >>> 0] = 192 | He >> 6, ae[$e++ >>> 0] = 128 | He & 63;
} else if (He <= 65535) {
if ($e + 2 >= $t)
break;
ae[$e++ >>> 0] = 224 | He >> 12, ae[$e++ >>> 0] = 128 | He >> 6 & 63, ae[$e++ >>> 0] = 128 | He & 63;
} else {
if ($e + 3 >= $t)
break;
ae[$e++ >>> 0] = 240 | He >> 18, ae[$e++ >>> 0] = 128 | He >> 12 & 63, ae[$e++ >>> 0] = 128 | He >> 6 & 63, ae[$e++ >>> 0] = 128 | He & 63;
}
}
return ae[$e >>> 0] = 0, $e - _t;
}
function Y(G, ae, $e) {
return q(G, ne, ae, $e);
}
var J, re, ne, ee, oe, ie, le, be, _e;
function ve(G) {
J = G, t8.HEAP8 = re = new Int8Array(G), t8.HEAP16 = ee = new Int16Array(G), t8.HEAP32 = ie = new Int32Array(G), t8.HEAPU8 = ne = new Uint8Array(G), t8.HEAPU16 = oe = new Uint16Array(G), t8.HEAPU32 = le = new Uint32Array(G), t8.HEAPF32 = be = new Float32Array(G), t8.HEAPF64 = _e = new Float64Array(G);
}
var Fe = t8.INITIAL_MEMORY || 16777216, Pe, st = [], lt = [], Ue = [], mt = false;
function it() {
return P;
}
function gt() {
if (t8.preRun)
for (typeof t8.preRun == "function" && (t8.preRun = [t8.preRun]); t8.preRun.length; )
Mt(t8.preRun.shift());
Va(st);
}
function xt() {
mt = true, Va(lt);
}
function Lr() {
if (t8.postRun)
for (typeof t8.postRun == "function" && (t8.postRun = [t8.postRun]); t8.postRun.length; )
or(t8.postRun.shift());
Va(Ue);
}
function Mt(G) {
st.unshift(G);
}
function eo(G) {
lt.unshift(G);
}
function or(G) {
Ue.unshift(G);
}
var Tt = 0, nr = null, sr = null;
function to(G) {
Tt++, t8.monitorRunDependencies && t8.monitorRunDependencies(Tt);
}
function ro(G) {
if (Tt--, t8.monitorRunDependencies && t8.monitorRunDependencies(Tt), Tt == 0 && (nr !== null && (clearInterval(nr), nr = null), sr)) {
var ae = sr;
sr = null, ae();
}
}
function fr(G) {
t8.onAbort && t8.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 Ba = "data:application/octet-stream;base64,";
function Lo(G) {
return G.startsWith(Ba);
}
function Hs(G) {
return G.startsWith("file://");
}
var Yt;
Yt = "tfjs-backend-wasm.wasm", Lo(Yt) || (Yt = f(Yt));
function za(G) {
try {
if (G == Yt && 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 tl() {
if (!D && (c || l)) {
if (typeof fetch == "function" && !Hs(Yt))
return fetch(Yt, { credentials: "same-origin" }).then(function(G) {
if (!G.ok)
throw "failed to load wasm binary file at '" + Yt + "'";
return G.arrayBuffer();
}).catch(function() {
return za(Yt);
});
if (g)
return new Promise(function(G, ae) {
g(Yt, function($e) {
G(new Uint8Array($e));
}, ae);
});
}
return Promise.resolve().then(function() {
return za(Yt);
});
}
function rl() {
var G = { env: ol, wasi_snapshot_preview1: ol };
function ae(Qe, He) {
var Gt = Qe.exports;
t8.asm = Gt, O = t8.asm.memory, ve(O.buffer), Pe = t8.asm.__indirect_function_table, eo(t8.asm.__wasm_call_ctors), ro("wasm-instantiate");
}
to("wasm-instantiate");
function $e(Qe) {
ae(Qe.instance);
}
function at(Qe) {
return tl().then(function(He) {
return WebAssembly.instantiate(He, G);
}).then(function(He) {
return He;
}).then(Qe, function(He) {
E("failed to asynchronously prepare wasm: " + He), fr(He);
});
}
function _t() {
return !D && typeof WebAssembly.instantiateStreaming == "function" && !Lo(Yt) && !Hs(Yt) && !m && typeof fetch == "function" ? fetch(Yt, { credentials: "same-origin" }).then(function(Qe) {
var He = WebAssembly.instantiateStreaming(Qe, G);
return He.then($e, function(Gt) {
return E("wasm streaming compile failed: " + Gt), E("falling back to ArrayBuffer instantiation"), at($e);
});
}) : at($e);
}
if (t8.instantiateWasm)
try {
var $t = t8.instantiateWasm(G, ae);
return $t;
} catch (Qe) {
E("Module.instantiateWasm callback failed with error: " + Qe), n(Qe);
}
return _t().catch(n), {};
}
var o0, vi;
function Op(G) {
this.name = "ExitStatus", this.message = "Program terminated with exit(" + G + ")", this.status = G;
}
function Va(G) {
for (; G.length > 0; )
G.shift()(t8);
}
function dy() {
fr("");
}
function ym() {
return 4294901760;
}
function Nu() {
return ym();
}
function fy(G, ae, $e) {
ne.copyWithin(G >>> 0, ae >>> 0, ae + $e >>> 0);
}
function bm(G) {
try {
return O.grow(G - J.byteLength + 65535 >>> 16), ve(O.buffer), 1;
} catch (ae) {
}
}
function Mp(G) {
var ae = ne.length;
G = G >>> 0;
var $e = ym();
if (G > $e)
return false;
let at = (Gt, oo) => Gt + (oo - Gt % oo) % oo;
for (var _t = 1; _t <= 4; _t *= 2) {
var $t = ae * (1 + 0.2 / _t);
$t = Math.min($t, G + 100663296);
var Qe = Math.min($e, at(Math.max(G, $t), 65536)), He = bm(Qe);
if (He)
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 Cm(G) {
return 52;
}
function hy(G, ae, $e, at, _t) {
return 70;
}
var gy = [null, [], []];
function n0(G, ae) {
var $e = gy[G];
ae === 0 || ae === 10 ? ((G === 1 ? _ : E)(U($e, 0)), $e.length = 0) : $e.push(ae);
}
function s0(G, ae, $e, at) {
for (var _t = 0, $t = 0; $t < $e; $t++) {
var Qe = le[ae >>> 2], He = le[ae + 4 >>> 2];
ae += 8;
for (var Gt = 0; Gt < He; Gt++)
n0(G, ne[Qe + Gt >>> 0]);
_t += He;
}
return le[at >>> 2] = _t, 0;
}
function wm(G) {
var ae = t8["_" + G];
return ae;
}
function Tu(G, ae) {
re.set(G, ae >>> 0);
}
function xy(G, ae, $e, at, _t) {
var $t = { string: (br) => {
var Ni = 0;
if (br != null && br !== 0) {
var Lm = (br.length << 2) + 1;
Ni = ul(Lm), Y(br, Ni, Lm);
}
return Ni;
}, array: (br) => {
var Ni = ul(br.length);
return Tu(br, Ni), Ni;
} };
function Qe(br) {
return ae === "string" ? j(br) : ae === "boolean" ? !!br : br;
}
var He = wm(G), Gt = [], oo = 0;
if (at)
for (var Wa = 0; Wa < at.length; Wa++) {
var Mm = $t[$e[Wa]];
Mm ? (oo === 0 && (oo = Fm()), Gt[Wa] = Mm(at[Wa])) : Gt[Wa] = at[Wa];
}
var pl = He.apply(null, Gt);
function zC(br) {
return oo !== 0 && Pm(oo), Qe(br);
}
return pl = zC(pl), pl;
}
function yy(G, ae, $e, at) {
$e = $e || [];
var _t = $e.every((Qe) => Qe === "number" || Qe === "boolean"), $t = ae !== "string";
return $t && _t && !at ? wm(G) : function() {
return xy(G, ae, $e, arguments, at);
};
}
var ol = { abort: dy, emscripten_get_heap_max: Nu, emscripten_memcpy_big: fy, emscripten_resize_heap: Mp, fd_close: Cm, fd_seek: hy, fd_write: s0 }, by = rl(), Sm = t8.___wasm_call_ctors = function() {
return (Sm = t8.___wasm_call_ctors = t8.asm.__wasm_call_ctors).apply(null, arguments);
}, Im = t8._init = function() {
return (Im = t8._init = t8.asm.init).apply(null, arguments);
}, Cy = t8._init_with_threads_count = function() {
return (Cy = t8._init_with_threads_count = t8.asm.init_with_threads_count).apply(null, arguments);
}, vm = t8._get_threads_count = function() {
return (vm = t8._get_threads_count = t8.asm.get_threads_count).apply(null, arguments);
}, wy = t8._register_tensor = function() {
return (wy = t8._register_tensor = t8.asm.register_tensor).apply(null, arguments);
}, Me = t8._dispose_data = function() {
return (Me = t8._dispose_data = t8.asm.dispose_data).apply(null, arguments);
}, nl = t8._dispose = function() {
return (nl = t8._dispose = t8.asm.dispose).apply(null, arguments);
}, Sy = t8._Abs = function() {
return (Sy = t8._Abs = t8.asm.Abs).apply(null, arguments);
}, km = t8._Acos = function() {
return (km = t8._Acos = t8.asm.Acos).apply(null, arguments);
}, Lp = t8._Acosh = function() {
return (Lp = t8._Acosh = t8.asm.Acosh).apply(null, arguments);
}, Iy = t8._Add = function() {
return (Iy = t8._Add = t8.asm.Add).apply(null, arguments);
}, vy = t8._AddN = function() {
return (vy = t8._AddN = t8.asm.AddN).apply(null, arguments);
}, ky = t8._All = function() {
return (ky = t8._All = t8.asm.All).apply(null, arguments);
}, Ny = t8._Any = function() {
return (Ny = t8._Any = t8.asm.Any).apply(null, arguments);
}, Ty = t8._ArgMax = function() {
return (Ty = t8._ArgMax = t8.asm.ArgMax).apply(null, arguments);
}, Nm = t8._ArgMin = function() {
return (Nm = t8._ArgMin = t8.asm.ArgMin).apply(null, arguments);
}, Tm = t8._Asin = function() {
return (Tm = t8._Asin = t8.asm.Asin).apply(null, arguments);
}, _y = t8._Asinh = function() {
return (_y = t8._Asinh = t8.asm.Asinh).apply(null, arguments);
}, $y = t8._Atan = function() {
return ($y = t8._Atan = t8.asm.Atan).apply(null, arguments);
}, Ey = t8._Atan2 = function() {
return (Ey = t8._Atan2 = t8.asm.Atan2).apply(null, arguments);
}, sl = t8._Atanh = function() {
return (sl = t8._Atanh = t8.asm.Atanh).apply(null, arguments);
}, Ry = t8._AvgPool = function() {
return (Ry = t8._AvgPool = t8.asm.AvgPool).apply(null, arguments);
}, Dy = t8._AvgPool3D = function() {
return (Dy = t8._AvgPool3D = t8.asm.AvgPool3D).apply(null, arguments);
}, Ay = t8._AvgPool3DGrad = function() {
return (Ay = t8._AvgPool3DGrad = t8.asm.AvgPool3DGrad).apply(null, arguments);
}, _u = t8._AvgPoolGrad = function() {
return (_u = t8._AvgPoolGrad = t8.asm.AvgPoolGrad).apply(null, arguments);
}, Fy = t8._BatchMatMul = function() {
return (Fy = t8._BatchMatMul = t8.asm.BatchMatMul).apply(null, arguments);
}, Py = t8._Bincount = function() {
return (Py = t8._Bincount = t8.asm.Bincount).apply(null, arguments);
}, _m = t8._BitwiseAnd = function() {
return (_m = t8._BitwiseAnd = t8.asm.BitwiseAnd).apply(null, arguments);
}, Oy = t8._Ceil = function() {
return (Oy = t8._Ceil = t8.asm.Ceil).apply(null, arguments);
}, al = t8._ClipByValue = function() {
return (al = t8._ClipByValue = t8.asm.ClipByValue).apply(null, arguments);
}, My = t8._Conv2D = function() {
return (My = t8._Conv2D = t8.asm.Conv2D).apply(null, arguments);
}, Ly = t8._Conv2DBackpropInput = function() {
return (Ly = t8._Conv2DBackpropInput = t8.asm.Conv2DBackpropInput).apply(null, arguments);
}, By = t8._Conv3D = function() {
return (By = t8._Conv3D = t8.asm.Conv3D).apply(null, arguments);
}, ki = t8._Conv3DBackpropFilterV2 = function() {
return (ki = t8._Conv3DBackpropFilterV2 = t8.asm.Conv3DBackpropFilterV2).apply(null, arguments);
}, il = t8._Conv3DBackpropInputV2 = function() {
return (il = t8._Conv3DBackpropInputV2 = t8.asm.Conv3DBackpropInputV2).apply(null, arguments);
}, zy = t8._Cos = function() {
return (zy = t8._Cos = t8.asm.Cos).apply(null, arguments);
}, Vy = t8._Cosh = function() {
return (Vy = t8._Cosh = t8.asm.Cosh).apply(null, arguments);
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function jC(r, e) {
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function XC(r) {
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function cl(r, e) {
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function qp(r, e) {
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var Uo = "AddN";
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var Ko = "Asin";
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var Qs = "AvgPool3D";
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var Zo = "BatchMatMul";
var Zs = "BatchToSpaceND";
var Jo = "Bincount";
var Ha = "BitwiseAnd";
var _ce = "BroadcastTo";
var Js = "BroadcastArgs";
var yo = "Cast";
var en = "Ceil";
var bo = "ClipByValue";
var Ri = "Complex";
var Di = "ComplexAbs";
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var ms = "ScatterNd";
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var ha = "SpaceToBatchND";
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var Yi = "StringToHashBucketFast";
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var Es = "TopK";
var Rs = "Transform";
var po = "Transpose";
var Qi = "Unique";
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var Zi = "UnsortedSegmentSum";
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var Au = "FromPixels";
var Ds = "RotateWithOffset";
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A().getBool("IS_TEST") || A().getBool("PROD") || console.warn(...r);
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Ja(n);
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return Mu.fromString(r, true, 16);
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var Ou = hl("b492b66fbe98f273");
var wr = hl("9ae16a3b2f90404f");
function nw(r) {
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function O0(r, e, t8) {
let o = r.slice(e, e + t8);
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function St(r, e) {
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function F0(r, e) {
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function Ji(r, e, t8 = hl("9ddfea08eb382d69")) {
let o = r.xor(e).mul(t8);
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n = n.add(r), s = Qt(s.add(n).add(o), 21);
let a = n;
return n = n.add(e), n = n.add(t8), s = s.add(Qt(n, 44)), [n.add(o), s.add(a)];
}
function Qm(r, e, t8, o) {
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}
function zG(r, e = r.length) {
if (e >= 8) {
let t8 = wr.add(e * 2), o = St(r, 0).add(wr), n = St(r, e - 8), s = Qt(n, 37).mul(t8).add(o), a = Qt(o, 25).add(n).mul(t8);
return Ji(s, a, t8);
}
if (e >= 4) {
let t8 = wr.add(e * 2), o = F0(r, 0);
return Ji(o.shl(3).add(e), F0(r, e - 4), t8);
}
if (e > 0) {
let t8 = r[0], o = r[e >> 1], n = r[e - 1], s = t8 + (o << 8), a = e + (n << 2);
return nw(wr.mul(s).xor(P0.mul(a))).mul(wr);
}
return wr;
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function VG(r, e = r.length) {
let t8 = wr.add(e * 2), o = St(r, 0).mul(Ou), n = St(r, 8), s = St(r, e - 8).mul(t8), a = St(r, e - 16).mul(wr);
return Ji(Qt(o.add(n), 43).add(Qt(s, 30)).add(a), o.add(Qt(n.add(wr), 18)).add(s), t8);
}
function WG(r, e = r.length) {
let t8 = wr.add(e * 2), o = St(r, 0).mul(wr), n = St(r, 8), s = St(r, e - 8).mul(t8), a = St(r, e - 16).mul(wr), i = Qt(o.add(n), 43).add(Qt(s, 30)).add(a), p = Ji(i, o.add(Qt(n.add(wr), 18)).add(s), t8), u = St(r, 16).mul(t8), c = St(r, 24), l = i.add(St(r, e - 32)).mul(t8), m = p.add(St(r, e - 24)).mul(t8);
return Ji(Qt(u.add(c), 43).add(Qt(l, 30)).add(m), u.add(Qt(c.add(o), 18)).add(l), t8);
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function UG(r, e = r.length) {
let t8 = Mu.fromNumber(81, true);
if (e <= 32)
return e <= 16 ? zG(r, e) : VG(r, e);
if (e <= 64)
return WG(r, e);
let o = t8, n = t8.mul(Ou).add(113), s = nw(n.mul(wr).add(113)).mul(wr), a = [Mu.UZERO, Mu.UZERO], i = [Mu.UZERO, Mu.UZERO];
o = o.mul(wr).add(St(r, 0));
let p = 0, u = (e - 1 >> 6) * 64, c = u + (e - 1 & 63) - 63;
do
o = Qt(o.add(n).add(a[0]).add(St(r, p + 8)), 37).mul(Ou), n = Qt(n.add(a[1]).add(St(r, p + 48)), 42).mul(Ou), o = o.xor(i[1]), n = n.add(a[0]).add(St(r, p + 40)), s = Qt(s.add(i[0]), 33).mul(Ou), a = Qm(r, p, a[1].mul(Ou), o.add(i[0])), i = Qm(r, p + 32, s.add(i[1]), n.add(St(r, p + 16))), [s, o] = [o, s], p += 64;
while (p !== u);
let l = Ou.add(s.and(255).shl(1));
return p = c, i[0] = i[0].add(e - 1 & 63), a[0] = a[0].add(i[0]), i[0] = i[0].add(a[0]), o = Qt(o.add(n).add(a[0]).add(St(r, p + 8)), 37).mul(l), n = Qt(n.add(a[1]).add(St(r, p + 48)), 42).mul(l), o = o.xor(i[1].mul(9)), n = n.add(a[0].mul(9).add(St(r, p + 40))), s = Qt(s.add(i[0]), 33).mul(l), a = Qm(r, p, a[1].mul(l), o.add(i[0])), i = Qm(r, p + 32, s.add(i[1]), n.add(St(r, p + 16))), [s, o] = [o, s], Ji(Ji(a[0], i[0], l).add(nw(n).mul(P0)).add(s), Ji(a[1], i[1], l).add(o), l);
}
function GG(r, e) {
return e === "string" ? eu(r) : ec([r], e);
}
function HG(r, e) {
return r instanceof Float32Array && e === "float32" || r instanceof Int32Array && e === "int32" || r instanceof Uint8Array && e === "bool";
}
function ec(r, e) {
if (e === "string")
throw new Error("Cannot convert a string[] to a TypedArray");
if (Array.isArray(r) && (r = Fs(r)), A().getBool("DEBUG") && jC(r, e), HG(r, e))
return r;
if (e == null || e === "float32" || e === "complex64")
return new Float32Array(r);
if (e === "int32")
return new Int32Array(r);
if (e === "bool") {
let t8 = new Uint8Array(r.length);
for (let o = 0; o < t8.length; ++o)
Math.round(r[o]) !== 0 && (t8[o] = 1);
return t8;
} else
throw new Error(`Unknown data type ${e}`);
}
function Lu() {
return A().platform.now();
}
function KG(r, e) {
return A().platform.fetch(r, e);
}
function eu(r, e = "utf-8") {
return e = e || "utf-8", A().platform.encode(r, e);
}
function tc(r, e = "utf-8") {
return e = e || "utf-8", A().platform.decode(r, e);
}
function Pt(r) {
return A().platform.isTypedArray != null ? A().platform.isTypedArray(r) : Xm(r);
}
function Fs(r, e = [], t8 = false) {
if (e == null && (e = []), typeof r == "boolean" || typeof r == "number" || typeof r == "string" || Ru(r) || r == null || Pt(r) && t8)
e.push(r);
else if (Array.isArray(r) || Pt(r))
for (let o = 0; o < r.length; ++o)
Fs(r[o], e, t8);
else {
let o = -1;
for (let n of Object.keys(r))
/^([1-9]+[0-9]*|0)$/.test(n) && (o = Math.max(o, Number(n)));
for (let n = 0; n <= o; n++)
Fs(r[n], e, t8);
}
return e;
}
var Zm = class {
constructor(e, t8) {
this.backendTimer = e, this.logger = t8, t8 == null && (this.logger = new aw());
}
profileKernel(e, t8, o) {
let n, s = () => {
n = o();
}, a, i = Lu();
if (this.backendTimer.timerAvailable())
a = this.backendTimer.time(s);
else {
s();
for (let u of n)
u.dataSync();
a = Promise.resolve({ kernelMs: Lu() - i });
}
if (A().getBool("CHECK_COMPUTATION_FOR_ERRORS"))
for (let u = 0; u < n.length; u++) {
let c = n[u];
c.data().then((l) => {
qG(l, c.dtype, e);
});
}
return { kernelName: e, outputs: n, inputs: t8, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") };
}
logKernelProfile(e) {
let { kernelName: t8, outputs: o, timeMs: n, inputs: s, extraInfo: a } = e;
o.forEach((i) => {
Promise.all([i.data(), n, a]).then((p) => {
this.logger.logKernelProfile(t8, i, p[0], p[1], s, p[2]);
});
});
}
};
function qG(r, e, t8) {
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 ${n} in the result of '${t8}'`), true;
}
return false;
}
var aw = class {
logKernelProfile(e, t8, o, n, s, a) {
let i = typeof n == "number" ? Eu(`${n}ms`, 9) : n.error, p = Eu(e, 25), u = t8.rank, c = t8.size, l = Eu(t8.shape.toString(), 14), m = "";
for (let d in s) {
let f = s[d];
if (f != null) {
let h = f.shape || t8.shape, g = h.length;
m += `${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 M0(r, e, t8) {
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[t8.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 L0(r, e, t8, 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 = t8(() => 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 (!Cr(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 B0 = 20;
var gl = 3;
var iw = 7;
function z0(r, e, t8, o) {
let n = qs(e), s = jG(r, e, t8, n), a = e.length, i = Jm(r, e, t8, n, s), p = ["Tensor"];
return o && (p.push(` dtype: ${t8}`), p.push(` rank: ${a}`), p.push(` shape: [${e}]`), p.push(" values:")), p.push(i.map((u) => " " + u).join(`
`)), p.join(`
`);
}
function jG(r, e, t8, o) {
let n = Ge(e), s = o[o.length - 1], a = new Array(s).fill(0), i = e.length, p = t8 === "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, t8).length);
}
return a;
}
function xl(r, e, t8) {
let o;
return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(iw))} + ${parseFloat(r[1].toFixed(iw))}j` : zo(r) ? o = `'${r}'` : t8 === "bool" ? o = V0(r) : o = parseFloat(r.toFixed(iw)).toString(), Eu(o, e);
}
function V0(r) {
return r === 0 ? "false" : "true";
}
function Jm(r, e, t8, o, n, s = true) {
let a = t8 === "complex64" ? 2 : 1, i = e[0], p = e.length;
if (p === 0) {
if (t8 === "complex64") {
let h = yl(r);
return [xl(h[0], 0, t8)];
}
return t8 === "bool" ? [V0(r[0])] : [r[0].toString()];
}
if (p === 1) {
if (i > B0) {
let g = gl * a, x = Array.from(r.slice(0, g)), b = Array.from(r.slice((i - gl) * a, i * a));
return t8 === "complex64" && (x = yl(x), b = yl(b)), ["[" + x.map((C, S) => xl(C, n[S], t8)).join(", ") + ", ..., " + b.map((C, S) => xl(C, n[i - gl + S], t8)).join(", ") + "]"];
}
return ["[" + (t8 === "complex64" ? yl(r) : Array.from(r)).map((g, x) => xl(g, n[x], t8)).join(", ") + "]"];
}
let u = e.slice(1), c = o.slice(1), l = o[0] * a, m = [];
if (i > B0) {
for (let h = 0; h < gl; h++) {
let g = h * l, x = g + l;
m.push(...Jm(r.slice(g, x), u, t8, c, n, false));
}
m.push("...");
for (let h = i - gl; h < i; h++) {
let g = h * l, x = g + l;
m.push(...Jm(r.slice(g, x), u, t8, c, n, h === i - 1));
}
} else
for (let h = 0; h < i; h++) {
let g = h * l, x = g + l;
m.push(...Jm(r.slice(g, x), u, t8, 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 t8 = 0; t8 < r.length; t8 += 2)
e.push([r[t8], r[t8 + 1]]);
return e;
}
var tt = class {
constructor(e, t8, o) {
if (this.dtype = t8, this.shape = e.slice(), this.size = Ge(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 (t8 === "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 || Km(t8, this.size), this.strides = qs(e);
}
set(e, ...t8) {
t8.length === 0 && (t8 = [0]), $(t8.length === this.rank, () => `The number of provided coordinates (${t8.length}) must match the rank (${this.rank})`);
let o = this.locToIndex(t8);
this.values[o] = e;
}
get(...e) {
e.length === 0 && (e = [0]);
let t8 = 0;
for (let n of e) {
if (n < 0 || n >= this.shape[t8]) {
let s = `Requested out of range element at ${e}. Buffer shape=${this.shape}`;
throw new Error(s);
}
t8++;
}
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 t8 = e[e.length - 1];
for (let o = 0; o < e.length - 1; ++o)
t8 += this.strides[o] * e[o];
return t8;
}
indexToLoc(e) {
if (this.rank === 0)
return [];
if (this.rank === 1)
return [e];
let t8 = new Array(this.shape.length);
for (let o = 0; o < t8.length - 1; ++o)
t8[o] = Math.floor(e / this.strides[o]), e -= t8[o] * this.strides[o];
return t8[t8.length - 1] = e, t8;
}
get rank() {
return this.shape.length;
}
toTensor() {
return Ps().makeTensor(this.values, this.shape, this.dtype);
}
};
var Ps = null;
var rc = null;
var XG = null;
function W0(r) {
Ps = r;
}
function U0(r) {
rc = r;
}
function G0(r) {
XG = r;
}
var ut = class {
constructor(e, t8, o, n) {
this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t8 || "float32", this.size = Ge(e), this.strides = qs(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 rc.buffer(this.shape, this.dtype, e);
}
bufferSync() {
return rc.buffer(this.shape, this.dtype, this.dataSync());
}
async array() {
let e = await this.data();
return $u(this.shape, e, this.dtype === "complex64");
}
arraySync() {
return $u(this.shape, this.dataSync(), this.dtype === "complex64");
}
async data() {
this.throwIfDisposed();
let e = Ps().read(this.dataId);
if (this.dtype === "string") {
let t8 = await e;
try {
return t8.map((o) => tc(o));
} catch (o) {
throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
}
}
return e;
}
dataToGPU(e) {
return this.throwIfDisposed(), Ps().readToGPU(this.dataId, e);
}
dataSync() {
this.throwIfDisposed();
let e = Ps().readSync(this.dataId);
if (this.dtype === "string")
try {
return e.map((t8) => tc(t8));
} catch (t8) {
throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
}
return e;
}
async bytes() {
this.throwIfDisposed();
let e = await Ps().read(this.dataId);
return this.dtype === "string" ? e : new Uint8Array(e.buffer);
}
dispose() {
this.isDisposed || (Ps().disposeTensor(this), this.isDisposedInternal = true);
}
get isDisposed() {
return this.isDisposedInternal;
}
throwIfDisposed() {
if (this.isDisposed)
throw new Error("Tensor is disposed.");
}
print(e = false) {
return rc.print(this, e);
}
clone() {
return this.throwIfDisposed(), rc.clone(this);
}
toString(e = false) {
let t8 = this.dataSync();
return z0(t8, this.shape, this.dtype, e);
}
cast(e) {
return this.throwIfDisposed(), rc.cast(this, e);
}
variable(e = true, t8, o) {
return this.throwIfDisposed(), Ps().makeVariable(this, e, t8, o);
}
};
Object.defineProperty(ut, Symbol.hasInstance, { value: (r) => !!r && r.data != null && r.dataSync != null && r.throwIfDisposed != null });
function YG() {
return ml("Tensor", () => ut);
}
YG();
var ei = class extends ut {
constructor(e, t8, o, n) {
super(e.shape, e.dtype, e.dataId, n), this.trainable = t8, this.name = o;
}
assign(e) {
if (e.dtype !== this.dtype)
throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);
if (!Cr(e.shape, this.shape))
throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);
Ps().disposeTensor(this), this.dataId = e.dataId, Ps().incRef(this, null);
}
dispose() {
Ps().disposeVariable(this), this.isDisposedInternal = true;
}
};
Object.defineProperty(ei, Symbol.hasInstance, { value: (r) => r instanceof ut && r.assign != null && r.assign instanceof Function });
var K0 = {};
Ke(K0, { assertTypesMatch: () => dw, getTensorsInContainer: () => bl, isTensorInList: () => ZG, makeTypesMatch: () => Oe });
var uw;
(function(r) {
r.R0 = "R0", r.R1 = "R1", r.R2 = "R2", r.R3 = "R3", r.R4 = "R4", r.R5 = "R5", r.R6 = "R6";
})(uw || (uw = {}));
var pw;
(function(r) {
r.float32 = "float32", r.int32 = "int32", r.bool = "int32", r.complex64 = "complex64";
})(pw || (pw = {}));
var cw;
(function(r) {
r.float32 = "float32", r.int32 = "int32", r.bool = "bool", r.complex64 = "complex64";
})(cw || (cw = {}));
var lw;
(function(r) {
r.float32 = "float32", r.int32 = "float32", r.bool = "float32", r.complex64 = "complex64";
})(lw || (lw = {}));
var mw;
(function(r) {
r.float32 = "complex64", r.int32 = "complex64", r.bool = "complex64", r.complex64 = "complex64";
})(mw || (mw = {}));
var QG = { float32: lw, int32: pw, bool: cw, complex64: mw };
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 QG[r][e];
}
function ti(r) {
return dt(r, "int32");
}
function ed(r) {
return r != null && typeof r == "object" && "texture" in r && r.texture instanceof WebGLTexture;
}
function td(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 t8 = dt(r.dtype, e.dtype);
return [r.cast(t8), e.cast(t8)];
}
function dw(r, e) {
$(r.dtype === e.dtype, () => `The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`);
}
function ZG(r, e) {
return e.some((t8) => t8.id === r.id);
}
function bl(r) {
let e = [];
return H0(r, e, /* @__PURE__ */ new Set()), e;
}
function H0(r, e, t8) {
if (r == null)
return;
if (r instanceof ut) {
e.push(r);
return;
}
if (!JG(r))
return;
let o = r;
for (let n in o) {
let s = o[n];
t8.has(s) || (t8.add(s), H0(s, e, t8));
}
}
function JG(r) {
return Array.isArray(r) || typeof r == "object";
}
function fw(r) {
return r.kernelName != null;
}
var rd = class {
constructor() {
this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() {
return Array.from(new Set(this.kernels.map((e) => e.name)));
} };
}
dispose() {
for (let e in this.registeredVariables)
this.registeredVariables[e].dispose();
}
};
var tu = class {
constructor(e) {
this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new rd();
}
async ready() {
if (this.pendingBackendInit != null)
return this.pendingBackendInit.then(() => {
});
if (this.backendInstance != null)
return;
let e = this.getSortedBackends();
for (let t8 = 0; t8 < e.length; t8++) {
let o = e[t8];
if (await this.initializeBackend(o).success) {
await this.setBackend(o);
return;
}
}
throw new Error("Could not initialize any backends, all backend initializations failed.");
}
get backend() {
if (this.pendingBackendInit != null)
throw new Error(`Backend '${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: t8 } = this.initializeBackendsAndReturnBest();
if (t8)
throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
this.setBackend(e);
}
return this.backendInstance;
}
backendNames() {
return Object.keys(this.registryFactory);
}
findBackend(e) {
if (!(e in this.registry))
if (e in this.registryFactory) {
let { asyncInit: t8 } = this.initializeBackend(e);
if (t8)
return null;
} else
return null;
return this.registry[e];
}
findBackendFactory(e) {
return e in this.registryFactory ? this.registryFactory[e].factory : null;
}
registerBackend(e, t8, o = 1) {
return e in this.registryFactory ? (Ca(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t8, 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: t8, asyncInit: o } = this.initializeBackend(e);
if (!(o ? await t8 : t8))
return false;
}
return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new Zm(this.backendInstance), true;
}
setupRegisteredKernels() {
jm(this.backendName).forEach((t8) => {
t8.setupFunc != null && t8.setupFunc(this.backendInstance);
});
}
disposeRegisteredKernels(e) {
jm(e).forEach((o) => {
o.disposeFunc != null && o.disposeFunc(this.registry[e]);
});
}
initializeBackend(e) {
let t8 = this.registryFactory[e];
if (t8 == null)
throw new Error(`Cannot initialize backend ${e}, no registration found.`);
try {
let o = t8.factory();
if (o && !(o instanceof so) && 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, Ca(`Initialization of backend ${e} failed`), Ca(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 Ca(`Initialization of backend ${e} failed`), Ca(o.stack || o.message), { success: false, asyncInit: false };
}
}
removeBackend(e) {
if (!(e in this.registryFactory))
throw new Error(`${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, t8) => this.registryFactory[t8].priority - this.registryFactory[e].priority);
}
initializeBackendsAndReturnBest() {
let e = this.getSortedBackends();
for (let t8 = 0; t8 < e.length; t8++) {
let o = e[t8], { 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, t8) {
let o = this.state.tensorInfo.get(t8), n = o.backend, s = this.readSync(t8), a = n.refCount(t8);
n.disposeData(t8, true), o.backend = e, e.move(t8, s, o.shape, o.dtype, a), this.shouldCheckForMemLeaks() && this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
}
tidy(e, t8) {
let o = null;
if (t8 == null) {
if (typeof e != "function")
throw new Error("Please provide a function to tidy()");
t8 = 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 t8 != "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 = t8(), n instanceof Promise && console.error("Cannot return a Promise inside of tidy."), n));
}
scopedRun(e, t8, o) {
e();
try {
let n = o();
return t8(), n;
} catch (n) {
throw t8(), n;
}
}
nextTensorId() {
return tu.nextTensorId++;
}
nextVariableId() {
return tu.nextVariableId++;
}
clone(e) {
let t8 = T.runKernel(Co, { x: e }), o = { x: e }, n = (a) => ({ x: () => {
let i = "float32", p = { x: a }, u = { dtype: i };
return T.runKernel(yo, p, u);
} }), s = [];
return this.addTapeNode(this.state.activeScope.name, o, [t8], n, s, {}), t8;
}
runKernel(e, t8, 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: t8, attrs: o });
}
shouldCheckForMemLeaks() {
return this.ENV.getBool("IS_TEST");
}
checkKernelForMemLeak(e, t8, 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 - t8 - 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 t8, 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 = fw(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
if (fw(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 '${this.backendName}'`), i = () => {
let b = this.backend.numDataIds();
p = x.kernelFunc({ inputs: h, attrs: g, backend: this.backend });
let C = Array.isArray(p) ? p : [p];
this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(f, b, C);
let S = C.map((k) => k.rank != null ? k : this.makeTensorFromTensorInfo(k));
if (n) {
let k = this.getTensorsForGradient(f, h, S);
o = this.saveTensorsForBackwardMode(k);
}
return S;
};
} else {
let { forwardFunc: f } = e, h = (g) => {
n && (o = g.map((x) => this.keep(this.clone(x))));
};
i = () => {
let g = this.backend.numDataIds();
p = this.tidy(() => f(this.backend, h));
let x = Array.isArray(p) ? p : [p];
return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, g, x), x;
};
}
let { inputs: c, attrs: l } = e, m = fw(e) ? null : e.backwardsFunc, d;
return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
!this.ENV.getBool("DEBUG") && !this.state.profiling ? t8 = i() : (d = this.profiler.profileKernel(u, c, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t8 = d.outputs);
}), n && this.addTapeNode(u, c, t8, m, o, l), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - s, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(c).map((f) => c[f] != null ? c[f].shape : null), outputShapes: t8.map((f) => f.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(p) ? t8 : t8[0];
}
saveTensorsForBackwardMode(e) {
return e.map((o) => this.keep(this.clone(o)));
}
getTensorsForGradient(e, t8, o) {
let n = ew(e);
if (n != null) {
let s = n.inputsToSave || [], a = n.outputsToSave || [], i;
n.saveAllInputs ? ($(Array.isArray(t8), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t8).map((u) => t8[u])) : i = s.map((u) => t8[u]);
let p = o.filter((u, c) => a[c]);
return i.concat(p);
}
return [];
}
makeTensor(e, t8, 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" && zo(e[0]) && (s = e.map((p) => eu(p)));
let a = n.write(s, t8, o), i = new ut(t8, o, a, this.nextTensorId());
if (this.trackTensor(i, n), o === "string") {
let p = this.state.tensorInfo.get(a), u = YC(s);
this.state.numBytes += u - p.bytes, p.bytes = u;
}
return i;
}
makeTensorFromDataId(e, t8, o, n) {
o = o || "float32";
let s = { dataId: e, shape: t8, dtype: o };
return this.makeTensorFromTensorInfo(s, n);
}
makeTensorFromTensorInfo(e, t8) {
let { dataId: o, shape: n, dtype: s } = e, a = new ut(n, s, o, this.nextTensorId());
return this.trackTensor(a, t8), a;
}
makeVariable(e, t8 = true, o, n) {
o = o || this.nextVariableId().toString(), n != null && n !== e.dtype && (e = e.cast(n));
let s = new ei(e, t8, 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, t8) {
this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++;
let o = 0;
e.dtype !== "complex64" && e.dtype !== "string" && (o = e.size * Hp(e.dtype)), this.state.numBytes += o, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t8 || this.backend, dtype: e.dtype, shape: e.shape, bytes: o })), e instanceof ei || this.track(e);
}
incRef(e, t8) {
this.trackTensor(e, t8), this.backend.incRef(e.dataId);
}
removeDataId(e, t8) {
this.state.tensorInfo.has(e) && this.state.tensorInfo.get(e).backend === t8 && (this.state.tensorInfo.delete(e), this.state.numDataBuffers--);
}
disposeTensor(e) {
if (!this.state.tensorInfo.has(e.dataId))
return;
let t8 = this.state.tensorInfo.get(e.dataId);
if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t8.bytes), e.dtype !== "complex64" && e.dtype !== "string") {
let o = e.size * Hp(e.dtype);
this.state.numBytes -= o;
}
t8.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t8.backend);
}
disposeVariables() {
for (let e in this.state.registeredVariables) {
let t8 = this.state.registeredVariables[e];
this.disposeVariable(t8);
}
}
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 t8 = 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 - t8, 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, t8, o, n, s, a) {
let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t8, outputs: o, saved: s }, p = ew(e);
p != null && (n = p.gradFunc), n != null && (i.gradient = (u) => (u = u.map((c, l) => {
if (c == null) {
let m = o[l], d = qp(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 t8 = { track: [], name: "unnamed scope", id: this.state.nextScopeId++ };
e && (t8.name = e), this.state.scopeStack.push(t8), this.state.activeScope = t8;
}
endScope(e) {
let t8 = bl(e), o = new Set(t8.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], t8.forEach((s) => {
!s.kept && s.scopeId === n.id && this.track(s);
});
}
gradients(e, t8, o, n = false) {
if ($(t8.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 ut, () => "The result y returned by f() must be a tensor.");
let a = M0(this.state.activeTape, t8, s);
if (!n && a.length === 0 && t8.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 ? e4(s.shape) : o, L0(i, a, (u) => this.tidy(u), t4);
let p = t8.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 $(Ks(e), () => "The f passed in customGrad(f) must be a function."), (...t8) => {
$(t8.every((i) => i instanceof ut), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
let o, n = {};
t8.forEach((i, p) => {
n[p] = i;
});
let s = (i, p) => (o = e(...t8, p), $(o.value instanceof ut, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), $(Ks(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 === t8.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 ut), () => "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, t8) {
return this.state.tensorInfo.get(e).backend.readToGPU(e, t8);
}
async time(e) {
let t8 = Lu(), o = await this.backend.time(e);
return o.wallMs = Lu() - t8, 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 rd();
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;
}
};
tu.nextTensorId = 0;
tu.nextVariableId = 0;
function e4(r) {
let e = cl(Ge(r), "float32");
return T.makeTensor(e, r, "float32");
}
function hw() {
let r = JC();
if (r._tfengine == null) {
let e = new ll(r);
r._tfengine = new tu(e);
}
return C0(r._tfengine.ENV), W0(() => r._tfengine), r._tfengine;
}
var T = hw();
function t4(r, e) {
let t8 = { a: r, b: e };
return T.runKernel(io, t8);
}
var ru = {};
Ke(ru, { isBrowser: () => xw, isMobile: () => n4, mockIsMobile: () => o4 });
function r4() {
return typeof navigator != "undefined" && navigator != null;
}
var gw;
function o4(r) {
gw = r;
}
function n4(r) {
if (gw !== void 0)
return gw;
if (r || r4()) {
if (r || (r = navigator), r.product === "ReactNative")
return true;
let e = r.userAgent || r.vendor || (typeof window != "undefined" ? window.opera : "");
if (!e) {
let t8 = r;
return t8.userAgentData && t8.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 xw() {
return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined";
}
var $r = A();
$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", () => xw());
$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 t8 = r;
if (Pt(r))
return e === "string" ? [] : [r.length];
if (ed(r)) {
let n = r.channels || "RGBA";
return [r.height, r.width * n.length];
} else if (td(r))
return [r.buffer.size / (e == null ? 4 : Hp(e))];
if (!Array.isArray(r))
return [];
let o = [];
for (; Array.isArray(t8) || Pt(t8) && e !== "string"; )
o.push(t8.length), t8 = t8[0];
return Array.isArray(r) && A().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && j0(r, o, []), o;
}
function j0(r, e, t8) {
if (t8 = t8 || [], !Array.isArray(r) && !Pt(r)) {
$(e.length === 0, () => `Element arr[${t8.join("][")}] is a primitive, but should be an array/TypedArray of ${e[0]} elements`);
return;
}
$(e.length > 0, () => `Element arr[${t8.join("][")}] should be a primitive, but is an array of ${r.length} elements`), $(r.length === e[0], () => `Element arr[${t8.join("][")}] should have ${e[0]} elements, but has ${r.length} elements`);
let o = e.slice(1);
for (let n = 0; n < r.length; ++n)
j0(r[n], o, t8.concat(n));
}
function q0(r, e, t8, o) {
if (r !== "string_or_numeric") {
if (r == null)
throw new Error("Expected dtype cannot be null.");
if (r !== "numeric" && r !== e || r === "numeric" && e === "string")
throw new Error(`Argument '${t8}' passed to '${o}' must be ${r} tensor, but got ${e} tensor`);
}
}
function v(r, e, t8, o = "numeric") {
if (r instanceof ut)
return q0(o, r.dtype, e, t8), r;
let n = _i(r);
if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), q0(o, n, e, t8), r == null || !Pt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") {
let p = r == null ? "null" : r.constructor.name;
throw new Error(`Argument '${e}' passed to '${t8}' must be a Tensor or TensorLike, but got '${p}'`);
}
let s = ar(r, n);
!Pt(r) && !Array.isArray(r) && (r = [r]);
let i = n !== "string" ? ec(r, n) : Fs(r, [], true);
return T.makeTensor(i, s, n);
}
function ri(r, e, t8, o = "numeric") {
if (!Array.isArray(r))
throw new Error(`Argument ${e} passed to ${t8} must be a \`Tensor[]\` or \`TensorLike[]\``);
return r.map((s, a) => v(s, `${e}[${a}]`, t8, o));
}
var yw = "__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 t8 = e[0], o = r[t8];
t8.endsWith("_") && (t8 = t8.substring(0, t8.length - 1)), t8 = t8 + yw;
let n = (...s) => {
T.startScope(t8);
try {
let a = o(...s);
return Ru(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: t8, configurable: true }), n;
}
function s4(r, e) {
let t8 = v(r, "real", "complex"), o = v(e, "imag", "complex");
yt(t8.shape, o.shape, `real and imag shapes, ${t8.shape} and ${o.shape}, must match in call to tf.complex().`);
let n = { real: t8, imag: o };
return T.runKernel(Ri, n);
}
var Er = N({ complex_: s4 });
function Sr(r, e, t8, o) {
if (o == null)
o = _i(r);
else if (o === "complex64")
throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");
if (td(r) || ed(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 || t8, 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) {
wt(e);
let n = Ge(e), s = Ge(t8);
$(n === s, () => `Based on the provided shape, [${e}], the tensor should have ${n} values but has ${s}`);
for (let a = 0; a < t8.length; ++a) {
let i = t8[a], p = a === t8.length - 1 ? i !== Ge(e.slice(a)) : true;
$(t8[a] === e[a] || !p, () => `Error creating a new Tensor. Inferred shape (${t8}) does not match the provided shape (${e}). `);
}
}
return !Pt(r) && !Array.isArray(r) && (r = [r]), e = e || t8, r = o !== "string" ? ec(r, o) : Fs(r, [], true), T.makeTensor(r, e, o);
}
function ir(r, e, t8) {
let o = ar(r, t8);
return Sr(r, e, o, t8);
}
var Cl = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
var jt = class {
static join(e) {
return new jt(e).slice();
}
constructor(e) {
if (this.shards = [], this.previousShardIndex = 0, e == null || (e instanceof Array || (e = [e]), e = e.map((o) => Pt(o) ? o.buffer : o), e.length === 0))
return;
this.bufferUniformSize = e[0].byteLength;
let t8 = 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 = t8 + n.byteLength;
this.shards.push({ buffer: n, start: t8, end: s }), t8 = s;
}
this.shards.length === 0 && (this.byteLength = 0), this.byteLength = this.shards[this.shards.length - 1].end;
}
slice(e = 0, t8 = this.byteLength) {
if (this.shards.length === 0)
return new ArrayBuffer(0);
if (e = isNaN(Number(e)) ? 0 : e, t8 = isNaN(Number(t8)) ? 0 : t8, e = Math.max(0, e), t8 = Math.min(this.byteLength, t8), t8 <= e)
return new ArrayBuffer(0);
let o = this.findShardForByte(e);
if (o === -1)
throw new Error(`Could not find start shard for byte ${e}`);
let n = t8 - e, s = new ArrayBuffer(n), a = new Uint8Array(s), i = 0;
for (let p = o; p < this.shards.length; p++) {
let u = this.shards[p], l = e + i - u.start, m = i, f = Math.min(t8, u.end) - u.start, h = new Uint8Array(u.buffer, l, f - l);
if (a.set(h, m), i += h.length, t8 < u.end)
break;
}
return s;
}
findShardForByte(e) {
if (this.shards.length === 0 || e < 0 || e >= this.byteLength)
return -1;
if (this.bufferUniformSize != null)
return this.previousShardIndex = Math.floor(e / this.bufferUniformSize), this.previousShardIndex;
function t8(n) {
return e < n.start ? -1 : e >= n.end ? 1 : 0;
}
if (t8(this.shards[this.previousShardIndex]) === 0)
return this.previousShardIndex;
let o = a4(this.shards, t8);
return o === -1 ? -1 : (this.previousShardIndex = o, this.previousShardIndex);
}
};
function a4(r, e) {
let t8 = 0, o = r.length;
for (; t8 <= o; ) {
let n = Math.floor((o - t8) / 2) + t8, s = e(r[n]);
if (s === 0)
return n;
s < 0 ? o = n : t8 = n + 1;
}
return -1;
}
var od = 4;
async function Y0(r, e) {
let t8 = [], o = [], n = Array.isArray(r) ? r.map((a) => a.name) : Object.keys(r);
for (let a = 0; a < n.length; ++a) {
let i = n[a], p = Array.isArray(r) ? r[a].tensor : r[i];
if (p.dtype !== "float32" && p.dtype !== "int32" && p.dtype !== "bool" && p.dtype !== "string" && p.dtype !== "complex64")
throw new Error(`Unsupported dtype in weight '${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) + od * 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 += od, f.set(x, h), h += x.length;
}
l(f);
});
o.push(c);
} else
o.push(p.data());
e != null && (u.group = e), t8.push(u);
}
let s = await Promise.all(o);
return { data: i4(s), specs: t8 };
}
function nd(r, e) {
let t8 = new jt(r), o = {}, n, s = 0;
for (let a of e) {
let i = a.name, p = a.dtype, u = a.shape, c = Ge(u), l;
if ("quantization" in a) {
let m = a.quantization;
if (m.dtype === "uint8" || m.dtype === "uint16") {
if (!("min" in m && "scale" in m))
throw new Error(`Weight ${a.name} with quantization ${m.dtype} doesn't have corresponding metadata min and scale.`);
} else if (m.dtype === "float16") {
if (p !== "float32")
throw new Error(`Weight ${a.name} is quantized with ${m.dtype} which only supports weights of type float32 not ${p}.`);
} else
throw new Error(`Weight ${a.name} has unknown quantization dtype ${m.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
let d = Cl[m.dtype], f = t8.slice(s, s + c * d), h = m.dtype === "uint8" ? new Uint8Array(f) : new Uint16Array(f);
if (p === "float32")
if (m.dtype === "uint8" || m.dtype === "uint16") {
l = new Float32Array(h.length);
for (let g = 0; g < h.length; g++) {
let x = h[g];
l[g] = x * m.scale + m.min;
}
} else if (m.dtype === "float16")
n === void 0 && (n = l4()), l = n(h);
else
throw new Error(`Unsupported quantization type ${m.dtype} for weight type float32.`);
else if (p === "int32") {
if (m.dtype !== "uint8" && m.dtype !== "uint16")
throw new Error(`Unsupported quantization type ${m.dtype} for weight type int32.`);
l = new Int32Array(h.length);
for (let g = 0; g < h.length; g++) {
let x = h[g];
l[g] = Math.round(x * m.scale + m.min);
}
} else
throw new Error(`Unsupported dtype in weight '${i}': ${p}`);
s += c * d;
} else if (p === "string") {
let m = Ge(a.shape);
l = [];
for (let d = 0; d < m; d++) {
let f = new Uint32Array(t8.slice(s, s + od))[0];
s += od;
let h = new Uint8Array(t8.slice(s, s + f));
l.push(h), s += f;
}
} else {
let m = Cl[p], d = t8.slice(s, s + c * m);
if (p === "float32")
l = new Float32Array(d);
else if (p === "int32")
l = new Int32Array(d);
else if (p === "bool")
l = new Uint8Array(d);
else if (p === "complex64") {
l = new Float32Array(d);
let f = new Float32Array(l.length / 2), h = new Float32Array(l.length / 2);
for (let b = 0; b < f.length; b++)
f[b] = l[b * 2], h[b] = l[b * 2 + 1];
let g = ir(f, u, "float32"), x = ir(h, u, "float32");
o[i] = Er(g, x), g.dispose(), x.dispose();
} else
throw new Error(`Unsupported dtype in weight '${i}': ${p}`);
s += c * m;
}
p !== "complex64" && (o[i] = ir(l, u, p));
}
return o;
}
function i4(r) {
if (r === null)
throw new Error(`Invalid input value: ${JSON.stringify(r)}`);
let e = 0, t8 = [];
r.forEach((s) => {
if (e += s.byteLength, t8.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 t8.forEach((s) => {
o.set(new Uint8Array(s.buffer), n), n += s.byteLength;
}), o.buffer;
}
var bw = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
function X0(r) {
return bw ? Buffer.byteLength(r, "utf8") : new Blob([r]).size;
}
function Q0(r) {
if (bw)
return Buffer.from(r).toString("base64");
let e = new Uint8Array(r), t8 = "";
for (let o = 0, n = e.length; o < n; o++)
t8 += String.fromCharCode(e[o]);
return btoa(t8);
}
function Z0(r) {
if (bw) {
let o = Buffer.from(r, "base64");
return o.buffer.slice(o.byteOffset, o.byteOffset + o.byteLength);
}
let e = atob(r), t8 = new Uint8Array(e.length);
for (let o = 0; o < e.length; ++o)
t8.set([e.charCodeAt(o)], o);
return t8.buffer;
}
function J0(r) {
return jt.join(r);
}
function Cw(r) {
let e = "/";
for (r = r.trim(); r.endsWith(e); )
r = r.slice(0, r.length - 1);
let t8 = r.split(e);
return t8[t8.length - 1];
}
function sd(r, e) {
let t8 = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy, weightsManifest: e };
return r.signature != null && (t8.signature = r.signature), r.userDefinedMetadata != null && (t8.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (t8.modelInitializer = r.modelInitializer), r.initializerSignature != null && (t8.initializerSignature = r.initializerSignature), r.trainingConfig != null && (t8.trainingConfig = r.trainingConfig), t8;
}
function ww(r, e, t8) {
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 (!t8)
throw new Error("modelJSON has weightsManifest but weightData is null");
o.weightSpecs = e, o.weightData = t8;
}
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 oc(r, e) {
let t8, o;
return r.weightsManifest != null && ([t8, o] = await e(r.weightsManifest)), ww(r, t8, o);
}
function wa(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 : X0(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : X0(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : new jt(r.weightData).byteLength };
}
function ad(r) {
let e = [];
for (let t8 of r)
e.push(...t8.weights);
return e;
}
function u4() {
let r = (t8) => {
let o = t8 << 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 t8 = 1; t8 < 1024; t8++)
e[t8] = r(t8);
for (let t8 = 1024; t8 < 2048; t8++)
e[t8] = 939524096 + (t8 - 1024 << 13);
return e;
}
function p4() {
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 c4() {
let r = new Uint32Array(64);
for (let e = 0; e < 64; e++)
r[e] = 1024;
return r[0] = r[32] = 0, r;
}
function l4() {
let r = u4(), e = p4(), t8 = c4();
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[t8[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, t8) {
return ft.getHandlers(e, "load", t8);
}
static getHandlers(e, t8, o) {
let n = [];
return (t8 === "load" ? ft.getInstance().loadRouters : ft.getInstance().saveRouters).forEach((a) => {
let i = a(e, o);
i !== null && n.push(i);
}), n;
}
};
var ek = (r) => ft.registerSaveRouter(r);
var tk = (r) => ft.registerLoadRouter(r);
var rk = (r) => ft.getSaveHandlers(r);
var ok = (r, e) => ft.getLoadHandlers(r, e);
var Sw = "tensorflowjs";
var Iw = 1;
var Bu = "models_store";
var ou = "model_info_store";
function nk() {
if (!A().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 vw(r) {
let e = r.result;
e.createObjectStore(Bu, { keyPath: "modelPath" }), e.createObjectStore(ou, { keyPath: "modelPath" });
}
var Sa = class {
constructor(e) {
if (this.indexedDB = nk(), 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, t8) {
return new Promise((o, n) => {
let s = this.indexedDB.open(Sw, Iw);
s.onupgradeneeded = () => vw(s), s.onsuccess = () => {
let a = s.result;
if (t8 == null) {
let i = a.transaction(Bu, "readonly"), u = i.objectStore(Bu).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 {
t8.weightData = jt.join(t8.weightData);
let i = wa(t8), p = a.transaction(ou, "readwrite"), u = p.objectStore(ou), c;
try {
c = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i });
} catch (m) {
return n(m);
}
let l;
c.onsuccess = () => {
l = a.transaction(Bu, "readwrite");
let m = l.objectStore(Bu), d;
try {
d = m.put({ modelPath: this.modelPath, modelArtifacts: t8, modelArtifactsInfo: i });
} catch (f) {
return n(f);
}
d.onsuccess = () => o({ modelArtifactsInfo: i }), d.onerror = (f) => {
u = p.objectStore(ou);
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);
});
}
};
Sa.URL_SCHEME = "indexeddb://";
var sk = (r) => A().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Sa.URL_SCHEME) ? m4(r.slice(Sa.URL_SCHEME.length)) : null;
ft.registerSaveRouter(sk);
ft.registerLoadRouter(sk);
function m4(r) {
return new Sa(r);
}
function d4(r) {
return r.startsWith(Sa.URL_SCHEME) ? r.slice(Sa.URL_SCHEME.length) : r;
}
var id = class {
constructor() {
this.indexedDB = nk();
}
async listModels() {
return new Promise((e, t8) => {
let o = this.indexedDB.open(Sw, Iw);
o.onupgradeneeded = () => vw(o), o.onsuccess = () => {
let n = o.result, s = n.transaction(ou, "readonly"), i = s.objectStore(ou).getAll();
i.onsuccess = () => {
let p = {};
for (let u of i.result)
p[u.modelPath] = u.modelArtifactsInfo;
e(p);
}, i.onerror = (p) => (n.close(), t8(i.error)), s.oncomplete = () => n.close();
}, o.onerror = (n) => t8(o.error);
});
}
async removeModel(e) {
return e = d4(e), new Promise((t8, o) => {
let n = this.indexedDB.open(Sw, Iw);
n.onupgradeneeded = () => vw(n), n.onsuccess = () => {
let s = n.result, a = s.transaction(ou, "readwrite"), i = a.objectStore(ou), 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(Bu, "readwrite");
let d = u.objectStore(Bu).delete(e);
d.onsuccess = () => t8(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 oi = "/";
var nc = "tensorflowjs_models";
var ak = "info";
var f4 = "model_topology";
var h4 = "weight_specs";
var g4 = "weight_data";
var x4 = "model_metadata";
function ik(r) {
return { info: [nc, r, ak].join(oi), topology: [nc, r, f4].join(oi), weightSpecs: [nc, r, h4].join(oi), weightData: [nc, r, g4].join(oi), modelMetadata: [nc, r, x4].join(oi) };
}
function uk(r) {
for (let e of Object.values(r))
window.localStorage.removeItem(e);
}
function y4(r) {
let e = r.split(oi);
if (e.length < 3)
throw new Error(`Invalid key format: ${r}`);
return e.slice(1, e.length - 1).join(oi);
}
function b4(r) {
return r.startsWith(Ia.URL_SCHEME) ? r.slice(Ia.URL_SCHEME.length) : r;
}
var Ia = class {
constructor(e) {
if (!A().getBool("IS_BROWSER") || typeof window == "undefined" || typeof window.localStorage == "undefined")
throw new Error("The current environment does not support local storage.");
if (this.LS = window.localStorage, e == null || !e)
throw new Error("For local storage, modelPath must not be null, undefined or empty.");
this.modelPath = e, this.keys = ik(this.modelPath);
}
async save(e) {
if (e.modelTopology instanceof ArrayBuffer)
throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
{
let t8 = JSON.stringify(e.modelTopology), o = JSON.stringify(e.weightSpecs), n = wa(e), s = jt.join(e.weightData);
try {
this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, t8), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, Q0(s));
let a = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, initializerSignature: e.initializerSignature != null ? e.initializerSignature : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(a)), { modelArtifactsInfo: n };
} catch (a) {
throw uk(this.keys), new Error(`Failed to save model '${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 t8 = {}, o = JSON.parse(this.LS.getItem(this.keys.topology));
if (o == null)
throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);
t8.modelTopology = o;
let n = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
if (n == null)
throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);
t8.weightSpecs = n;
let s = this.LS.getItem(this.keys.modelMetadata);
if (s != null) {
let i = JSON.parse(s);
t8.format = i.format, t8.generatedBy = i.generatedBy, t8.convertedBy = i.convertedBy, i.signature != null && (t8.signature = i.signature), i.userDefinedMetadata != null && (t8.userDefinedMetadata = i.userDefinedMetadata), i.modelInitializer != null && (t8.modelInitializer = i.modelInitializer), i.initializerSignature != null && (t8.initializerSignature = i.initializerSignature), i.trainingConfig != null && (t8.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 t8.weightData = Z0(a), t8;
}
};
Ia.URL_SCHEME = "localstorage://";
var pk = (r) => A().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ia.URL_SCHEME) ? C4(r.slice(Ia.URL_SCHEME.length)) : null;
ft.registerSaveRouter(pk);
ft.registerLoadRouter(pk);
function C4(r) {
return new Ia(r);
}
var ud = class {
constructor() {
$(A().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), $(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
}
async listModels() {
let e = {}, t8 = nc + oi, o = oi + ak;
for (let n = 0; n < this.LS.length; ++n) {
let s = this.LS.key(n);
if (s.startsWith(t8) && s.endsWith(o)) {
let a = y4(s);
e[a] = JSON.parse(this.LS.getItem(s));
}
}
return e;
}
async removeModel(e) {
e = b4(e);
let t8 = ik(e);
if (this.LS.getItem(t8.info) == null)
throw new Error(`Cannot find model at path '${e}'`);
let o = JSON.parse(this.LS.getItem(t8.info));
return uk(t8), o;
}
};
var sc = "://";
var Zt = class {
constructor() {
this.managers = {};
}
static getInstance() {
return Zt.instance == null && (Zt.instance = new Zt()), Zt.instance;
}
static registerManager(e, t8) {
$(e != null, () => "scheme must not be undefined or null."), e.endsWith(sc) && (e = e.slice(0, e.indexOf(sc))), $(e.length > 0, () => "scheme must not be an empty string.");
let o = Zt.getInstance();
$(o.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), o.managers[e] = t8;
}
static getManager(e) {
let t8 = Zt.getInstance().managers[e];
if (t8 == null)
throw new Error(`Cannot find model manager for scheme '${e}'`);
return t8;
}
static getSchemes() {
return Object.keys(Zt.getInstance().managers);
}
};
function pd(r) {
if (r.indexOf(sc) === -1)
throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Zt.getSchemes().join(",")}`);
return { scheme: r.split(sc)[0], path: r.split(sc)[1] };
}
async function ck(r, e, t8 = 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 = pd(r).scheme, p = pd(r).path, u = i === pd(r).scheme, c = await n.load();
t8 && u && await Zt.getManager(i).removeModel(p);
let l = await a.save(c);
return t8 && !u && await Zt.getManager(i).removeModel(p), l.modelArtifactsInfo;
}
async function lk() {
let r = Zt.getSchemes(), e = {};
for (let t8 of r) {
let o = await Zt.getManager(t8).listModels();
for (let n in o) {
let s = t8 + sc + n;
e[s] = o[n];
}
}
return e;
}
async function mk(r) {
let e = pd(r);
return Zt.getManager(e.scheme).removeModel(e.path);
}
async function dk(r, e) {
return ck(r, e, false);
}
async function fk(r, e) {
return ck(r, e, true);
}
var kw = class {
constructor() {
this.messageName = "setTimeoutCustom", this.functionRefs = [], this.handledMessageCount = 0, this.hasEventListener = false;
}
fetch(e, t8) {
return fetch(e, t8);
}
now() {
return performance.now();
}
encode(e, t8) {
if (t8 !== "utf-8" && t8 !== "utf8")
throw new Error(`Browser's encoder only supports utf-8, but got ${t8}`);
return this.textEncoder == null && (this.textEncoder = new TextEncoder()), this.textEncoder.encode(e);
}
decode(e, t8) {
return new TextDecoder(t8).decode(e);
}
setTimeoutCustom(e, t8) {
if (typeof window == "undefined" || !A().getBool("USE_SETTIMEOUTCUSTOM")) {
setTimeout(e, t8);
return;
}
this.functionRefs.push(e), setTimeout(() => {
window.postMessage({ name: this.messageName, index: this.functionRefs.length - 1 }, "*");
}, t8), 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 Xm(e);
}
};
if (A().get("IS_BROWSER")) {
A().setPlatform("browser", new kw());
try {
Zt.registerManager(Ia.URL_SCHEME, new ud());
} catch (r) {
}
try {
Zt.registerManager(Sa.URL_SCHEME, new id());
} catch (r) {
}
}
var w4 = { importFetch: () => hk() };
var Nw;
var Tw = class {
constructor() {
this.util = gk(), this.textEncoder = new this.util.TextEncoder();
}
fetch(e, t8) {
return A().global.fetch != null ? A().global.fetch(e, t8) : (Nw == null && (Nw = w4.importFetch()), Nw(e, t8));
}
now() {
let e = process.hrtime();
return e[0] * 1e3 + e[1] / 1e6;
}
encode(e, t8) {
if (t8 !== "utf-8" && t8 !== "utf8")
throw new Error(`Node built-in encoder only supports utf-8, but got ${t8}`);
return this.textEncoder.encode(e);
}
decode(e, t8) {
return e.length === 0 ? "" : new this.util.TextDecoder(t8).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);
}
};
A().get("IS_NODE") && !A().get("IS_BROWSER") && A().setPlatform("node", new Tw());
function me(r, e = "float32", t8) {
return e = e || "float32", wt(r), new tt(r, e, t8);
}
function S4(r, e) {
let t8 = v(r, "x", "cast");
if (!XC(e))
throw new Error(`Failed to cast to unknown dtype ${e}`);
if (e === "string" && t8.dtype !== "string" || e !== "string" && t8.dtype === "string")
throw new Error("Only strings can be casted to strings");
let o = { x: t8 }, n = { dtype: e };
return T.runKernel(yo, o, n);
}
var qe = N({ cast_: S4 });
function I4(r) {
let t8 = { x: v(r, "x", "clone", "string_or_numeric") };
return T.runKernel(Co, t8);
}
var Ur = N({ clone_: I4 });
function cd(r, e = false) {
console.log(r.toString(e));
}
hw();
var v4 = { buffer: me, cast: qe, clone: Ur, print: cd };
U0(v4);
function ede() {
A().set("PROD", true);
}
function tde() {
A().set("DEBUG", true);
}
function rde() {
A().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled.");
}
function _w(r) {
A().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(r + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
}
G0(_w);
function ode() {
T.disposeVariables();
}
function ur() {
return T;
}
function nde() {
return T.memory();
}
function sde(r) {
return T.profile(r);
}
function De(r, e) {
return T.tidy(r, e);
}
function Ot(r) {
bl(r).forEach((t8) => t8.dispose());
}
function Rr(r) {
return T.keep(r);
}
function ade(r) {
return T.time(r);
}
function ide(r) {
return T.setBackend(r);
}
function ude() {
return T.ready();
}
function pde() {
return T.backendName;
}
function cde(r) {
T.removeBackend(r);
}
function lde(r) {
return T.findBackend(r);
}
function mde(r) {
return T.findBackendFactory(r);
}
function nu(r, e, t8 = 1) {
return T.registerBackend(r, e, t8);
}
function dde() {
return T.backend;
}
function fde(r, e) {
A().setPlatform(r, e);
}
function k4(r, e) {
let t8 = v(r, "a", "add"), o = v(e, "b", "add");
[t8, o] = Oe(t8, o);
let n = { a: t8, b: o };
return T.runKernel(io, n);
}
var Ce = N({ add_: k4 });
function N4(r, e) {
let t8 = v(r, "a", "floorDiv"), o = v(e, "b", "floorDiv");
[t8, o] = Oe(t8, o);
let n = { a: t8, b: o };
return T.runKernel(Sn, n);
}
var ld = N({ floorDiv_: N4 });
function T4(r, e) {
let t8 = v(r, "a", "div"), o = v(e, "b", "div");
if ([t8, o] = Oe(t8, o), t8.dtype === "int32" && o.dtype === "int32")
return ld(t8, o);
let n = { a: t8, b: o }, s = {};
return T.runKernel(fn, n, s);
}
var je = N({ div_: T4 });
function _4(r, e) {
let t8 = v(r, "a", "mul"), o = v(e, "b", "mul");
[t8, o] = Oe(t8, o);
let n = { a: t8, b: o };
return T.runKernel(Xn, n);
}
var se = N({ mul_: _4 });
function $4(r) {
let e = v(r, "x", "abs");
if (e.dtype === "complex64") {
let t8 = { x: e };
return T.runKernel(Di, t8);
} else {
let t8 = { x: e };
return T.runKernel(js, t8);
}
}
var Jt = N({ abs_: $4 });
function E4(r) {
let t8 = { x: v(r, "x", "acos") };
return T.runKernel(Vo, t8);
}
var xk = N({ acos_: E4 });
function R4(r) {
let t8 = { x: v(r, "x", "acosh") };
return T.runKernel(Wo, t8);
}
var yk = N({ acosh_: R4 });
function D4(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")), t8 = e[0];
e.forEach((n) => {
if (n.dtype !== t8.dtype)
throw new Error("All tensors passed to tf.addN() must have the same dtype");
}), e.forEach((n) => {
if (!Cr(n.shape, t8.shape))
throw new Error("All tensors passed to tf.addN() must have the same shape");
});
let o = e;
return T.runKernel(Uo, o);
}
var bk = N({ addN_: D4 });
function A4(r, e = null, t8 = false) {
let n = { x: v(r, "x", "all", "bool") }, s = { axis: e, keepDims: t8 };
return T.runKernel(Go, n, s);
}
var Ck = N({ all_: A4 });
function F4(r, e = null, t8 = false) {
let n = { x: v(r, "x", "any", "bool") }, s = { axis: e, keepDims: t8 };
return T.runKernel(Ho, n, s);
}
var wk = N({ any_: F4 });
function P4(r, e = 0) {
let o = { x: v(r, "x", "argMax") }, n = { axis: e };
return T.runKernel(Xs, o, n);
}
var Sk = N({ argMax_: P4 });
function O4(r, e = 0) {
let o = { x: v(r, "x", "argMin") }, n = { axis: e };
return T.runKernel(Ys, o, n);
}
var Ik = N({ argMin_: O4 });
function M4(r) {
let t8 = { x: v(r, "x", "asin") };
return T.runKernel(Ko, t8);
}
var vk = N({ asin_: M4 });
function L4(r) {
let t8 = { x: v(r, "x", "asinh") };
return T.runKernel(qo, t8);
}
var kk = N({ asinh_: L4 });
function B4(r) {
let t8 = { x: v(r, "x", "atan") };
return T.runKernel(jo, t8);
}
var Nk = N({ atan_: B4 });
function z4(r, e) {
let t8 = v(r, "a", "atan2"), o = v(e, "b", "atan2");
[t8, o] = Oe(t8, o);
let n = { a: t8, b: o };
return T.runKernel(Yo, n);
}
var Tk = N({ atan2_: z4 });
function V4(r) {
let t8 = { x: v(r, "x", "atanh") };
return T.runKernel(Xo, t8);
}
var _k = N({ atanh_: V4 });
function W4(r, e, t8, o, n = "NHWC", s) {
let a = r[3], i = [...e, a], p = Ek(n);
return Vu(r, i, t8, s, o, null, null, p);
}
function Ew(r, e, t8, 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 Vu(r, u, t8, o, n, s, false, a);
}
function U4(r, e, t8, o, n, s, a = "NDHWC") {
let [i, p, u] = $w(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 $k(r, c, t8, o, n, false, l, s);
}
function Vu(r, e, t8, o, n, s, a = false, i = "channelsLast") {
let [p, u, c, l] = [-1, -1, -1, -1];
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[p, u, c, l] = r;
else if (i === "channelsFirst")
[p, l, u, c] = r;
else
throw new Error(`Unknown dataFormat ${i}`);
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}
function $k(r, e, t8, 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, C] = $w(t8), [S, k, _] = $w(o), E = ac(d, S), R = ac(f, k), D = ac(h, _), { padInfo: P, outDepth: O, outHeight: M, outWidth: L } = q4(n, u, c, l, x, b, C, 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: P, strideDepth: x, strideHeight: b, strideWidth: C, filterDepth: d, filterHeight: f, filterWidth: h, effectiveFilterDepth: E, effectiveFilterHeight: R, effectiveFilterWidth: D, dilationDepth: S, dilationHeight: k, dilationWidth: _, inShape: r, outShape: z, filterShape: e };
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function G4(r, e, t8, o, n) {
o == null && (o = Rw(r, e, t8));
let s = r[0], a = r[1], i = Sl((s - e + 2 * o) / t8 + 1, n), p = Sl((a - e + 2 * o) / t8 + 1, n);
return [i, p];
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function H4(r, e, t8, o, n, s) {
n == null && (n = Rw(r, e[0], o[0]));
let a = [0, 0, 0, t8];
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r[i] + 2 * n >= e[i] && (a[i] = Sl((r[i] - e[i] + 2 * n) / o[i] + 1, s));
return a;
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function Rw(r, e, t8, o = 1) {
let n = ac(e, o);
return Math.floor((r[0] * (t8 - 1) - t8 + n) / 2);
}
function wl(r) {
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}
function $w(r) {
return typeof r == "number" ? [r, r, r] : r;
}
function ac(r, e) {
return e <= 1 ? r : r + (r - 1) * (e - 1);
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function K4(r, e, t8, o, n, s, a, i, p) {
let u, c, l;
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c = d[0], l = d[1];
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let m = Math.max(0, (c - 1) * o + s - e), d = Math.max(0, (l - 1) * n + a - t8), 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" };
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u = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((e - s + 1) / o), l = Math.ceil((t8 - a + 1) / n);
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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];
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let l, m, d, f;
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m = Math.ceil(e / n), d = Math.ceil(t8 / s), f = Math.ceil(o / a);
let h = (m - 1) * n + i - e, g = (d - 1) * s + p - t8, x = (f - 1) * a + u - o, b = Math.floor(h / 2), C = 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: C, type: "SAME" };
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throw Error(`Unknown padding parameter: ${r}`);
return { padInfo: l, outDepth: m, outHeight: d, outWidth: f };
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function Sl(r, e) {
if (!e)
return Math.trunc(r);
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return Math.round(r);
case "ceil":
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return Math.floor(r);
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throw new Error(`Unknown roundingMode ${e}`);
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function zu(r) {
let [e, t8, o] = wl(r);
return e === 1 && t8 === 1 && o === 1;
}
function gr(r, e) {
return zu(r) || zu(e);
}
function va(r) {
return wl(r).every((e) => e > 0);
}
function Ek(r) {
if (r === "NHWC")
return "channelsLast";
if (r === "NCHW")
return "channelsFirst";
throw new Error(`Unknown dataFormat ${r}`);
}
function Lt(r, e, t8) {
if (t8 != null) {
if (typeof e == "string")
throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${t8} but got pad ${e}.`);
if (typeof e == "number")
$(Ga(e), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t8} but got pad ${e}.`);
else if (typeof e == "object")
e.forEach((o) => {
o.forEach((n) => {
$(Ga(n), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t8} but got pad ${n}.`);
});
});
else
throw Error(`Error in ${r}: Unknown padding parameter: ${e}`);
}
}
function j4(r, e) {
let o = { x: v(r, "x", "reshape", "string_or_numeric") }, n = { shape: e };
return T.runKernel(ma, o, n);
}
var W = N({ reshape_: j4 });
function X4(r, e, t8, o, n) {
let s = v(r, "x", "avgPool", "float32"), a = 1;
$(gr(t8, a), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${t8} and dilations '${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: t8, pad: o, dimRoundingMode: n }, l = T.runKernel(Qo, u, c);
return l = qe(l, s.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var md = N({ avgPool_: X4 });
function Y4(r, e, t8, 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}`), $(typeof t8 == "number" && t8 > 0 || Array.isArray(t8) && t8[0] > 0 && t8[1] > 0 && t8[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${t8}'`), Lt("avgPool3d", o, n);
let u = { x: i }, c = { filterSize: e, strides: t8, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(Qs, u, c);
return l = qe(l, i.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l;
}
var Rk = N({ avgPool3d_: Y4 });
function Q4(r, e = 0) {
$(r.length >= 1, () => "Pass at least one tensor to concat");
let t8 = ri(r, "tensors", "concat", "string_or_numeric");
if (t8[0].dtype === "complex64" && t8.forEach((s) => {
if (s.dtype !== "complex64")
throw new Error(`Cannot concatenate complex64 tensors with a tensor
with dtype ${s.dtype}. `);
}), t8.length === 1)
return Ur(t8[0]);
let o = t8, n = { axis: e };
return T.runKernel(ea, o, n);
}
var bt = N({ concat_: Q4 });
function Z4(r, e, t8 = false, o = false) {
let n = v(r, "a", "matMul"), s = v(e, "b", "matMul");
[n, s] = Oe(n, s);
let a = { a: n, b: s }, i = { transposeA: t8, transposeB: o };
return T.runKernel(Zo, a, i);
}
var Ze = N({ matMul_: Z4 });
function J4(r) {
let t8 = { x: v(r, "x", "sigmoid", "float32") };
return T.runKernel(bs, t8);
}
var Na = N({ sigmoid_: J4 });
function eH(r, e, t8) {
let o = v(r, "x", "slice", "string_or_numeric");
if (o.rank === 0)
throw new Error("Slicing scalar is not possible");
let n = { x: o }, s = { begin: e, size: t8 };
return T.runKernel(fa, n, s);
}
var Xe = N({ slice_: eH });
function tH(r) {
let t8 = { x: v(r, "x", "tanh", "float32") };
return T.runKernel($s, t8);
}
var Il = N({ tanh_: tH });
function rH(r, e, t8, o, n, s) {
let a = v(r, "forgetBias", "basicLSTMCell"), i = v(e, "lstmKernel", "basicLSTMCell"), p = v(t8, "lstmBias", "basicLSTMCell"), u = v(o, "data", "basicLSTMCell"), c = v(n, "c", "basicLSTMCell"), l = v(s, "h", "basicLSTMCell"), m = bt([u, l], 1), d = Ze(m, i), f = Ce(d, p), h = f.shape[0], g = f.shape[1] / 4, x = [h, g], b = Xe(f, [0, 0], x), C = Xe(f, [0, g], x), S = Xe(f, [0, g * 2], x), k = Xe(f, [0, g * 3], x), _ = Ce(se(Na(b), Il(C)), se(c, Na(Ce(a, S)))), E = se(Il(_), Na(k));
return [_, E];
}
var Dk = N({ basicLSTMCell_: rH });
function oH(r, e, t8) {
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}`), $(t8.length === e.length, () => `crops.length is ${t8.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: t8 };
return T.runKernel(Zs, s, a);
}
var dd = N({ batchToSpaceND_: oH });
function Ak(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 nH(r, e, t8, o, n, s) {
s == null && (s = 1e-3);
let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t8, "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: Ak(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(In, m, d);
return W(f, a.shape);
}
var su = N({ batchNorm_: nH });
function sH(r, e, t8, o, n, s) {
let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t8, "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}.`), su(a, i, p, c, u, s);
}
var Fk = N({ batchNorm2d_: sH });
function aH(r, e, t8, o, n, s) {
let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t8, "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}.`), su(a, i, p, c, u, s);
}
var Pk = N({ batchNorm3d_: aH });
function iH(r, e, t8, o, n, s) {
let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t8, "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}.`), su(a, i, p, c, u, s);
}
var Ok = N({ batchNorm4d_: iH });
function uH(r, e, t8) {
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}`), $(t8 >= 0, () => `size must be non-negative, but got ${t8}.`), $(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: t8 };
return T.runKernel(Jo, s, a);
}
var fd = N({ bincount_: uH });
function pH(r, e) {
let t8 = v(r, "x", "bitwiseAnd"), o = v(e, "y", "bitwiseAnd");
if (!Cr(t8.shape, o.shape))
throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${t8.shape}, y: ${o.shape}`);
if (t8.dtype !== "int32" || o.dtype !== "int32")
throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${t8.dtype} and type of y: ${o.dtype}`);
let n = { a: t8, b: o };
return T.runKernel(Ha, n);
}
var Mk = N({ bitwiseAnd_: pH });
function cH(r, e) {
let t8 = v(r, "s0", "broadcastArgs", "int32"), o = v(e, "s1", "broadcastArgs", "int32");
if (t8.rank !== 1)
throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t8.rank}`);
if (o.rank !== 1)
throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`);
let n = { s0: t8, s1: o };
return T.runKernel(Js, n);
}
var Lk = N({ broadcastArgs_: cH });
function lH(r, e) {
let t8 = v(r, "broadcastTo", "x"), o = t8.shape;
if (wt(e), e.length < t8.rank)
throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t8.rank}.`);
if (e.length > t8.rank) {
let u = t8.shape.slice();
for (; u.length < e.length; )
u.unshift(1);
t8 = W(t8, u);
}
let n = t8.shape, s = Array.from(e);
for (let u = e.length - 1; u >= 0; u--)
if (n[u] === e[u])
s[u] = 1;
else if (t8.shape[u] !== 1)
throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);
if (s.map((u, c) => u > 1 ? c : -1).filter((u) => u >= 0).length === 0)
return Ur(t8);
let i = { x: t8 }, p = { reps: s };
return T.runKernel(uo, i, p);
}
var au = N({ broadcastTo_: lH });
function mH(r) {
let t8 = { x: v(r, "x", "ceil", "float32") };
return T.runKernel(en, t8);
}
var Bk = N({ ceil_: mH });
function Ta(r, e, t8) {
wt(r), t8 = t8 || _i(e);
let o = { shape: r, value: e, dtype: t8 };
return T.runKernel(na, {}, o);
}
function dH(r, e, t8) {
let o = v(r, "x", "clipByValue");
if ($(e <= t8, () => `Error in clip: min (${e}) must be less than or equal to max (${t8}).`), e === t8)
return Ta(o.shape, e, o.dtype);
let n = { x: o }, s = { clipValueMin: e, clipValueMax: t8 };
return T.runKernel(bo, n, s);
}
var zk = N({ clipByValue_: dH });
function fH(r) {
return bt(r, 0);
}
var Vk = N({ concat1d_: fH });
function hH(r, e) {
return bt(r, e);
}
var Wk = N({ concat2d_: hH });
function gH(r, e) {
return bt(r, e);
}
var Uk = N({ concat3d_: gH });
function xH(r, e) {
return bt(r, e);
}
var Gk = N({ concat4d_: xH });
function yH(r, e, t8, 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(t8, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t8} and dilations '${s}'`), $(va(s), () => "Error in conv2D: Dilated rates should be larger than 0."), $(va(t8), () => "Error in conv2D: Strides should be larger than 0.");
let m = { x: u, filter: p }, d = { strides: t8, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(tn, m, d);
return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var iu = N({ conv2d_: yH });
function bH(r, e, t8, 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(t8, s), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${t8} and dilation '${s}'`), $(va(s), () => "Error in conv1D: Dilated rates should be larger than 0."), $(va(t8), () => "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 = iu(m, l, [1, t8], 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 Hk = N({ conv1d_: bH });
function CH(r, e, t8, 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}`), $(t8.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${t8.rank}`);
let c = s === "NHWC" ? i[3] : i[1], l = s === "NHWC" ? p.shape[3] : p.shape[1];
$(c === t8.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t8.shape[2]}.`), $(l === t8.shape[3], () => `Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t8.shape[3]}.`), Lt("conv2dDerInput", n, a);
let m = { dy: p, filter: t8 }, d = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, inputShape: i }, f = T.runKernel(rn, m, d);
return u ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var hd = N({ conv2DBackpropInput_: CH });
function wH(r, e, t8, o, n, s) {
let a = v(r, "x", "conv2dTranspose"), i = v(e, "filter", "conv2dTranspose");
return hd(t8, a, i, o, n, "NHWC", s);
}
var Kk = N({ conv2dTranspose_: wH });
function SH(r, e, t8, 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(t8, s), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${t8} and dilations '${s}'`), $(n === "NDHWC", () => `Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`), $(va(s), () => "Error in conv3D: Dilated rates should be larger than 0."), $(va(t8), () => "Error in conv3D: Strides should be larger than 0.");
let c = { x: p, filter: i }, l = { strides: t8, pad: o, dataFormat: n, dilations: s }, m = T.runKernel(on, c, l);
return u ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m;
}
var qk = N({ conv3d_: SH });
function IH(r, e, t8, 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}`), $(t8.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${t8.rank}`), $(p === t8.shape[3], () => `Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t8.shape[3]}.`), $(u === t8.shape[4], () => `Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t8.shape[4]}.`);
let c = { dy: a, filter: t8 }, l = { pad: n, strides: o, inputShape: s }, m = T.runKernel(nn, c, l);
return i ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m;
}
var jk = N({ conv3DBackpropInput_: IH });
function vH(r, e, t8, o, n) {
let s = v(r, "x", "conv3dTranspose"), a = v(e, "filter", "conv3dTranspose");
return jk(t8, s, a, o, n);
}
var Xk = N({ conv3dTranspose_: vH });
function kH(r) {
let t8 = { x: v(r, "x", "cos", "float32") };
return T.runKernel(sn, t8);
}
var Yk = N({ cos_: kH });
function NH(r) {
let t8 = { x: v(r, "x", "cosh", "float32") };
return T.runKernel(an, t8);
}
var Qk = N({ cosh_: NH });
function TH(r, e = 0, t8 = false, o = false) {
let s = { x: v(r, "x", "cumprod") }, a = { axis: e, exclusive: t8, reverse: o };
return T.runKernel(un, s, a);
}
var Zk = N({ cumprod_: TH });
function _H(r, e = 0, t8 = false, o = false) {
let s = { x: v(r, "x", "cumsum") }, a = { axis: e, exclusive: t8, reverse: o };
return T.runKernel(pn, s, a);
}
var Jk = N({ cumsum_: _H });
function $H(r, e, t8, 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}.`), $(t8 >= 0, () => `size must be non-negative, but got ${t8}.`), $(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: t8, binaryOutput: o };
return T.runKernel(ta, a, i);
}
var e2 = N({ denseBincount_: $H });
function EH(r, e, t8 = "NHWC") {
let o = v(r, "x", "depthToSpace", "float32"), n = t8 === "NHWC" ? o.shape[1] : o.shape[2], s = t8 === "NHWC" ? o.shape[2] : o.shape[3], a = t8 === "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: t8 };
return T.runKernel(ln, i, p);
}
var t2 = N({ depthToSpace_: EH });
function RH(r, e, t8, 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: t8, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(mn, m, d);
return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var ic = N({ depthwiseConv2d_: RH });
function DH(r) {
let t8 = { x: v(r, "x", "diag") };
return T.runKernel(ra, t8);
}
var r2 = N({ diag_: DH });
function AH(r, e, t8, 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: t8, pad: o, dilations: n }, m = T.runKernel(dn, c, l);
return u ? W(m, [m.shape[1], m.shape[2], m.shape[3]]) : m;
}
var o2 = N({ dilation2d_: AH });
var Ir = {};
Ke(Ir, { assertAndGetBroadcastShape: () => rt, getBroadcastDims: () => n2, getReductionAxes: () => gd });
function n2(r, e) {
let t8 = r.length, o = [];
for (let n = 0; n < t8; n++) {
let s = t8 - 1 - n, a = r[s] || 1;
(e[e.length - 1 - n] || 1) > 1 && a === 1 && o.unshift(s);
}
return o;
}
function gd(r, e) {
let t8 = [];
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) && t8.unshift(s);
}
return t8;
}
function rt(r, e) {
let t8 = Math.max(r.length, e.length), o = new Array(t8);
for (let n = 0; n < t8; 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[t8 - n - 1] = a;
else if (a === 1)
o[t8 - 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[t8 - n - 1] = s;
}
return o;
}
function FH(r, e) {
let t8 = v(r, "a", "equal", "string_or_numeric"), o = v(e, "b", "equal", "string_or_numeric");
[t8, o] = Oe(t8, o), rt(t8.shape, o.shape);
let n = { a: t8, b: o };
return T.runKernel(xn, n);
}
var xd = N({ equal_: FH });
function PH(r, e, t8) {
let o = v(e, "a", "where"), n = v(t8, "b", "where"), s = v(r, "condition", "where", "bool"), a = rt(rt(s.shape, o.shape), n.shape), i = au(s, a), p = au(o, a), u = au(n, a), c = { condition: i, t: p, e: u };
return T.runKernel(da, c);
}
var co = N({ where_: PH });
function OH(r) {
let t8 = { x: v(r, "x", "zerosLike") };
return T.runKernel(ba, t8);
}
var Ht = N({ zerosLike_: OH });
function MH(r, e) {
let t8 = v(r, "a", "div"), o = v(e, "b", "div");
[t8, o] = Oe(t8, o);
let n = je(t8, o), s = Ht(n), a = xd(o, s);
return co(a, s, n);
}
var s2 = N({ divNoNan_: MH });
function LH(r, e) {
let t8 = v(r, "t1", "dot"), o = v(e, "t2", "dot");
$((t8.rank === 1 || t8.rank === 2) && (o.rank === 1 || o.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${t8.rank} and ${o.rank}.`);
let n = t8.rank === 1 ? t8.size : t8.shape[1], s = o.rank === 1 ? o.size : o.shape[0];
if ($(n === s, () => `Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`), t8.rank === 1 && o.rank === 1) {
let a = W(t8, [1, -1]), i = W(o, [-1, 1]), p = Ze(a, i);
return W(p, []);
} else if (t8.rank === 1 && o.rank === 2) {
let a = W(t8, [1, -1]), i = W(o, [o.shape[0], o.shape[1]]), p = Ze(a, i);
return W(p, [p.size]);
} else if (t8.rank === 2 && o.rank === 1) {
let a = W(o, [-1, 1]), i = Ze(t8, a);
return W(i, [i.size]);
} else {
let a = W(o, [o.shape[0], o.shape[1]]);
return Ze(t8, a);
}
}
var a2 = N({ dot_: LH });
function BH(r, ...e) {
let t8 = e.map((n, s) => v(n, `tensors${s}`, "einsum")), o = { equation: r };
return T.runKernel(Li, t8, o);
}
var i2 = N({ einsum_: BH });
function zH(r) {
let t8 = { x: v(r, "x", "elu", "float32") };
return T.runKernel(hn, t8);
}
var yd = N({ elu_: zH });
function VH(r, e) {
let t8 = v(r, "x", "ensureShape", "string_or_numeric");
if (!HC(t8.shape, e))
throw new Error(`EnsureShape: Shape of tensor ${t8.shape} is not compatible with expected shape ${e}`);
return r;
}
var u2 = N({ ensureShape_: VH });
function WH(r) {
let e = v(r, "x", "erf");
$(e.dtype === "int32" || e.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), e.dtype === "int32" && (e = qe(e, "float32"));
let t8 = { x: e };
return T.runKernel(gn, t8);
}
var p2 = N({ erf_: WH });
function Dw(r, e) {
for (let t8 = 0; t8 < r.length; ++t8)
if (r[r.length - t8 - 1] !== e - 1 - t8)
return false;
return true;
}
function c2(r, e, t8) {
let o = r.length + e.length, n = [], s = 0, a = 0;
for (let i = 0; i < o; i++)
t8.indexOf(i) === -1 ? n.push(r[s++]) : n.push(e[a++]);
return n;
}
function UH(r, e) {
let t8 = [], o = r.length;
for (let s = 0; s < o; s++)
e.indexOf(s) === -1 && t8.push(r[s]);
let n = e.map((s) => r[s]);
return [t8, n];
}
function ni(r, e) {
let t8 = e.map((o) => 1);
return c2(r, t8, e);
}
function GH(r, e, t8) {
$(Dw(e, t8), () => `${r} supports only inner-most axes for now. Got axes ${e} and rank-${t8} input.`);
}
function HH(r, e) {
if (Dw(r, e))
return null;
let t8 = [];
for (let o = 0; o < e; ++o)
r.indexOf(o) === -1 && t8.push(o);
return r.forEach((o) => t8.push(o)), t8;
}
function KH(r) {
return r.map((e, t8) => [t8, e]).sort((e, t8) => e[1] - t8[1]).map((e) => e[0]);
}
function qH(r, e) {
let t8 = [];
for (let o = e - r; o < e; ++o)
t8.push(o);
return t8;
}
function XH(r, e = null, t8 = false) {
let n = { x: v(r, "x", "max") }, s = { reductionIndices: e, keepDims: t8 };
return T.runKernel(zn, n, s);
}
var _a = N({ max_: XH });
function YH(r, e = null, t8 = false) {
let n = { x: v(r, "x", "min") }, s = { axis: e, keepDims: t8 };
return T.runKernel(Gn, n, s);
}
var vl = N({ min_: YH });
function QH(r, e) {
let t8 = v(r, "base", "pow"), o = v(e, "exp", "pow");
[t8, o] = Oe(t8, o);
let n = { a: t8, b: o };
return T.runKernel(ts, n);
}
var si = N({ pow_: QH });
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 Sr(r, [], [], e);
}
function ZH(r) {
let t8 = { x: v(r, "x", "sqrt", "float32") };
return T.runKernel(ws, t8);
}
var Dr = N({ sqrt_: ZH });
function JH(r) {
let e = v(r, "x", "square"), t8 = {};
return T.runKernel("Square", { x: e }, t8);
}
var er = N({ square_: JH });
function eK(r, e = null, t8 = false) {
let o = v(r, "x", "sum");
o.dtype === "bool" && (o = qe(o, "int32"));
let n = { x: o }, s = { axis: e, keepDims: t8 };
return T.runKernel(Ss, n, s);
}
var ot = N({ sum_: eK });
function tK(r, e = "euclidean", t8 = null, o = false) {
r = v(r, "x", "norm");
let n = l2(r, e, t8), s = n.shape;
if (o) {
let a = Ti(t8, r.shape);
s = ni(n.shape, a);
}
return W(n, s);
}
function l2(r, e, t8 = null) {
if (r.rank === 0)
return Jt(r);
if (r.rank !== 1 && t8 === null)
return l2(W(r, [-1]), e, t8);
if (r.rank === 1 || typeof t8 == "number" || Array.isArray(t8) && t8.length === 1) {
if (e === 1)
return ot(Jt(r), t8);
if (e === 1 / 0)
return _a(Jt(r), t8);
if (e === -1 / 0)
return vl(Jt(r), t8);
if (e === "euclidean" || e === 2)
return Dr(ot(si(Jt(r), ke(2, "int32")), t8));
throw new Error(`Error in norm: invalid ord value: ${e}`);
}
if (Array.isArray(t8) && t8.length === 2) {
if (e === 1)
return _a(ot(Jt(r), t8[0]), t8[1] - 1);
if (e === 1 / 0)
return _a(ot(Jt(r), t8[1]), t8[0]);
if (e === -1 / 0)
return vl(ot(Jt(r), t8[1]), t8[0]);
if (e === "fro" || e === "euclidean")
return Dr(ot(er(r), t8));
throw new Error(`Error in norm: invalid ord value: ${e}`);
}
throw new Error(`Error in norm: invalid axis: ${t8}`);
}
var Wu = N({ norm_: tK });
function rK(r, e = null, t8 = false) {
return Wu(r, "euclidean", e, t8);
}
var m2 = N({ euclideanNorm_: rK });
function oK(r) {
let t8 = { x: v(r, "x", "exp") };
return T.runKernel(yn, t8);
}
var _o = N({ exp_: oK });
function nK(r, e = 0) {
let t8 = v(r, "x", "expandDims", "string_or_numeric");
$(e <= t8.rank, () => "Axis must be <= rank of the tensor");
let o = { input: t8 }, n = { dim: e };
return T.runKernel(oa, o, n);
}
var ai = N({ expandDims_: nK });
function sK(r) {
let t8 = { x: v(r, "x", "expm1") };
return T.runKernel(bn, t8);
}
var d2 = N({ expm1_: sK });
function aK(r, e) {
let t8 = v(r, "x", "tile", "string_or_numeric");
$(t8.rank === e.length, () => `Error in transpose: rank of input ${t8.rank} must match length of reps ${e}.`);
let o = { x: t8 }, n = { reps: e };
return T.runKernel(uo, o, n);
}
var uu = N({ tile_: aK });
function iK(r, e, t8, 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 (t8 == null)
return a;
if (t8.length === 1)
return uu(ai(a, 0), [t8[0], 1, 1]);
if (t8.length === 2)
return uu(ai(ai(a, 0), 0), [t8[0], t8[1], 1, 1]);
if (t8.length === 3)
return uu(ai(ai(ai(a, 0), 0), 0), [t8[0], t8[1], t8[2], 1, 1]);
throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${t8.length}D.`);
}
var bd = N({ eye_: iK });
function uK(r) {
let t8 = { x: v(r, "x", "floor", "float32") };
return T.runKernel(wn, t8);
}
var Cd = N({ floor_: uK });
function pK(r, e, t8 = 0, o = 0) {
let n = v(r, "x", "gather"), s = v(e, "indices", "gather", "int32"), a = { x: n, indices: s }, i = { axis: t8, batchDims: o };
return T.runKernel(sa, a, i);
}
var wd = N({ gather_: pK });
function cK(r, e) {
let t8 = v(r, "a", "greater", "string_or_numeric"), o = v(e, "b", "greater", "string_or_numeric");
[t8, o] = Oe(t8, o), rt(t8.shape, o.shape);
let n = { a: t8, b: o };
return T.runKernel(kn, n);
}
var Uu = N({ greater_: cK });
function lK(r, e) {
let t8 = v(r, "a", "greaterEqual", "string_or_numeric"), o = v(e, "b", "greaterEqual", "string_or_numeric");
[t8, o] = Oe(t8, o), rt(t8.shape, o.shape);
let n = { a: t8, b: o };
return T.runKernel(Nn, n);
}
var Sd = N({ greaterEqual_: lK });
function mK(r) {
let t8 = { input: v(r, "input", "imag") };
return T.runKernel(Vi, t8);
}
var pu = N({ imag_: mK });
function dK(r) {
let t8 = { x: v(r, "x", "isFinite") };
return T.runKernel(Tn, t8);
}
var f2 = N({ isFinite_: dK });
function fK(r) {
let t8 = { x: v(r, "x", "isInf") };
return T.runKernel(_n, t8);
}
var h2 = N({ isInf_: fK });
function hK(r) {
let t8 = { x: v(r, "x", "isNaN") };
return T.runKernel($n, t8);
}
var g2 = N({ isNaN_: hK });
function gK(r, e = 0.2) {
let o = { x: v(r, "x", "leakyRelu") }, n = { alpha: e };
return T.runKernel(En, o, n);
}
var Id = N({ leakyRelu_: gK });
function xK(r, e) {
let t8 = v(r, "a", "less", "string_or_numeric"), o = v(e, "b", "less", "string_or_numeric");
[t8, o] = Oe(t8, o), rt(t8.shape, o.shape);
let n = { a: t8, b: o };
return T.runKernel(Rn, n);
}
var kl = N({ less_: xK });
function yK(r, e) {
let t8 = v(r, "a", "lessEqual", "string_or_numeric"), o = v(e, "b", "lessEqual", "string_or_numeric");
[t8, o] = Oe(t8, o), rt(t8.shape, o.shape);
let n = { a: t8, b: o };
return T.runKernel(Dn, n);
}
var uc = N({ lessEqual_: yK });
function x2(r, e, t8) {
if (t8 <= 0)
throw new Error("The number of values should be positive.");
let o = { start: r, stop: e, num: t8 };
return T.runKernel(An, {}, o);
}
function bK(r, e = 5, t8 = 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}.`), $(Ga(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: t8, alpha: o, beta: n }, c = T.runKernel(Bn, p, u);
return i ? W(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
}
var y2 = N({ localResponseNormalization_: bK });
function CK(r) {
let t8 = { x: v(r, "x", "log", "float32") };
return T.runKernel(Fn, t8);
}
var ii = N({ log_: CK });
function wK(r) {
let t8 = { x: v(r, "x", "log1p") };
return T.runKernel(Pn, t8);
}
var vd = N({ log1p_: wK });
function SK(r) {
return $(Ks(r), () => "The f passed in grad(f) must be a function"), (e, t8) => {
let o = v(e, "x", "tf.grad", "string_or_numeric"), n = t8 != null ? v(t8, "dy", "tf.grad") : null;
return T.tidy(() => {
let { value: s, grads: a } = T.gradients(() => r(o), [o], n);
return n != null && yt(s.shape, n.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), kd(a), a[0];
});
};
}
function IK(r) {
return $(Ks(r), () => "The f passed in grads(f) must be a function"), (e, t8) => {
$(Array.isArray(e), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
let o = ri(e, "args", "tf.grads", "string_or_numeric"), n = t8 != null ? v(t8, "dy", "tf.grads") : null;
return T.tidy(() => {
let { value: s, grads: a } = T.gradients(() => r(...o), o, n);
return n != null && yt(s.shape, n.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), kd(a), a;
});
};
}
function vK(r) {
return $(Ks(r), () => "The f passed in valueAndGrad(f) must be a function"), (e, t8) => {
$(e instanceof ut, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), $(t8 == null || t8 instanceof ut, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
let { grads: o, value: n } = T.gradients(() => r(e), [e], t8);
return kd(o), { grad: o[0], value: n };
};
}
function kK(r) {
return $(Ks(r), () => "The f passed in valueAndGrads(f) must be a function"), (e, t8) => {
$(Array.isArray(e) && e.every((n) => n instanceof ut), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), $(t8 == null || t8 instanceof ut, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
let o = T.gradients(() => r(...e), e, t8);
return t8 != null && yt(o.value.shape, t8.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), kd(o.grads), o;
};
}
function Aw(r, e) {
$(Ks(r), () => "The f passed in variableGrads(f) must be a function"), $(e == null || Array.isArray(e) && e.every((u) => u instanceof ei), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
let t8 = e != null;
if (!t8) {
e = [];
for (let u in T.registeredVariables)
e.push(T.registeredVariables[u]);
}
let o = t8 ? 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 vr(r) {
return T.customGrad(r);
}
function kd(r) {
if (r.filter((t8) => t8 == 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 NK(r) {
let t8 = { x: v(r, "x", "neg") };
return T.runKernel(ua, t8);
}
var pr = N({ neg_: NK });
function TK(r) {
let t8 = { x: v(r, "x", "softplus") };
return T.runKernel(Cs, t8);
}
var Nd = N({ softplus_: TK });
function _K(r) {
let e = v(r, "x", "logSigmoid");
return vr((o) => ({ value: pr(Nd(pr(o))), gradFunc: (a) => se(a, Na(pr(o))) }))(e);
}
var b2 = N({ logSigmoid_: _K });
function $K(r, e) {
let t8 = v(r, "a", "sub"), o = v(e, "b", "sub");
[t8, o] = Oe(t8, o);
let n = { a: t8, b: o };
return T.runKernel(Ts, n);
}
var Te = N({ sub_: $K });
function EK(r, e = -1) {
let t8 = v(r, "logits", "logSoftmax");
if (e === -1 && (e = t8.rank - 1), e !== t8.rank - 1)
throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${t8.rank} and axis was ${e}`);
return vr((n, s) => {
let i = _a(n, e, true), p = Te(n, i), u = Te(qe(p, "float32"), ii(ot(_o(p), e, true)));
return s([u]), { value: u, gradFunc: (l, m) => {
let [d] = m, f = true, h = _o(d);
return Te(l, se(ot(l, e, f), h));
} };
})(t8);
}
var C2 = N({ logSoftmax_: EK });
function RK(r, e = null, t8 = false) {
let o = v(r, "x", "logSumExp"), n = Ti(e, o.shape), s = _a(o, n, true), a = Te(o, s), i = _o(a), p = ot(i, n), u = ii(p), c = Ce(W(s, u.shape), u);
if (t8) {
let l = ni(c.shape, n);
return W(c, l);
}
return c;
}
var Td = N({ logSumExp_: RK });
function DK(r, e) {
let t8 = v(r, "a", "logicalAnd", "bool"), o = v(e, "b", "logicalAnd", "bool");
rt(t8.shape, o.shape);
let n = { a: t8, b: o };
return T.runKernel(On, n);
}
var Gu = N({ logicalAnd_: DK });
function AK(r) {
let t8 = { x: v(r, "x", "logicalNot", "bool") };
return T.runKernel(Mn, t8);
}
var _d = N({ logicalNot_: AK });
function FK(r, e) {
let t8 = v(r, "a", "logicalOr", "bool"), o = v(e, "b", "logicalOr", "bool");
rt(t8.shape, o.shape);
let n = { a: t8, b: o };
return T.runKernel(Ln, n);
}
var $d = N({ logicalOr_: FK });
function PK(r, e) {
let t8 = v(r, "a", "logicalXor", "bool"), o = v(e, "b", "logicalXor", "bool");
return rt(t8.shape, o.shape), Gu($d(r, e), _d(Gu(r, e)));
}
var w2 = N({ logicalXor_: PK });
var Ed = 2147483648;
function OK(r, e, t8 = "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 (Ge(p.shape) >= Ed)
throw new Error(`values tensor size must less than ${Ed}`);
if (i.shape[1] >= Ed)
throw new Error(`trailing dim_size must less than ${Ed} for int32 output type, was ${i.shape[1]}`);
let u = { sortedSequence: i, values: p }, c = { side: t8 };
return T.runKernel(fs, u, c);
}
var Nl = N({ searchSorted_: OK });
function S2(r, e) {
return Nl(r, e, "left");
}
function MK(r, e, t8, 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(t8, a), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${t8} and dilations '${a}'`), Lt("maxPool", o, n);
let u = { x: i }, c = { filterSize: e, strides: t8, pad: o, dimRoundingMode: n }, l = T.runKernel(Wn, u, c);
return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var Rd = N({ maxPool_: MK });
function LK(r, e = [1, 1, 1], t8, 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: t8, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(aa, u, c);
return p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l;
}
var I2 = N({ maxPool3d_: LK });
function BK(r, e, t8, o, n = false) {
let a = { x: v(r, "x", "maxPoolWithArgmax") }, i = { filterSize: e, strides: t8, pad: o, includeBatchInIndex: n }, p = T.runKernel(ia, a, i);
return { result: p[0], indexes: p[1] };
}
var v2 = N({ maxPoolWithArgmax_: BK });
function zK(r, e) {
let t8 = v(r, "a", "maximum"), o = v(e, "b", "maximum");
[t8, o] = Oe(t8, o), t8.dtype === "bool" && (t8 = qe(t8, "int32"), o = qe(o, "int32")), rt(t8.shape, o.shape);
let n = { a: t8, b: o };
return T.runKernel(Vn, n);
}
var Dd = N({ maximum_: zK });
function VK(r, e = null, t8 = false) {
let n = { x: v(r, "x", "mean") }, s = { axis: e, keepDims: t8 };
return T.runKernel(Un, n, s);
}
var Hu = N({ mean_: VK });
function Gr(r, e = "float32") {
if (wt(r), e === "complex64") {
let o = Gr(r, "float32"), n = Gr(r, "float32");
return Er(o, n);
}
let t8 = qp(Ge(r), e);
return T.makeTensor(t8, r, e);
}
function $a(r, e = "float32") {
if (wt(r), e === "complex64") {
let o = $a(r, "float32"), n = Gr(r, "float32");
return Er(o, n);
}
let t8 = cl(Ge(r), e);
return T.makeTensor(t8, r, e);
}
function k2(r, e, { indexing: t8 = "xy" } = {}) {
if (t8 !== "xy" && t8 !== "ij")
throw new TypeError(`${t8} is not a valid third argument to meshgrid`);
if (r === void 0)
return [];
let o = v(r, "x", "meshgrid", r instanceof ut ? r.dtype : "float32");
if (e === void 0)
return [o];
let n = v(e, "y", "meshgrid", e instanceof ut ? e.dtype : "float32"), s = Ge(o.shape), a = Ge(n.shape);
return t8 === "xy" ? (o = W(o, [1, -1]), n = W(n, [-1, 1]), [Ze($a([a, 1], o.dtype), o), Ze(n, $a([1, s], n.dtype))]) : (o = W(o, [-1, 1]), n = W(n, [1, -1]), [Ze(o, $a([1, a], o.dtype)), Ze($a([s, 1], n.dtype), n)]);
}
function WK(r, e) {
let t8 = v(r, "a", "minimum"), o = v(e, "b", "minimum");
[t8, o] = Oe(t8, o), t8.dtype === "bool" && (t8 = qe(t8, "int32"), o = qe(o, "int32")), rt(t8.shape, o.shape);
let n = { a: t8, b: o };
return T.runKernel(Hn, n);
}
var Ku = N({ minimum_: WK });
function UK(r, e, t8) {
$(t8 === "reflect" || t8 === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${t8}.`);
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 = t8 === "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: t8 }, a = { x: o };
return T.runKernel(Kn, a, s);
}
var N2 = N({ mirrorPad_: UK });
function GK(r, e) {
let t8 = v(r, "a", "mod"), o = v(e, "b", "mod");
[t8, o] = Oe(t8, o);
let n = { a: t8, b: o };
return T.runKernel(qn, n);
}
var T2 = N({ mod_: GK });
function HK(r, e = null, t8 = false) {
r = v(r, "x", "moments");
let o = Ti(e, r.shape), n = Hu(r, o, t8), s = n.shape;
t8 || (s = ni(n.shape, o));
let a = er(Te(qe(r, "float32"), W(n, s))), i = Hu(a, o, t8);
return { mean: n, variance: i };
}
var _2 = N({ moments_: HK });
function KK(r, e, t8, o) {
let n = v(e, "data", "multiRNNCell"), s = ri(t8, "c", "multiRNNCell"), a = ri(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 $2 = N({ multiRNNCell_: KK });
function qK(r, e, t8, 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}`);
t8 = t8 || Math.random();
let p = { logits: a === 1 ? W(n, [1, -1]) : n }, u = { numSamples: e, seed: t8, normalized: o }, c = T.runKernel(jn, p, u);
return a === 1 ? W(c, [c.size]) : c;
}
var E2 = N({ multinomial_: qK });
function jK(r, e) {
let t8 = v(r, "a", "notEqual", "string_or_numeric"), o = v(e, "b", "notEqual", "string_or_numeric");
[t8, o] = Oe(t8, o), rt(t8.shape, o.shape);
let n = { a: t8, b: o };
return T.runKernel(Yn, n);
}
var Ad = N({ notEqual_: jK });
function XK(r, e, t8 = 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: t8, offValue: o };
return T.runKernel(Jn, a, i);
}
var Tl = N({ oneHot_: XK });
function YK(r) {
let t8 = { x: v(r, "x", "onesLike") };
return T.runKernel(pa, t8);
}
var R2 = N({ onesLike_: YK });
function QK(r, e) {
let t8 = v(r, "v1", "outerProduct"), o = v(e, "v2", "outerProduct");
$(t8.rank === 1 && o.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${t8.rank} and ${o.rank}.`);
let n = W(t8, [-1, 1]), s = W(o, [1, -1]);
return Ze(n, s);
}
var D2 = N({ outerProduct_: QK });
function ZK(r, e, t8 = 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: t8 }, s = { x: o };
return T.runKernel(es, s, n);
}
var Ea = N({ pad_: ZK });
function JK(r, e, t8 = 0) {
return $(e.length === 2, () => "Invalid number of paddings. Must be length of 2."), Ea(r, [e], t8);
}
var A2 = N({ pad1d_: JK });
function eq(r, e, t8 = 0) {
return $(e.length === 2 && e[0].length === 2 && e[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Ea(r, e, t8);
}
var F2 = N({ pad2d_: eq });
function tq(r, e, t8 = 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."), Ea(r, e, t8);
}
var P2 = N({ pad3d_: tq });
function rq(r, e, t8 = 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."), Ea(r, e, t8);
}
var O2 = N({ pad4d_: rq });
function oq(r, e, t8) {
let o = v(r, "x", "spaceToBatchND");
$(o.rank >= 1 + e.length, () => `input rank ${o.rank} should be > than [blockShape] ${e.length}`), $(t8.length === e.length, () => `paddings.shape[0] ${t8.length} must be equal to [blockShape] ${e.length}`), $(o.shape.reduce((a, i, p) => p > 0 && p <= e.length ? a && (i + t8[p - 1][0] + t8[p - 1][1]) % e[p - 1] === 0 : a, true), () => `input spatial dimensions ${o.shape.slice(1)} with paddings ${t8.toString()} must be divisible by blockShapes ${e.toString()}`);
let n = { x: o }, s = { blockShape: e, paddings: t8 };
return T.runKernel(ha, n, s);
}
var Fd = N({ spaceToBatchND_: oq });
function nq(r, e, t8, 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 = Ew(p.shape, e, s, n, o), l = [c.dilationHeight, c.dilationWidth], m;
o === "same" ? m = aq([c.filterHeight, c.filterWidth], l) : m = [[0, 0], [0, 0]];
let d = l[0] === 1 && l[1] === 1, [f, h] = sq([c.inHeight, c.inWidth], l, m), g = d ? o : "valid", x = d ? p : Fd(p, l, f), C = (t8 === "avg" ? () => md(x, e, s, g, a) : () => Rd(x, e, s, g, a))(), S = d ? C : dd(C, l, h);
return u ? W(S, [S.shape[1], S.shape[2], S.shape[3]]) : S;
}
function sq(r, e, t8) {
let o = t8.map((c) => c[0]), n = t8.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 aq(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 M2 = N({ pool_: nq });
function iq(r, e) {
let t8 = v(r, "x", "prelu"), o = v(e, "alpha", "prelu"), n = { x: t8, alpha: o };
return T.runKernel(rs, n);
}
var Pd = N({ prelu_: iq });
function uq(r, e = null, t8 = false) {
let o = v(r, "x", "prod");
o.dtype === "bool" && (o = qe(o, "int32"));
let n = { x: o }, s = { axis: e, keepDims: t8 };
return T.runKernel(os, n, s);
}
var L2 = N({ prod_: uq });
function pq(r, e, t8, o) {
let n = r.map((c, l) => v(c, `tensors${l}`, "raggedGather", "int32")), s = v(e, "paramsDenseValues", "raggedGather"), a = v(t8, "indices", "raggedGather", "int32"), i = { paramsNestedSplits: n, paramsDenseValues: s, indices: a }, p = { outputRaggedRank: o }, u = T.runKernel(jp, i, p);
return { outputNestedSplits: u.slice(0, u.length - 1), outputDenseValues: u[u.length - 1] };
}
var B2 = N({ raggedGather_: pq });
function cq(r, e, t8) {
let o = v(r, "starts", "raggedRange"), n = v(e, "limits", "raggedRange", o.dtype), s = v(t8, "deltas", "raggedRange", o.dtype), a = { starts: o, limits: n, deltas: s }, i = T.runKernel(Xp, a);
return { rtNestedSplits: i[0], rtDenseValues: i[1] };
}
var z2 = N({ raggedRange_: cq });
function lq(r, e, t8, o, n) {
let s = v(r, "shape", "raggedTensorToTensor", "int32"), a = v(e, "values", "raggedTensorToTensor"), i = v(t8, "defaultValue", "raggedTensorToTensor", a.dtype), p = o.map((l, m) => v(l, `tensors${m}`, "raggedTensorToTensor", "int32")), u = { shape: s, values: a, defaultValue: i, rowPartitionTensors: p }, c = { rowPartitionTypes: n };
return T.runKernel(Yp, u, c);
}
var V2 = N({ raggedTensorToTensor_: lq });
function mq(r, e, t8) {
wt(r);
let o = Ge(r), n = null;
if (t8 == null || t8 === "float32")
n = new Float32Array(o);
else if (t8 === "int32")
n = new Int32Array(o);
else if (t8 === "bool")
n = new Uint8Array(o);
else
throw new Error(`Unknown data type ${t8}`);
for (let s = 0; s < o; s++)
n[s] = e();
return T.makeTensor(n, r, t8);
}
var W2 = N({ rand_: mq });
var zd = Up(zw());
var u1 = {};
Ke(u1, { TEST_EPSILON_FLOAT16: () => s1, createVideoElement: () => kq, encodeStrings: () => i1, expectArrayBuffersEqual: () => vq, expectArraysClose: () => Cq, expectArraysEqual: () => Sq, expectNumbersClose: () => a1, expectPromiseToFail: () => wq, expectValuesInRange: () => Iq, play: () => Nq, testEpsilon: () => Md });
var bq = 1e-3;
var s1 = 0.1;
function Cq(r, e, t8) {
return t8 == null && (t8 = Md()), Vw(r, e, (o, n) => Ww(o, n, t8));
}
function Md() {
return T.backend.floatPrecision() === 32 ? bq : s1;
}
function Vw(r, e, t8) {
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: ${i}`);
}
if (Array.isArray(r) && Array.isArray(e)) {
let a = ar(r), i = ar(e);
if (!Cr(a, i))
throw new Error(`Arrays have different shapes. Actual: [${a}]. Expected: [${i}]`);
}
let n = Pt(r) ? r : Fs(r), s = Pt(e) ? e : Fs(e);
if (n.length !== s.length)
throw new Error(`Arrays have different lengths actual: ${n.length} vs expected: ${s.length}.
Actual: ${n}.
Expected: ${s}.`);
for (let a = 0; a < s.length; ++a) {
let i = n[a], p = s[a];
if (!t8(i, p))
throw new Error(`Arrays differ: actual[${a}] = ${i}, expected[${a}] = ${p}.
Actual: ${n}.
Expected: ${s}.`);
}
typeof expect != "undefined" && expect().nothing();
}
function wq(r, e) {
r().then(() => e.fail(), () => e()), typeof expect != "undefined" && expect().nothing();
}
function Sq(r, e) {
let t8 = typeof e == "string" || typeof e == "number" || typeof e == "boolean" ? [e] : e;
return zo(r) || zo(r[0]) || zo(e) || zo(e[0]) ? Vw(r, t8, (o, n) => o == n) : Vw(r, e, (o, n) => Ww(o, n, 0));
}
function a1(r, e, t8) {
if (t8 == null && (t8 = Md()), !Ww(r, e, t8))
throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`);
typeof expect != "undefined" && expect().nothing();
}
function Ww(r, e, t8) {
return !isFinite(r) && !isFinite(e) ? true : !(isNaN(r) || isNaN(e) || Math.abs(r - e) > t8);
}
function Iq(r, e, t8) {
for (let o = 0; o < r.length; o++)
if (r[o] < e || r[o] > t8)
throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t8}`);
}
function vq(r, e) {
let t8 = new Float32Array(r), o = new Float32Array(e);
if (t8.length !== o.length)
throw new Error(`Expected ArrayBuffer to be of length ${o.length}, but it was ${t8.length}`);
for (let n = 0; n < o.length; n++)
if (t8[n] !== o[n])
throw new Error(`Expected ArrayBuffer value at ${n} to be ${o[n]} but got ${t8[n]} instead`);
}
function i1(r) {
for (let e = 0; e < r.length; e++) {
let t8 = r[e];
Array.isArray(t8) ? i1(t8) : r[e] = eu(t8);
}
return r;
}
function kq(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((t8) => {
e.addEventListener("loadeddata", (o) => t8(e)), e.load();
});
}
async function Nq(r) {
await r.play(), "requestVideoFrameCallback" in r && await new Promise((e) => {
r.requestVideoFrameCallback(e);
});
}
var ju = class {
constructor(e, t8, o, n, s) {
this.mean = e, this.stdDev = t8, 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 = zd.alea(a.toString());
}
nextValue() {
if (!isNaN(this.nextVal)) {
let n = this.nextVal;
return this.nextVal = NaN, n;
}
let e, t8, 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, t8 = this.mean + this.stdDev * s * i, (!this.truncated || this.isValidTruncated(e)) && (o = true);
}
return (!this.truncated || this.isValidTruncated(t8)) && (this.nextVal = this.convertValue(t8)), 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 Ld = class {
constructor(e, t8, o, n) {
this.alpha = e, this.beta = 1 / t8, this.dtype = o;
let s = n || Math.random();
this.randu = zd.alea(s.toString()), this.randn = new ju(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, t8, 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, t8 = 1 - 0.331 * e * e, o = 0.5 * e + this.d * (1 - a + Math.log(a)), s = this.randu(), s < t8 || 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 Bd = class {
constructor(e = 0, t8 = 1, o, n) {
if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t8 - 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} - ${t8} <= 1 and dtype is not float`);
this.random = zd.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, t8 = 1, o = "float32", n) {
if (wt(r), t8 == null && (t8 = 1), o == null && (o = "float32"), o !== "float32" && o !== "int32")
throw new Error(`Unsupported data type ${o}`);
let s = new Ld(e, t8, o, n), a = me(r, o);
for (let i = 0; i < a.values.length; i++)
a.values[i] = s.nextValue();
return a.toTensor();
}
var p1 = N({ randomGamma_: Tq });
function _q(r, e = 0, t8 = 1, o, n) {
if (wt(r), o != null && o === "bool")
throw new Error(`Unsupported data type ${o}`);
let s = new ju(e, t8, 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 Vd = N({ randomNormal_: _q });
function $q(r, e, t8) {
if (e != null && e === "bool")
throw new Error(`Unsupported data type ${e}`);
return Vd(r, 0, 1, e, t8);
}
var c1 = N({ randomStandardNormal_: $q });
function Eq(r, e = 0, t8 = 1, o = "float32", n) {
wt(r);
let s = me(r, o), a = new Bd(e, t8, null, n);
for (let i = 0; i < s.values.length; i++)
s.values[i] = a.nextValue();
return s.toTensor();
}
var pc = N({ randomUniform_: Eq });
function Rq(r, e, t8, o) {
return pc(r, e, t8, "int32", o);
}
var l1 = N({ randomUniformInt_: Rq });
function cu(r, e, t8 = 1, o = "float32") {
if (t8 === 0)
throw new Error("Cannot have a step of zero");
let n = { start: r, stop: e, step: t8, dtype: o };
return T.runKernel(la, {}, n);
}
function Dq(r) {
let t8 = { input: v(r, "input", "real") };
return T.runKernel(Gi, t8);
}
var ui = N({ real_: Dq });
function Aq(r) {
let t8 = { x: v(r, "x", "reciprocal") };
return T.runKernel(ns, t8);
}
var m1 = N({ reciprocal_: Aq });
function Fq(r) {
let t8 = { x: v(r, "x", "relu") };
return T.runKernel(ss, t8);
}
var lu = N({ relu_: Fq });
function Pq(r) {
let t8 = { x: v(r, "x", "relu6") };
return T.runKernel(us, t8);
}
var Wd = N({ relu6_: Pq });
function Oq(r, e) {
let o = { x: v(r, "x", "reverse") }, n = { dims: e };
return T.runKernel(ps, o, n);
}
var lo = N({ reverse_: Oq });
function Mq(r) {
let e = v(r, "x", "reverse");
return $(e.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${e.rank}.`), lo(e, 0);
}
var d1 = N({ reverse1d_: Mq });
function Lq(r, e) {
let t8 = v(r, "x", "reverse");
return $(t8.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${t8.rank}.`), lo(t8, e);
}
var f1 = N({ reverse2d_: Lq });
function Bq(r, e) {
let t8 = v(r, "x", "reverse");
return $(t8.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${t8.rank}.`), lo(t8, e);
}
var h1 = N({ reverse3d_: Bq });
function zq(r, e) {
let t8 = v(r, "x", "reverse");
return $(t8.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${t8.rank}.`), lo(t8, e);
}
var g1 = N({ reverse4d_: zq });
function Vq(r) {
let t8 = { x: v(r, "x", "round") };
return T.runKernel(cs, t8);
}
var Ud = N({ round_: Vq });
function Wq(r) {
let t8 = { x: v(r, "x", "rsqrt", "float32") };
return T.runKernel(ls, t8);
}
var x1 = N({ rsqrt_: Wq });
function Uq(r) {
let t8 = { x: v(r, "x", "selu") };
return T.runKernel(hs, t8);
}
var y1 = N({ selu_: Uq });
function Gq(r, e, t8, o, n, s = [1, 1], a = "NHWC") {
let i = v(r, "x", "separableConv2d"), p = v(e, "depthwiseFilter", "separableConv2d"), u = v(t8, "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 = ic(c, p, o, n, a, s), g = iu(f, u, 1, "valid", a);
return l ? W(g, [g.shape[1], g.shape[2], g.shape[3]]) : g;
}
var b1 = N({ separableConv2d_: Gq });
async function Hq(r, e) {
let t8 = v(r, "x", "setdiff1d"), o = v(e, "y", "setdiff1d");
$(t8.dtype === o.dtype, () => `x and y should have the same dtype, but got x (${t8.dtype}) and y (${o.dtype}).`), $(t8.rank === 1, () => `x should be 1D tensor, but got x (${t8.shape}).`), $(o.rank === 1, () => `y should be 1D tensor, but got y (${o.shape}).`);
let n = await t8.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], t8.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 = Hq;
function Kq(r) {
let t8 = { x: v(r, "x", "sign") };
return T.runKernel(ys, t8);
}
var w1 = N({ sign_: Kq });
function qq(r) {
let t8 = { x: v(r, "x", "sin", "float32") };
return T.runKernel(gs, t8);
}
var S1 = N({ sin_: qq });
function jq(r) {
let t8 = { x: v(r, "x", "sinh") };
return T.runKernel(xs, t8);
}
var I1 = N({ sinh_: jq });
function Xq(r, e, t8) {
let o = v(r, "x", "slice1d");
return $(o.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${o.rank} tensor`), Xe(o, [e], [t8]);
}
var v1 = N({ slice1d_: Xq });
function Yq(r, e, t8) {
let o = v(r, "x", "slice2d");
return $(o.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${o.rank} tensor`), Xe(o, e, t8);
}
var k1 = N({ slice2d_: Yq });
function Qq(r, e, t8) {
let o = v(r, "x", "slice3d");
return $(o.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${o.rank} tensor`), Xe(o, e, t8);
}
var N1 = N({ slice3d_: Qq });
function Zq(r, e, t8) {
let o = v(r, "x", "slice4d");
return $(o.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${o.rank} tensor`), Xe(o, e, t8);
}
var T1 = N({ slice4d_: Zq });
function Jq(r, e = -1) {
let t8 = v(r, "logits", "softmax", "float32");
if (e === -1 && (e = t8.rank - 1), e !== t8.rank - 1)
throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${t8.rank} and dim was ${e}`);
let o = { logits: t8 }, n = { dim: e };
return T.runKernel(Is, o, n);
}
var _1 = N({ softmax_: Jq });
function e6(r) {
$(r.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`);
let e = { input: r };
return T.runKernel(Bi, e);
}
var cc = N({ fft_: e6 });
function t6(r) {
$(r.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`);
let e = { input: r };
return T.runKernel(zi, e);
}
var Xu = N({ ifft_: t6 });
function r6(r) {
let e = r.shape[r.shape.length - 1], t8 = r.size / e, o;
if (e <= 2) {
let n = W(r, [t8, e]);
o = Xu(n);
} else {
let n = [t8, 2 * (e - 1)], s = W(ui(r), [t8, e]), a = W(pu(r), [t8, e]), i = lo(Xe(s, [0, 1], [t8, e - 2]), 1), p = se(lo(Xe(a, [0, 1], [t8, e - 2]), 1), ke(-1)), u = bt([s, i], 1), c = bt([a, p], 1), l = W(Er(u, c), [n[0], n[1]]);
o = Xu(l);
}
if (o = ui(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 Gd = N({ irfft_: r6 });
function o6(r, e, t8 = 0) {
let n = { x: v(r, "x", "split") }, s = { numOrSizeSplits: e, axis: t8 };
return T.runKernel(ga, n, s);
}
var pi = N({ split_: o6 });
function n6(r, e) {
$(r.dtype === "float32", () => `The dtype for rfft() must be real value but got ${r.dtype}`);
let t8 = r.shape[r.shape.length - 1], o = r.size / t8, n;
if (e != null && e < t8) {
let f = r.shape.map((g) => 0), h = r.shape.map((g) => g);
h[r.shape.length - 1] = e, n = Xe(r, f, h), t8 = e;
} else if (e != null && e > t8) {
let f = r.shape.map((h) => h);
f[r.shape.length - 1] = e - t8, n = bt([r, Gr(f)], r.shape.length - 1), t8 = e;
} else
n = r;
let s = Ht(n), a = W(Er(n, s), [o, t8]), i = cc(a), p = Math.floor(t8 / 2) + 1, u = ui(i), c = pu(i), l = pi(u, [p, t8 - p], u.shape.length - 1), m = pi(c, [p, t8 - p], c.shape.length - 1), d = n.shape.slice();
return d[n.shape.length - 1] = p, W(Er(l[0], m[0]), d);
}
var lc = N({ rfft_: n6 });
function s6(r, e) {
let t8 = v(r, "a", "squaredDifference"), o = v(e, "b", "squaredDifference");
[t8, o] = Oe(t8, o), rt(t8.shape, o.shape);
let n = { a: t8, b: o }, s = {};
return T.runKernel(ks, n, s);
}
var Hd = N({ squaredDifference_: s6 });
function a6(r, e) {
let t8 = v(r, "x", "squeeze", "string_or_numeric");
return W(t8, KC(t8.shape, e).newShape);
}
var mc = N({ squeeze_: a6 });
function i6(r, e = 0) {
let t8 = ri(r, "tensors", "stack", "string_or_numeric");
$(t8.length >= 1, () => "Pass at least one tensor to tf.stack"), t8.length > 0 && $(e <= t8[0].rank, () => "Axis must be <= rank of the tensor");
let o = t8, n = { axis: e };
return T.runKernel(ca, o, n);
}
var kr = N({ stack_: i6 });
function u6(r, e = 0) {
let o = { x: v(r, "x", "step") }, n = { alpha: e };
return T.runKernel(wo, o, n);
}
var Kd = N({ step_: u6 });
function p6(r, e, t8, 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: t8, strides: o, beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p };
return T.runKernel(Ns, c, l);
}
var $1 = N({ stridedSlice_: p6 });
function c6(r) {
let t8 = { x: v(r, "x", "tan", "float32") };
return T.runKernel(_s, t8);
}
var E1 = N({ tan_: c6 });
function xr(r, e) {
ao(r);
let t8 = ar(r, e);
if (t8.length !== 1)
throw new Error("tensor1d() requires values to be a flat/TypedArray");
return Sr(r, null, t8, e);
}
function mu(r, e, t8) {
if (ao(r), e != null && e.length !== 2)
throw new Error("tensor2d() requires shape to have two numbers");
let o = ar(r, t8);
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 Sr(r, e, o, t8);
}
function qd(r, e, t8) {
if (ao(r), e != null && e.length !== 3)
throw new Error("tensor3d() requires shape to have three numbers");
let o = ar(r, t8);
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 Sr(r, e, o, t8);
}
function R1(r, e, t8) {
if (ao(r), e != null && e.length !== 4)
throw new Error("tensor4d() requires shape to have four numbers");
let o = ar(r, t8);
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 Sr(r, e, o, t8);
}
function D1(r, e, t8) {
if (ao(r), e != null && e.length !== 5)
throw new Error("tensor5d() requires shape to have five numbers");
let o = ar(r, t8);
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 Sr(r, e, o, t8);
}
function A1(r, e, t8) {
if (ao(r), e != null && e.length !== 6)
throw new Error("tensor6d() requires shape to have six numbers");
let o = ar(r, t8);
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, Sr(r, e, o, t8);
}
var du = {};
Ke(du, { calculateShapes: () => F1, validateInput: () => dc, validateUpdateShape: () => Uw });
function Uw(r, e, t8) {
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: ${t8.shape}, indices.shape: ${e.shape}, shape: ${r}, sliceDim: ${o}, and batchDim: ${n}.`;
if (t8.rank < n)
throw new Error(s + ` update.rank < ${n}. `);
if (r.length < o + (t8.rank - n))
throw new Error(s + ` Output shape length < ${o + (t8.rank - n)}`);
if (t8.rank !== n + r.length - o)
throw new Error(s + ` update.rank != ${n + r.length - o}`);
for (let a = 0; a < n; ++a)
if (t8.shape[a] !== e.shape[a])
throw new Error(s + ` updates.shape[${a}] (${t8.shape[a]}) != indices.shape[${a}] (${e.shape[a]}).`);
for (let a = 0; a < t8.rank - n; ++a)
if (t8.shape[a + n] !== r[a + o])
throw new Error(s + ` updates.shape[${a + n}] (${t8.shape[a + n]}) != shape[${a + n}] (${r[a + n]})`);
}
function dc(r, e, t8) {
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 (t8.length < 1)
throw new Error(`Output rank must be greater or equal to 1, but got shape: ${t8}`);
if (t8.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}`);
}
Uw(t8, e, r);
}
function F1(r, e, t8) {
let o = e.shape.length, n = o > 1 ? e.shape[o - 1] : 1, s = t8.length, a = 1;
for (let l = n; l < s; ++l)
a *= t8[l];
let i = n < 1 ? 1 : n, p = Ge(e.shape) / i, u = [...qs(t8.slice(0, n)), 1], c = Ge(t8);
return { sliceRank: n, numUpdates: p, sliceSize: a, strides: u, outputSize: c };
}
function l6(r, e, t8) {
let o = v(r, "tensor", "tensorScatterupdate"), n = v(e, "indices", "tensorScatterupdate", "int32"), s = v(t8, "updates", "tensorScatterupdate");
if (dc(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(ds, a, i);
}
var P1 = N({ tensorScatterUpdate_: l6 });
function m6(r, e = 1, t8 = 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: t8 }, [i, p] = T.runKernel(Es, s, a);
return { values: i, indices: p };
}
var O1 = N({ topk_: m6 });
function d6(r, e = 0, t8 = 1, o, n) {
if (wt(r), o != null && o === "bool")
throw new Error("Unsupported data type $ { dtype }");
let s = new ju(e, t8, 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 M1 = N({ truncatedNormal_: d6 });
function f6(r, e = 0) {
let t8 = v(r, "x", "unique", "string_or_numeric");
$(t8.rank > 0, () => "The input tensor must be at least 1D");
let o = { x: t8 }, n = { axis: e }, [s, a] = T.runKernel(Qi, o, n);
return { values: s, indices: a };
}
var L1 = N({ unique_: f6 });
function h6(r, e, t8) {
let o = v(r, "x", "unsortedSegmentSum"), n = v(e, "segmentIds", "unsortedSegmentSum", "int32");
$(Ga(t8), () => "numSegments must be of dtype int");
let s = { x: o, segmentIds: n }, a = { numSegments: t8 };
return T.runKernel(Zi, s, a);
}
var B1 = N({ unsortedSegmentSum_: h6 });
function g6(r, e = 0) {
let t8 = v(r, "x", "unstack", "string_or_numeric");
$(e >= -t8.shape.length && e < t8.shape.length, () => `Axis = ${e} is not in [-${t8.shape.length}, ${t8.shape.length})`);
let o = { value: t8 }, n = { axis: e };
return T.runKernel(ya, o, n);
}
var mo = N({ unstack_: g6 });
function z1(r, e) {
return Nl(r, e, "right");
}
function V1(r, e = true, t8, o) {
return T.makeVariable(r, e, t8, o);
}
function jd(r, e) {
let t8 = [];
for (let s = 0; s < e.length; s++)
e[s] && t8.push(s);
let o = me(r, "int32"), n = me([t8.length, r.length], "int32");
for (let s = 0; s < t8.length; s++) {
let a = o.indexToLoc(t8[s]), i = s * r.length;
n.values.set(a, i);
}
return n.toTensor();
}
async function x6(r) {
let e = v(r, "condition", "whereAsync", "bool"), t8 = await e.data(), o = jd(e.shape, t8);
return r !== e && e.dispose(), o;
}
var Xd = x6;
async function y6(r, e, t8) {
let o = v(r, "tensor", "boolMask"), n = v(e, "mask", "boolMask", "bool"), s = t8 == null ? 0 : t8, a = n.rank, i = o.shape;
$(a > 0, () => "mask cannot be scalar"), yt(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 Xd(l), d = mc(m, [1]), f = wd(c, d, s);
return r !== o && o.dispose(), e !== n && n.dispose(), d.dispose(), c.dispose(), l.dispose(), m.dispose(), f;
}
var b6 = y6;
function C6(r, e, t8) {
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 = ui(o), i = pu(o);
return a = T.runKernel(po, { x: a }, s), i = T.runKernel(po, { x: i }, s), t8 && (i = pr(i)), Er(a, i);
}) : T.runKernel(po, n, s);
}
var fc = N({ transpose_: C6 });
function w6(r, e, t8, o, n = true) {
let s = v(r, "v", "movingAverage"), a = v(e, "x", "movingAverage"), i = v(t8, "decay", "movingAverage");
dw(s, a), $(Cr(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 = je(c, Te(p, si(i, l)));
}
return Ce(s, c);
}
var S6 = N({ movingAverage_: w6 });
function I6(r, e, t8) {
wt(t8);
let o = v(r, "indices", "scatterND", "int32"), n = v(e, "updates", "scatterND");
dc(n, o, t8);
let s = { indices: o, updates: n }, a = { shape: t8 };
return T.runKernel(ms, s, a);
}
var v6 = N({ scatterND_: I6 });
function W1(r, e, t8, 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 (t8.length !== s)
throw new Error(`outputShape has incorrect number of elements:, ${t8.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 N6(r, e, t8, o = 0) {
wt(t8);
let n = v(r, "sparseIndices", "sparseToDense", "int32"), s = v(e, "sparseValues", "sparseToDense", "string_or_numeric"), a = v(o, "defaultValue", "sparseToDense", s.dtype);
W1(n, s, t8, a);
let i = { sparseIndices: n, sparseValues: s, defaultValue: a }, p = { outputShape: t8 };
return T.runKernel(vs, i, p);
}
var T6 = N({ sparseToDense_: N6 });
function _6(r, e) {
let t8 = v(e, "indices", "gatherND", "int32"), n = { params: v(r, "x", "gatherND", "string_or_numeric"), indices: t8 };
return T.runKernel(vn, n);
}
var $6 = N({ gatherND_: _6 });
function U1(r, e) {
if (e == null)
return r.shape.slice();
if (Cr(r.shape, e))
return e;
if (r.shape.length === e.length) {
let t8 = [];
for (let o = 0; o < r.shape.length; o++)
e[o] == null && r.shape[o] != null ? t8.push(r.shape[o]) : t8.push(e[o]);
return t8;
}
return e;
}
function E6(r, e, t8, o) {
let n = v(r, "x", "dropout");
if ($(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 ut ? n.clone() : n;
let s = U1(n, t8), a = 1 - e, i = je(Cd(Ce(pc(s, 0, 1, "float32", o), a)), a);
return se(n, i);
}
var R6 = N({ dropout_: E6 });
function Gw(r) {
return Math.floor(Math.pow(2, Math.ceil(Math.log(r) / Math.log(2))));
}
function _l(r, e, t8) {
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 - t8 * Math.cos(a);
}
return xr(n, "float32");
}
async function D6(r, e, t8 = 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}`), yt(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];
$(t8 > 0 && t8 <= s, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${t8}`);
let a = await o.data(), i = await n.data(), [p, u] = [a.length / s, s], c = qC("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 < t8; 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 A6 = D6;
var Hw = {};
Ke(Hw, { conv2d: () => H1, depthwiseConv2d: () => j1, matMul: () => X1 });
function F6(r, e, t8, 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}.`), $(t8.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${t8}.`);
let u = s === "NHWC" ? i.shape[3] : i.shape[1], c = s === "NHWC" ? p.shape[3] : p.shape[1];
$(u === t8[2], () => `Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${t8[2]}.`), $(c === t8[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${t8[3]}).`), Lt("conv2dDerFilter", n, a);
let l = { x: i, dy: p }, m = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, filterShape: t8 };
return T.runKernel(Ai, l, m);
}
var G1 = N({ conv2DBackpropFilter_: F6 });
function Yu(r, e, t8) {
if (t8 == null || t8 === "linear")
return r;
if (t8 === "relu")
return se(r, Kd(e));
throw new Error(`Cannot compute gradient for fused activation ${t8}.`);
}
function Qu(r, e) {
let t8 = e, o = gd(r.shape, e.shape);
return o.length > 0 && (t8 = ot(t8, o)), W(t8, r.shape);
}
function Zu(r, e, t8, o) {
if (e === "linear")
return r;
if (e === "relu")
return lu(r);
if (e === "elu")
return yd(r);
if (e === "relu6")
return Wd(r);
if (e === "prelu")
return Pd(r, t8);
if (e === "leakyrelu")
return Id(r, o);
if (e === "sigmoid")
return Na(r);
throw new Error(`Unknown fused activation ${e}.`);
}
var Ju = (r, e) => !(r > 0) || e === "linear";
function P6({ x: r, filter: e, strides: t8, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) {
if (p = p || "linear", Ju(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 _ = iu(r, e, t8, o, n, s, a);
return i != null && (_ = Ce(_, i)), Zu(_, 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(t8, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t8} and dilations '${s}'`);
let g = Vu(d.shape, m.shape, t8, 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 C = (_, E) => {
$(n === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${n} but only NHWC is currently supported.`);
let [R, D, P, O] = E, M = Yu(_, P, p);
$(zu(s), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
let L = hd(D.shape, M, R, t8, o), B = G1(D, M, R.shape, t8, 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: t8, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c };
return i == null ? vr((E, R, D) => {
let P = T.runKernel(Io, S, k);
return D([R, E, P]), f && (P = W(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: C };
})(d, m) : vr((E, R, D, P) => {
let O = T.runKernel(Io, S, k);
return P([R, E, O, D]), f && (O = W(O, [O.shape[1], O.shape[2], O.shape[3]])), { value: O, gradFunc: C };
})(d, m, x);
}
var H1 = N({ fusedConv2d_: P6 });
function O6(r, e, t8, 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: t8 };
return T.runKernel(Fi, u, c);
}
var K1 = N({ depthwiseConv2dNativeBackpropFilter_: O6 });
function M6(r, e, t8, 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: t8 }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, inputShape: r }, l = T.runKernel(Pi, u, c);
return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var q1 = N({ depthwiseConv2dNativeBackpropInput_: M6 });
function L6({ x: r, filter: e, strides: t8, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) {
if (Ju(T.state.gradientDepth, p) === false) {
let k = ic(r, e, t8, o, n, s, a);
return i != null && (k = Ce(k, i)), Zu(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(t8, s), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${t8} and dilations '${s}'`), Lt("fused depthwiseConv2d", o, a);
let h = Vu(d.shape, m.shape, t8, 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, _) => {
$(zu(s), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);
let [E, R, D, P] = _, O = Yu(k, D, p), M = q1(R.shape, O, E, t8, o, s, a), L = K1(R, O, E.shape, t8, o, s, a);
if (P != null) {
let B = Qu(g, O);
return [M, L, B];
}
return [M, L];
}, C = { x: d, filter: m, bias: g, preluActivationWeights: x }, S = { strides: t8, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c };
return i == null ? vr((_, E, R) => {
let D = T.runKernel(vo, C, S);
return R([E, _, D]), f && (D = W(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: b };
})(d, m) : vr((_, E, R, D) => {
let P = T.runKernel(vo, C, S);
return D([E, _, P, R]), f && (P = W(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: b };
})(d, m, g);
}
var j1 = N({ fusedDepthwiseConv2d_: L6 });
function B6({ a: r, b: e, transposeA: t8 = false, transposeB: o = false, bias: n, activation: s = "linear", preluActivationWeights: a, leakyreluAlpha: i = 0.2 }) {
if (Ju(T.state.gradientDepth, s) === false) {
let O = Ze(r, e, t8, o);
return n != null && (O = Ce(O, n)), Zu(O, s, a, i);
}
let p = v(r, "a", "fused matMul"), u = v(e, "b", "fused matMul");
[p, u] = Oe(p, u);
let c = t8 ? p.shape[p.rank - 2] : p.shape[p.rank - 1], l = o ? u.shape[u.rank - 1] : u.shape[u.rank - 2], m = t8 ? 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 = Ge(f), x = Ge(h);
$(c === l, () => `Error in fused matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${p.shape} and ${u.shape} and transposeA=${t8} and transposeB=${o} must match.`);
let C = rt(p.shape.slice(0, -2), u.shape.slice(0, -2)).concat([m, d]), S = t8 ? 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(C, _.shape));
let E;
a != null && (E = v(a, "prelu weights", "fused matMul"));
let R = (O, M) => {
let [L, B, z, U] = M, j = Yu(W(O, z.shape), z, s), q, Y;
if (!t8 && !o ? (q = Ze(j, B, false, true), Y = Ze(L, j, true, false)) : !t8 && o ? (q = Ze(j, B, false, false), Y = Ze(j, L, true, false)) : t8 && !o ? (q = Ze(B, j, false, true), Y = Ze(L, j, false, false)) : (q = Ze(B, j, true, true), Y = Ze(j, L, true, true)), n != null) {
let J = Qu(U, j);
return [q, Y, J];
} else
return [q, Y];
}, D = { a: S, b: k, bias: _, preluActivationWeights: E }, P = { transposeA: t8, transposeB: o, activation: s, leakyreluAlpha: i };
return n == null ? vr((M, L, B) => {
let z = T.runKernel(So, D, P);
return B([M, L, z]), { value: W(z, C), gradFunc: R };
})(S, k) : vr((M, L, B, z) => {
let U = T.runKernel(So, D, P);
return z([M, L, U, B]), { value: W(U, C), gradFunc: R };
})(S, k, _);
}
var X1 = N({ fusedMatMul_: B6 });
function z6(r) {
return _l(r, 0.54, 0.46);
}
var Y1 = N({ hammingWindow_: z6 });
function V6(r) {
return _l(r, 0.5, 0.5);
}
var Yd = N({ hannWindow_: V6 });
function W6(r, e, t8, o = false, n = 0) {
let s = 0, a = [];
for (; s + e <= r.size; )
a.push(Xe(r, s, e)), s += t8;
if (o)
for (; s < r.size; ) {
let i = s + e - r.size, p = bt([Xe(r, s, e - i), Ta([i], n)]);
a.push(p), s += t8;
}
return a.length === 0 ? mu([], [0, e]) : W(bt(a), [a.length, e]);
}
var Qd = N({ frame_: W6 });
function U6(r, e, t8, o, n = Yd) {
o == null && (o = Gw(e));
let s = Qd(r, e, t8), a = se(s, n(e));
return lc(a, o);
}
var Q1 = N({ stft_: U6 });
function G6(r, e, t8, o, n = "bilinear", s = 0) {
let a = v(r, "image", "cropAndResize"), i = v(e, "boxes", "cropAndResize", "float32"), p = v(t8, "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(cn, c, l);
}
var Z1 = N({ cropAndResize_: G6 });
function H6(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 t8 = { image: e };
return T.runKernel(Cn, t8, {});
}
var J1 = N({ flipLeftRight_: H6 });
function K6(r) {
let e = v(r, "image", "grayscaleToRGB"), t8 = e.rank - 1, o = e.shape[t8];
$(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, t8), n[t8] = 3, uu(e, n);
}
var eN = N({ grayscaleToRGB_: K6 });
function q6(r, e, t8 = 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: t8, center: o };
return T.runKernel(Ds, s, a);
}
var tN = N({ rotateWithOffset_: q6 });
function $o(r, e, t8, o, n, s) {
o == null && (o = 0.5), n == null && (n = Number.NEGATIVE_INFINITY), s == null && (s = 0);
let a = r.shape[0];
return t8 = Math.min(t8, 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: t8, iouThreshold: o, scoreThreshold: n, softNmsSigma: s };
}
function j6(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY) {
let s = v(r, "boxes", "nonMaxSuppression", "float32"), a = v(e, "scores", "nonMaxSuppression", "float32"), i = $o(s, a, t8, o, n);
t8 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold;
let p = { maxOutputSize: t8, iouThreshold: o, scoreThreshold: n };
return T.runKernel(Qn, { boxes: s, scores: a }, p);
}
var rN = N({ nonMaxSuppression_: j6 });
function oN(r, e, t8) {
let o = X6(r, e, t8), n = o < 0 ? -(o + 1) : o;
r.splice(n, 0, e);
}
function X6(r, e, t8) {
return Q6(r, e, t8 || Y6);
}
function Y6(r, e) {
return r > e ? 1 : r < e ? -1 : 0;
}
function Q6(r, e, t8) {
let o = 0, n = r.length, s = 0, a = false;
for (; o < n; ) {
s = o + (n - o >>> 1);
let i = t8(e, r[s]);
i > 0 ? o = s + 1 : (n = s, a = !i);
}
return a ? o : -o - 1;
}
function Zd(r, e, t8, o, n) {
return Kw(r, e, t8, o, n, 0);
}
function Jd(r, e, t8, o, n, s) {
return Kw(r, e, t8, o, n, 0, false, s, true);
}
function ef(r, e, t8, o, n, s) {
return Kw(r, e, t8, o, n, s, true);
}
function Kw(r, e, t8, 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(nN);
let c = s > 0 ? -0.5 / s : 0, l = [], m = [];
for (; l.length < t8 && u.length > 0; ) {
let g = u.pop(), { score: x, boxIndex: b, suppressBeginIndex: C } = g;
if (x < n)
break;
let S = false;
for (let k = l.length - 1; k >= C; --k) {
let _ = Z6(r, b, l[k]);
if (_ >= o) {
S = true;
break;
}
if (g.score = g.score * J6(o, c, _), g.score <= n)
break;
}
g.suppressBeginIndex = l.length, S || (g.score === x ? (l.push(b), m.push(g.score)) : g.score > n && oN(u, g, nN));
}
let d = l.length, f = t8 - 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 Z6(r, e, t8) {
let o = r.subarray(e * 4, e * 4 + 4), n = r.subarray(t8 * 4, t8 * 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), C = Math.max(x - h, 0) * Math.max(b - g, 0);
return C / (d + f - C);
}
function J6(r, e, t8) {
let o = Math.exp(e * t8 * t8);
return t8 <= r ? o : 0;
}
function nN(r, e) {
return r.score - e.score || r.score === e.score && e.boxIndex - r.boxIndex;
}
async function ej(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY) {
let s = v(r, "boxes", "nonMaxSuppressionAsync"), a = v(e, "scores", "nonMaxSuppressionAsync"), i = $o(s, a, t8, o, n);
t8 = 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 } = Zd(u, c, t8, o, n);
return s !== r && s.dispose(), a !== e && a.dispose(), xr(l, "int32");
}
var sN = ej;
function tj(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) {
let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = $o(a, i, t8, o, n, s);
t8 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma;
let u = { boxes: a, scores: i }, c = { maxOutputSize: t8, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }, l = T.runKernel(Zn, u, c);
return { selectedIndices: l[0], selectedScores: l[1] };
}
var aN = N({ nonMaxSuppressionWithScore_: tj });
async function rj(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) {
let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = $o(a, i, t8, o, n, s);
t8 = 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 } = ef(c, l, t8, o, n, s);
return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: xr(m, "int32"), selectedScores: xr(d) };
}
var iN = rj;
function oj(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) {
let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = $o(a, i, t8, 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(Xa, m, d);
return { selectedIndices: f[0], validOutputs: f[1] };
}
var uN = N({ nonMaxSuppressionPadded_: oj });
async function nj(r, e, t8, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) {
let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = $o(a, i, t8, 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 } = Jd(m, d, u, c, l, s);
return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: xr(f, "int32"), validOutputs: ke(h, "int32") };
}
var pN = nj;
function sj(r, e, t8 = 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 || t8 === 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: t8, halfPixelCenters: o, size: e }, u = T.runKernel(is, i, p);
return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u;
}
var cN = N({ resizeBilinear_: sj });
function aj(r, e, t8 = 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"), $(o === false || t8 === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
let s = n, a = false;
n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]]));
let [] = e, i = { images: s }, p = { alignCorners: t8, halfPixelCenters: o, size: e }, u = T.runKernel(as, i, p);
return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u;
}
var lN = N({ resizeNearestNeighbor_: aj });
function ij(r, e = "binary", t8 = false, o = 0.5) {
let n = v(r, "image", "threshold"), s = 0.2989, a = 0.587, i = 0.114, p = n.shape[0] * n.shape[1], u = se(xr([o]), 255), c, l, m, d;
if ($(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] = pi(n, [1, 1, 1], -1);
let g = se(c, s), x = se(l, a), b = se(m, i);
d = Ce(Ce(g, x), b);
} else
d = r;
if (e === "otsu") {
let g = fd(qe(Ud(d), "int32"), ir([]), 256);
u = uj(g, p);
}
let f = t8 ? uc(d, u) : Uu(d, u);
return qe(se(f, 255), "int32");
}
function uj(r, e) {
let t8 = xr([-1]), o = xr([0]), n = xr([0]), s, a, i, p, u, c;
for (let l = 0; l < r.size - 1; l++) {
s = Xe(r, 0, l + 1), a = Xe(r, l + 1), u = je(ot(s), e), c = je(ot(a), e);
let m = ot(se(s, cu(0, s.size)));
i = je(m, ot(s));
let d = Ta(a.shape, s.size), f = Ce(cu(0, a.size), d), h = se(a, f);
p = je(ot(h), ot(a));
let g = Te(i, p), x = Te(i, p), b = se(u, c);
n = se(se(b, g), x);
let C = Uu(n, o);
o = co(C, n, o), t8 = co(C, xr([l]), t8);
}
return t8;
}
var mN = N({ threshold_: ij });
function pj(r, e, t8 = "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: t8, fillMode: o, fillValue: n, outputShape: s };
return T.runKernel(Rs, p, u);
}
var dN = N({ transform_: pj });
function cj(r, e, t8) {
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 = co(kl(e, 0), s, Ku(e, s))), typeof t8 == "number" ? ($(t8 % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${t8}.`), $(t8 <= a, () => `bandPart(): numUpper (${t8}) must not be greater than the number of columns (${a}).`), p = v(t8 < 0 ? a : t8, "numUpper", "bandPart")) : ($(t8.dtype === "int32", () => "bandPart(): numUpper's dtype must be an int32."), p = co(kl(t8, 0), a, Ku(t8, a)));
let u = W(cu(0, s, 1, "int32"), [-1, 1]), c = cu(0, a, 1, "int32"), l = Te(u, c), m = Gu(uc(l, i), Sd(l, pr(p))), d = Gr([s, a], o.dtype);
return W(kr(mo(W(o, [-1, s, a])).map((f) => co(m, f, d))), n);
}
var fN = N({ bandPart_: cj });
function lj(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 = pi(r, r.shape[0], 0).map((n) => mc(n, [0]));
$(r.length <= r[0].shape[0], () => `Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);
let t8 = [], o = r;
for (let n = 0; n < r.length; ++n)
t8.push(T.tidy(() => {
let s = o[n];
if (n > 0)
for (let a = 0; a < n; ++a) {
let i = se(ot(se(t8[a], s)), t8[a]);
s = Te(s, i);
}
return je(s, Wu(s, "euclidean"));
}));
return e ? kr(t8, 0) : t8;
}
var hN = N({ gramSchmidt_: lj });
function mj(r, e = false) {
if ($(r.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`), r.rank === 2)
return gN(r, e);
{
let t8 = r.shape.slice(0, r.shape.length - 2).reduce((p, u) => p * u), o = mo(W(r, [t8, r.shape[r.shape.length - 2], r.shape[r.shape.length - 1]]), 0), n = [], s = [];
o.forEach((p) => {
let [u, c] = gN(p, e);
n.push(u), s.push(c);
});
let a = W(kr(n, 0), r.shape), i = W(kr(s, 0), r.shape);
return [a, i];
}
}
function gN(r, e = false) {
return T.tidy(() => {
$(r.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);
let t8 = r.shape[0], o = r.shape[1], n = bd(t8), s = Ur(r), a = mu([[1]], [1, 1]), i = Ur(a), p = t8 >= o ? o : t8;
for (let u = 0; u < p; ++u) {
let c = s, l = i, m = n;
[i, s, n] = T.tidy(() => {
let d = Xe(s, [u, u], [t8 - u, 1]), f = Wu(d), h = Xe(s, [u, u], [1, 1]), g = co(Uu(h, 0), mu([[-1]]), mu([[1]])), x = Te(h, se(g, f)), b = je(d, x);
b.shape[0] === 1 ? i = Ur(a) : i = bt([a, Xe(b, [1, 0], [b.shape[0] - 1, b.shape[1]])], 0);
let C = pr(je(Ze(g, x), f)), S = Xe(s, [u, 0], [t8 - u, o]), k = se(C, i), _ = fc(i);
if (u === 0)
s = Te(S, Ze(k, Ze(_, S)));
else {
let D = Te(S, Ze(k, Ze(_, S)));
s = bt([Xe(s, [0, 0], [u, o]), D], 0);
}
let E = fc(k), R = Xe(n, [0, u], [t8, n.shape[1] - u]);
if (u === 0)
n = Te(R, Ze(Ze(R, i), E));
else {
let D = Te(R, Ze(Ze(R, i), E));
n = bt([Xe(n, [0, 0], [t8, u]), D], 1);
}
return [i, s, n];
}), Ot([c, l, m]);
}
return !e && t8 > o && (n = Xe(n, [0, 0], [t8, o]), s = Xe(s, [0, 0], [o, o])), [n, s];
});
}
var xN = N({ qr_: mj });
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 dj(r, e, t8 = 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 (t8 === Et.NONE)
return s;
if (t8 === Et.SUM)
return ot(s);
if (t8 === Et.MEAN) {
if (n == null)
return Hu(s);
{
let a = o.size / n.size, i = je(ot(s), ot(n));
return a > 1 ? je(i, ke(a)) : i;
}
}
if (t8 === Et.SUM_BY_NONZERO_WEIGHTS) {
if (n == null)
return je(ot(s), ke(o.size));
{
let a = se(n, $a(o.shape)), i = qe(ot(Ad(a, ke(0))), "float32");
return je(ot(s), i);
}
}
throw Error(`Unknown reduction: ${t8}`);
}
var cr = N({ computeWeightedLoss_: dj });
function fj(r, e, t8, o = Et.SUM_BY_NONZERO_WEIGHTS) {
let n = v(r, "labels", "absoluteDifference"), s = v(e, "predictions", "absoluteDifference"), a = null;
t8 != null && (a = v(t8, "weights", "absoluteDifference")), yt(n.shape, s.shape, "Error in absoluteDifference: ");
let i = Jt(Te(n, s));
return cr(i, a, o);
}
var yN = N({ absoluteDifference_: fj });
function hj(r, e, t8, 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")), yt(s.shape, a.shape, "Error in cosineDistance: ");
let p = ke(1), u = Te(p, ot(se(s, a), t8, true));
return cr(u, i, n);
}
var bN = N({ cosineDistance_: hj });
function gj(r, e, t8, o = Et.SUM_BY_NONZERO_WEIGHTS) {
let n = v(r, "labels", "hingeLoss"), s = v(e, "predictions", "hingeLoss"), a = null;
t8 != null && (a = v(t8, "weights", "hingeLoss")), yt(n.shape, s.shape, "Error in hingeLoss: ");
let i = ke(1);
n = Te(se(ke(2), n), i);
let p = lu(Te(i, se(n, s)));
return cr(p, a, o);
}
var CN = N({ hingeLoss_: gj });
function xj(r, e, t8, o = 1, n = Et.SUM_BY_NONZERO_WEIGHTS) {
let s = v(r, "labels", "huberLoss"), a = v(e, "predictions", "huberLoss"), i = null;
t8 != null && (i = v(t8, "weights", "huberLoss")), yt(s.shape, a.shape, "Error in huberLoss: ");
let p = ke(o), u = Jt(Te(a, s)), c = Ku(u, p), l = Te(u, c), m = Ce(se(ke(0.5), er(c)), se(p, l));
return cr(m, i, n);
}
var wN = N({ huberLoss_: xj });
function yj(r, e, t8, o = 1e-7, n = Et.SUM_BY_NONZERO_WEIGHTS) {
let s = v(r, "labels", "logLoss"), a = v(e, "predictions", "logLoss"), i = null;
t8 != null && (i = v(t8, "weights", "logLoss")), yt(s.shape, a.shape, "Error in logLoss: ");
let p = ke(1), u = ke(o), c = pr(se(s, ii(Ce(a, u)))), l = se(Te(p, s), ii(Ce(Te(p, a), u))), m = Te(c, l);
return cr(m, i, n);
}
var SN = N({ logLoss_: yj });
function bj(r, e, t8, o = Et.SUM_BY_NONZERO_WEIGHTS) {
let n = v(r, "labels", "meanSquaredError"), s = v(e, "predictions", "meanSquaredError"), a = null;
t8 != null && (a = v(t8, "weights", "meanSquaredError")), yt(n.shape, s.shape, "Error in meanSquaredError: ");
let i = Hd(n, s);
return cr(i, a, o);
}
var IN = N({ meanSquaredError_: bj });
function Cj(r, e) {
let t8 = v(r, "labels", "sigmoidCrossEntropyWithLogits"), o = v(e, "logits", "sigmoidCrossEntropyWithLogits");
yt(t8.shape, o.shape, "Error in sigmoidCrossEntropyWithLogits: ");
let n = lu(o), s = se(o, t8), a = vd(_o(pr(Jt(o))));
return Ce(Te(n, s), a);
}
function wj(r, e, t8, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) {
let s = v(r, "multiClassLabels", "sigmoidCrossEntropy"), a = v(e, "logits", "sigmoidCrossEntropy"), i = null;
if (t8 != null && (i = v(t8, "weights", "sigmoidCrossEntropy")), yt(s.shape, a.shape, "Error in sigmoidCrossEntropy: "), o > 0) {
let u = ke(o), c = ke(1), l = ke(0.5);
s = Ce(se(s, Te(c, u)), se(l, u));
}
let p = Cj(s, a);
return cr(p, i, n);
}
var vN = N({ sigmoidCrossEntropy_: wj });
function Sj(r, e, t8 = -1) {
if (t8 === -1 && (t8 = e.rank - 1), t8 !== 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 ${t8}`);
return vr((n, s, a) => {
let p = Td(s, [t8], true), u = Te(qe(s, "float32"), p);
a([n, u]);
let c = pr(se(u, n));
return { value: ot(c, [t8]), gradFunc: (d, f) => {
let [h, g] = f, x = ni(d.shape, [t8]);
return [se(W(d, x), Te(qe(h, "float32"), _o(g))), se(W(d, x), Te(_o(g), qe(h, "float32")))];
} };
})(r, e);
}
function Ij(r, e, t8, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) {
let s = v(r, "onehotLabels", "softmaxCrossEntropy"), a = v(e, "logits", "softmaxCrossEntropy"), i = null;
if (t8 != null && (i = v(t8, "weights", "softmaxCrossEntropy")), yt(s.shape, a.shape, "Error in softmaxCrossEntropy: "), o > 0) {
let u = ke(o), c = ke(1), l = ke(s.shape[1]);
s = Ce(se(s, Te(c, u)), je(u, l));
}
let p = Sj(s, a);
return cr(p, i, n);
}
var kN = N({ softmaxCrossEntropy_: Ij });
function vj(r, e, t8, o) {
let n = v(r, "indices", "sparseFillEmptyRows", "int32"), s = v(e, "values", "sparseFillEmptyRows"), a = v(t8, "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(Hi, p);
return { outputIndices: u[0], outputValues: u[1], emptyRowIndicator: u[2], reverseIndexMap: u[3] };
}
var NN = N({ sparseFillEmptyRows_: vj });
function kj(r, e, t8) {
let o = v(r, "inputIndices", "sparseReshape", "int32"), n = v(e, "inputShape", "sparseReshape", "int32"), s = v(t8, "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(Za, a);
return { outputIndices: i[0], outputShape: i[1] };
}
var TN = N({ sparseReshape_: kj });
function Nj(r, e, t8) {
let o = v(r, "data", "sparseSegmentMean"), n = v(e, "indices", "sparseSegmentMean", "int32"), s = v(t8, "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(Ki, a);
}
var _N = N({ sparseSegmentMean_: Nj });
function Tj(r, e, t8) {
let o = v(r, "data", "sparseSegmentSum"), n = v(e, "indices", "sparseSegmentSum", "int32"), s = v(t8, "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(qi, a);
}
var $N = N({ sparseSegmentSum_: Tj });
function _j(r, e, t8, 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: t8, nGramWidths: o, leftPad: n, rightPad: s, padWidth: a, preserveShortSequences: i }, l = { data: p, dataSplits: u }, m = T.runKernel(xa, l, c);
return { nGrams: m[0], nGramsSplits: m[1] };
}
var EN = N({ stringNGrams_: _j });
function $j(r, e, t8 = 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: t8 }, a = { input: o, delimiter: n }, i = T.runKernel(Xi, a, s);
return { indices: i[0], values: i[1], shape: i[2] };
}
var RN = N({ stringSplit_: $j });
function Ej(r, e) {
let t8 = 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: t8 };
return T.runKernel(Yi, n, o);
}
var DN = N({ stringToHashBucketFast_: Ej });
function Rj(r, e, t8, o = true) {
let n = v(r, "input", "staticRegexReplace", "string"), s = { pattern: e, rewrite: t8, replaceGlobal: o };
return T.runKernel(Du, { x: n }, s);
}
var AN = N({ staticRegexReplace_: Rj });
var Dj = { fft: cc, ifft: Xu, rfft: lc, irfft: Gd };
var Aj = { hammingWindow: Y1, hannWindow: Yd, frame: Qd, stft: Q1 };
var Fj = { flipLeftRight: J1, grayscaleToRGB: eN, resizeNearestNeighbor: lN, resizeBilinear: cN, rotateWithOffset: tN, cropAndResize: Z1, nonMaxSuppression: rN, nonMaxSuppressionAsync: sN, nonMaxSuppressionWithScore: aN, nonMaxSuppressionWithScoreAsync: iN, nonMaxSuppressionPadded: uN, nonMaxSuppressionPaddedAsync: pN, threshold: mN, transform: dN };
var Pj = { bandPart: fN, gramSchmidt: hN, qr: xN };
var Oj = { absoluteDifference: yN, computeWeightedLoss: cr, cosineDistance: bN, hingeLoss: CN, huberLoss: wN, logLoss: SN, meanSquaredError: IN, sigmoidCrossEntropy: vN, softmaxCrossEntropy: kN };
var Mj = { sparseFillEmptyRows: NN, sparseReshape: TN, sparseSegmentMean: _N, sparseSegmentSum: $N };
var Lj = { stringNGrams: EN, stringSplit: RN, stringToHashBucketFast: DN, staticRegexReplace: AN };
var FN = {};
Ke(FN, { Serializable: () => $l, SerializationMap: () => Ra, registerClass: () => qw });
var $l = class {
getClassName() {
return this.constructor.className;
}
static fromConfig(e, t8) {
return new e(t8);
}
};
var Ra = class {
constructor() {
this.classNameMap = {};
}
static getMap() {
return Ra.instance == null && (Ra.instance = new Ra()), Ra.instance;
}
static register(e) {
Ra.getMap().classNameMap[e.className] = [e, e.fromConfig];
}
};
function qw(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."), Ra.register(r);
}
var Nr = class extends $l {
minimize(e, t8 = 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), t8 ? n : (n.dispose(), null);
}
get iterations() {
return this.iterations_ == null && (this.iterations_ = 0), this.iterations_;
}
incrementIterations() {
this.iterations_ = this.iterations + 1;
}
computeGradients(e, t8) {
return Aw(e, t8);
}
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 ${this.getClassName()}`);
}
async extractIterations(e) {
return this.iterations_ = (await e[0].tensor.data())[0], e.slice(1);
}
};
Object.defineProperty(Nr, Symbol.hasInstance, { value: (r) => r.minimize != null && r.computeGradients != null && r.applyGradients != null });
var ep = class extends Nr {
static get className() {
return "Adadelta";
}
constructor(e, t8, o = null) {
super(), this.learningRate = e, this.rho = t8, this.epsilon = o, this.accumulatedGrads = [], this.accumulatedUpdates = [], o == null && (this.epsilon = T.backend.epsilon());
}
applyGradients(e) {
(Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
let s = T.registeredVariables[o], a = false;
this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accum_grad`, variable: De(() => Ht(s).variable(a)) }), this.accumulatedUpdates[n] == null && (this.accumulatedUpdates[n] = { originalName: `${o}/accum_var`, variable: De(() => Ht(s).variable(a)) });
let i = Array.isArray(e) ? e[n].tensor : e[o];
if (i == null)
return;
let p = this.accumulatedGrads[n].variable, u = this.accumulatedUpdates[n].variable;
De(() => {
let c = Ce(se(p, this.rho), se(er(i), 1 - this.rho)), l = se(je(Dr(Ce(u, this.epsilon)), Dr(Ce(p, this.epsilon))), i), m = Ce(se(u, this.rho), se(er(l), 1 - this.rho));
p.assign(c), u.assign(m);
let d = Ce(se(l, -this.learningRate), s);
s.assign(d);
});
}), this.incrementIterations();
}
dispose() {
this.accumulatedUpdates != null && (Ot(this.accumulatedGrads.map((e) => e.variable)), Ot(this.accumulatedUpdates.map((e) => e.variable)));
}
async getWeights() {
let e = [...this.accumulatedGrads, ...this.accumulatedUpdates];
return [await this.saveIterations()].concat(e.map((t8) => ({ name: t8.originalName, tensor: t8.variable })));
}
async setWeights(e) {
e = await this.extractIterations(e);
let t8 = e.length / 2, o = false;
this.accumulatedGrads = e.slice(0, t8).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedUpdates = e.slice(t8, t8 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) }));
}
getConfig() {
return { learningRate: this.learningRate, rho: this.rho, epsilon: this.epsilon };
}
static fromConfig(e, t8) {
return new e(t8.learningRate, t8.rho, t8.epsilon);
}
};
var tp = class extends Nr {
static get className() {
return "Adagrad";
}
constructor(e, t8 = 0.1) {
super(), this.learningRate = e, this.initialAccumulatorValue = t8, this.accumulatedGrads = [];
}
applyGradients(e) {
(Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
let s = T.registeredVariables[o];
this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accumulator`, variable: De(() => Ta(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 = Ce(i, er(a));
i.assign(p);
let u = Ce(se(je(a, Dr(Ce(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 t8 = false;
this.accumulatedGrads = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t8) }));
}
getConfig() {
return { learningRate: this.learningRate, initialAccumulatorValue: this.initialAccumulatorValue };
}
static fromConfig(e, t8) {
return new e(t8.learningRate, t8.initialAccumulatorValue);
}
};
var rp = class extends Nr {
static get className() {
return "Adam";
}
constructor(e, t8, o, n = null) {
super(), this.learningRate = e, this.beta1 = t8, this.beta2 = o, this.epsilon = n, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], De(() => {
this.accBeta1 = ke(t8).variable(), this.accBeta2 = ke(o).variable();
}), n == null && (this.epsilon = T.backend.epsilon());
}
applyGradients(e) {
let t8 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e);
De(() => {
let o = Te(1, this.accBeta1), n = Te(1, this.accBeta2);
t8.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 = Ce(se(c, this.beta1), se(u, 1 - this.beta1)), d = Ce(se(l, this.beta2), se(er(u), 1 - this.beta2)), f = je(m, o), h = je(d, n);
c.assign(m), l.assign(d);
let g = Ce(se(je(f, Ce(Dr(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((t8) => ({ name: t8.originalName, tensor: t8.variable })));
}
async setWeights(e) {
e = await this.extractIterations(e), De(() => {
this.accBeta1.assign(si(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(si(this.beta2, this.iterations_ + 1));
});
let t8 = e.length / 2, o = false;
this.accumulatedFirstMoment = e.slice(0, t8).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedSecondMoment = e.slice(t8, t8 * 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, t8) {
return new e(t8.learningRate, t8.beta1, t8.beta2, t8.epsilon);
}
};
var op = class extends Nr {
static get className() {
return "Adamax";
}
constructor(e, t8, o, n = null, s = 0) {
super(), this.learningRate = e, this.beta1 = t8, this.beta2 = o, this.epsilon = n, this.decay = s, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], De(() => {
this.iteration = ke(0).variable(), this.accBeta1 = ke(t8).variable();
}), n == null && (this.epsilon = T.backend.epsilon());
}
applyGradients(e) {
let t8 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e);
De(() => {
let o = Te(1, this.accBeta1), n = je(-this.learningRate, Ce(se(this.iteration, this.decay), 1));
t8.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 = Ce(se(c, this.beta1), se(u, 1 - this.beta1)), d = se(l, this.beta2), f = Jt(u), h = Dd(d, f);
c.assign(m), l.assign(h);
let g = Ce(se(je(n, o), je(m, Ce(h, this.epsilon))), i);
i.assign(g);
}), this.iteration.assign(Ce(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, t8) {
return new e(t8.learningRate, t8.beta1, t8.beta2, t8.epsilon, t8.decay);
}
};
var ci = class extends Nr {
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 = Ce(se(this.c, s), a);
a.assign(i);
});
}), this.incrementIterations();
}
setLearningRate(e) {
this.learningRate = e, this.c != null && this.c.dispose(), this.c = Rr(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, t8) {
return new e(t8.learningRate);
}
};
var np = class extends ci {
static get className() {
return "Momentum";
}
constructor(e, t8, o = false) {
super(e), this.learningRate = e, this.momentum = t8, 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: `${o}/momentum`, variable: De(() => Ht(s).variable(false)) });
let a = this.accumulations[n].variable, i = Array.isArray(e) ? e[n].tensor : e[o];
i != null && De(() => {
let p, u = Ce(se(this.m, a), i);
this.useNesterov ? p = Ce(se(this.c, Ce(i, se(u, this.m))), s) : p = Ce(se(this.c, u), s), a.assign(u), s.assign(p);
});
}), this.incrementIterations();
}
dispose() {
this.m.dispose(), this.accumulations != null && Ot(this.accumulations.map((e) => e.variable));
}
setMomentum(e) {
this.momentum = e;
}
async getWeights() {
return [await this.saveIterations()].concat(this.accumulations.map((e) => ({ name: e.originalName, tensor: e.variable })));
}
async setWeights(e) {
e = await this.extractIterations(e);
let t8 = false;
this.accumulations = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t8) }));
}
getConfig() {
return { learningRate: this.learningRate, momentum: this.momentum, useNesterov: this.useNesterov };
}
static fromConfig(e, t8) {
return new e(t8.learningRate, t8.momentum, t8.useNesterov);
}
};
var sp = class extends Nr {
static get className() {
return "RMSProp";
}
constructor(e, t8 = 0.9, o = 0, n = null, s = false) {
if (super(), this.learningRate = e, this.decay = t8, this.momentum = o, this.epsilon = n, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = s, n == null && (this.epsilon = T.backend.epsilon()), e == null)
throw new Error("learningRate for RMSPropOptimizer must be defined.");
}
applyGradients(e) {
(Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
let s = T.registeredVariables[o], a = false;
this.accumulatedMeanSquares[n] == null && (this.accumulatedMeanSquares[n] = { originalName: `${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 = Ce(se(p, this.decay), se(er(i), 1 - this.decay));
if (this.centered) {
let l = this.accumulatedMeanGrads[n].variable, m = Ce(se(l, this.decay), se(i, 1 - this.decay)), d = je(se(i, this.learningRate), Dr(Te(c, Ce(er(m), this.epsilon)))), f = Ce(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 = Ce(se(p, this.decay), se(er(i), 1 - this.decay)), m = Ce(se(u, this.momentum), je(se(i, this.learningRate), Dr(Ce(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((t8) => ({ name: t8.originalName, tensor: t8.variable })));
}
async setWeights(e) {
e = await this.extractIterations(e);
let t8 = this.centered ? e.length / 3 : e.length / 2, o = false;
this.accumulatedMeanSquares = e.slice(0, t8).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedMoments = e.slice(t8, t8 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.centered && (this.accumulatedMeanGrads = e.slice(t8 * 2, t8 * 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, t8) {
return new e(t8.learningRate, t8.decay, t8.momentum, t8.epsilon, t8.centered);
}
};
var Bj = [ep, tp, rp, op, np, sp, ci];
function PN() {
for (let r of Bj)
qw(r);
}
var mi = {};
Ke(mi, { CompositeArrayBuffer: () => jt, browserFiles: () => MN, browserHTTPRequest: () => zN, concatenateArrayBuffers: () => J0, copyModel: () => dk, decodeWeights: () => nd, encodeWeights: () => Y0, fromMemory: () => VN, fromMemorySync: () => Jw, getLoadHandlers: () => ok, getModelArtifactsForJSON: () => oc, getModelArtifactsForJSONSync: () => ww, getModelArtifactsInfoForJSON: () => wa, getSaveHandlers: () => rk, getWeightSpecs: () => ad, http: () => rf, isHTTPScheme: () => tf, listModels: () => lk, loadWeights: () => LN, moveModel: () => fk, registerLoadRouter: () => tk, registerSaveRouter: () => ek, removeModel: () => mk, weightsLoaderFactory: () => Qw, withSaveHandler: () => WN, withSaveHandlerSync: () => UN });
var zj = "model";
var Vj = ".json";
var Wj = ".weights.bin";
function ON(r) {
return new Promise((e) => setTimeout(e)).then(r);
}
var li = class {
constructor(e) {
if (!A().getBool("IS_BROWSER"))
throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
e.startsWith(li.URL_SCHEME) && (e = e.slice(li.URL_SCHEME.length)), (e == null || e.length === 0) && (e = zj), this.modelJsonFileName = e + Vj, this.weightDataFileName = e + Wj;
}
async save(e) {
if (typeof document == "undefined")
throw new Error("Browser downloads are not supported in this environment since `document` is not present");
let t8 = jt.join(e.weightData), o = window.URL.createObjectURL(new Blob([t8], { 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 n = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], s = sd(e, n), a = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), i = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
if (i.download = this.modelJsonFileName, i.href = a, await ON(() => i.dispatchEvent(new MouseEvent("click"))), e.weightData != null) {
let p = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
p.download = this.weightDataFileName, p.href = o, await ON(() => p.dispatchEvent(new MouseEvent("click")));
}
return { modelArtifactsInfo: wa(e) };
}
}
};
li.URL_SCHEME = "downloads://";
var jw = 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, t8) => {
let o = new FileReader();
o.onload = (n) => {
let s = JSON.parse(n.target.result), a = s.modelTopology;
if (a == null) {
t8(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));
return;
}
if (s.weightsManifest == null) {
t8(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));
return;
}
if (this.weightsFiles.length === 0) {
e({ modelTopology: a });
return;
}
let p = oc(s, (u) => this.loadWeights(u));
e(p);
}, o.onerror = (n) => t8(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), o.readAsText(this.jsonFile);
});
}
loadWeights(e) {
let t8 = [], o = [];
for (let a of e)
t8.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) => [t8, a]);
}
loadWeightsFile(e, t8) {
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 '${e}'.`), s.readAsArrayBuffer(t8);
});
}
checkManifestAndWeightFiles(e) {
let t8 = [], o = this.weightsFiles.map((s) => Cw(s.name)), n = {};
for (let s of e)
s.paths.forEach((a) => {
let i = Cw(a);
if (t8.indexOf(i) !== -1)
throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);
if (t8.push(i), o.indexOf(i) === -1)
throw new Error(`Weight file with basename '${i}' is not provided.`);
n[a] = this.weightsFiles[o.indexOf(i)];
});
if (t8.length !== this.weightsFiles.length)
throw new Error(`Mismatch in the number of files in weights manifest (${t8.length}) and the number of weight files provided (${this.weightsFiles.length}).`);
return n;
}
};
var Uj = (r) => A().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(li.URL_SCHEME) ? Gj(r.slice(li.URL_SCHEME.length)) : null;
ft.registerSaveRouter(Uj);
function Gj(r = "model") {
return new li(r);
}
function MN(r) {
return new jw(r);
}
function Xw(r, e, t8, o) {
a(r), t8 = t8 == null ? 0 : t8, o = o == null ? 1 : o, i(t8, o);
let n = 0, s = (p) => (p.then((u) => {
let c = t8 + ++n / r.length * (o - t8);
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 Yw(r, e) {
e == null && (e = {});
let t8 = e.fetchFunc == null ? A().platform.fetch : e.fetchFunc, o = r.map((l) => t8(l, e.requestInit, { isBinary: true })), n = 0, s = 0.5, i = (e.onProgress == null ? await Promise.all(o) : await Xw(o, e.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1;
return e.onProgress == null ? await Promise.all(i) : await Xw(i, e.onProgress, p, u);
}
async function LN(r, e = "", t8, o) {
return Qw((a) => Yw(a, { requestInit: o }))(r, e, t8);
}
function Qw(r) {
return async (e, t8 = "", 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] * Ge(g.shape), C = () => {
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 && (C(), a[k] = true);
}) : C(), 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: ${i.join(", ")}.`);
}
let p = n.reduce((d, f, h) => (f && d.push(h), d), []), u = [];
p.forEach((d) => {
e[d].paths.forEach((f) => {
let h = t8 + (t8.endsWith("/") ? "" : "/") + f;
u.push(h);
});
});
let c = await r(u), l = {}, m = 0;
return p.forEach((d) => {
let f = e[d].paths.length, h = new jt(c.slice(m, m + f));
s[d].forEach((x) => {
let b = h.slice(x.groupOffset, x.groupOffset + x.sizeBytes), C = nd(b, [x.manifestEntry]);
for (let S in C)
l[S] = C[S];
}), m += f;
}), l;
};
}
var Hj = "application/octet-stream";
var Kj = "application/json";
var El = class {
constructor(e, t8) {
if (this.DEFAULT_METHOD = "POST", t8 == null && (t8 = {}), this.weightPathPrefix = t8.weightPathPrefix, this.onProgress = t8.onProgress, this.weightUrlConverter = t8.weightUrlConverter, t8.fetchFunc != null ? ($(typeof t8.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 = t8.fetchFunc) : this.fetch = A().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, t8.requestInit != null && t8.requestInit.body != null)
throw new Error("requestInit is expected to have no pre-existing body, but has one.");
this.requestInit = t8.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 t8 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
t8.body = new FormData();
let o = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], n = sd(e, o);
if (t8.body.append("model.json", new Blob([JSON.stringify(n)], { type: Kj }), "model.json"), e.weightData != null) {
let a = jt.join(e.weightData);
t8.body.append("model.weights.bin", new Blob([a], { type: Hj }), "model.weights.bin");
}
let s = await this.fetch(this.path, t8);
if (s.ok)
return { modelArtifactsInfo: wa(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 t8;
try {
t8 = 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 = t8.modelTopology, n = t8.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 oc(t8, (s) => this.loadWeights(s));
}
async loadWeights(e) {
let t8 = Array.isArray(this.path) ? this.path[1] : this.path, [o, n] = qj(t8), s = this.weightPathPrefix || o, a = ad(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 Yw(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
return [a, u];
}
};
El.URL_SCHEME_REGEX = /^https?:\/\//;
function qj(r) {
let e = r.lastIndexOf("/"), t8 = r.lastIndexOf("?"), o = r.substring(0, e), n = t8 > e ? r.substring(t8) : "";
return [o + "/", n];
}
function tf(r) {
return r.match(El.URL_SCHEME_REGEX) != null;
}
var BN = (r, e) => {
if (typeof fetch == "undefined" && (e == null || e.fetchFunc == null))
return null;
{
let t8 = true;
if (Array.isArray(r) ? t8 = r.every((o) => tf(o)) : t8 = tf(r), t8)
return rf(r, e);
}
return null;
};
ft.registerSaveRouter(BN);
ft.registerLoadRouter(BN);
function rf(r, e) {
return new El(r, e);
}
function zN(r, e) {
return rf(r, e);
}
var Rl = class {
constructor(e) {
this.modelArtifacts = e;
}
load() {
return this.modelArtifacts;
}
};
var of = class {
constructor(e) {
this.saveHandler = e;
}
save(e) {
return this.saveHandler(e);
}
};
var Zw = class {
constructor(e) {
e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t8) => Promise.resolve(e.save(t8)));
}
};
function VN(r, e, t8, o) {
let n = arguments;
return new Zw(Jw(...n));
}
function Jw(r, e, t8, 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: t8, trainingConfig: o }));
}
function WN(r) {
return new of(r);
}
function UN(r) {
return new of(r);
}
var HN = {};
Ke(HN, { confusionMatrix: () => GN });
function jj(r, e, t8) {
let o = v(r, "labels", "confusionMatrix"), n = v(e, "predictions", "confusionMatrix");
$(t8 == null || t8 > 0 && Number.isInteger(t8), () => `If provided, numClasses must be a positive integer, but got ${t8}`), $(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.`), $(t8 > 0 && Number.isInteger(t8), () => `numClasses is required to be a positive integer, but got ${t8}`);
let s = Tl(qe(o, "int32"), t8), a = Tl(qe(n, "int32"), t8), i = fc(s), p = Ze(i, a);
return qe(p, "int32");
}
var GN = N({ confusionMatrix_: jj });
var XN = {};
Ke(XN, { draw: () => rX, fromPixels: () => oX, fromPixelsAsync: () => Jj, toPixels: () => tX });
var ap;
var KN = false;
function qN(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 t8 = false, o = false, n = false, s = false, a = false, i = false;
if (r.data instanceof Uint8Array)
t8 = 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(Au, T.backendName) != null) {
let f = { pixels: r }, h = { numChannels: e };
return T.runKernel(Au, 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 || t8)
l = r.data;
else if (s || n || i) {
if (ap == null)
if (typeof document == "undefined")
if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined")
ap = new OffscreenCanvas(1, 1).getContext("2d");
else
throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
else
ap = document.createElement("canvas").getContext("2d", { willReadFrequently: true });
ap.canvas.width = u, ap.canvas.height = c, ap.drawImage(r, 0, 0, u, c), l = ap.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 qd(m, [c, u, e], "int32");
}
function Xj(r) {
return r != null && r.data instanceof Uint8Array;
}
function Yj() {
return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap");
}
function Qj(r) {
return r != null && r.width !== 0 && r.height !== 0;
}
function Zj(r) {
return Yj() && !(r instanceof ImageBitmap) && Qj(r) && !Xj(r);
}
async function Jj(r, e = 3) {
let t8 = null;
if (A().getBool("WRAP_TO_IMAGEBITMAP") && Zj(r)) {
let o;
try {
o = await createImageBitmap(r, { premultiplyAlpha: "none" });
} catch (n) {
o = null;
}
o != null && o.width === r.width && o.height === r.height ? t8 = o : t8 = r;
} else
t8 = r;
return qN(t8, e);
}
function jN(r) {
if (r.rank !== 2 && r.rank !== 3)
throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${r.rank}.`);
let e = r.rank === 2 ? 1 : r.shape[2];
if (e > 4 || e === 2)
throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${e}`);
if (r.dtype !== "float32" && r.dtype !== "int32")
throw new Error(`Unsupported type for toPixels: ${r.dtype}. Please use float32 or int32 tensors.`);
}
function eX(r) {
let e = (r == null ? void 0 : r.alpha) || 1;
if (e > 1 || e < 0)
throw new Error(`Alpha value ${e} is suppoed to be in range [0 - 1].`);
}
async function tX(r, e) {
let t8 = v(r, "img", "toPixels");
if (!(r instanceof ut)) {
let u = t8;
t8 = qe(u, "int32"), u.dispose();
}
jN(t8);
let [o, n] = t8.shape.slice(0, 2), s = t8.rank === 2 ? 1 : t8.shape[2], a = await t8.data(), i = t8.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 (t8.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 (t8.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) {
KN || (console.warn("tf.browser.toPixels is not efficient to draw tensor on canvas. Please try tf.browser.draw instead."), KN = true), e.width = n, e.height = o;
let u = e.getContext("2d"), c = new ImageData(p, n, o);
u.putImageData(c, 0, 0);
}
return t8 !== r && t8.dispose(), p;
}
function rX(r, e, t8) {
let o = v(r, "img", "draw");
if (!(r instanceof ut)) {
let a = o;
o = qe(a, "int32"), a.dispose();
}
jN(o), eX(t8 == null ? void 0 : t8.imageOptions);
let n = { image: o }, s = { canvas: e, options: t8 };
T.runKernel(qm, n, s);
}
var oX = N({ fromPixels_: qN });
var nf = {};
Ke(nf, { prepareAndValidate: () => YN });
function YN(r, e) {
let t8 = r.shape.length, o = e.shape.length;
if (t8 < 1)
throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${t8}.`);
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] > t8)
throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${e.shape[o - 1]} vs. ${t8}`);
if (Ge(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 < t8; ++l)
u *= i[l], p.push(i[l]);
let c = [...qs(r.shape).map((l) => l / u), 1].slice(0, s);
return [p, a, u, c];
}
var ct = {};
Ke(ct, { assertParamsValid: () => sX, computeFlatOffset: () => cX, computeOutShape: () => iX, getNormalizedAxes: () => uX, isSliceContinous: () => pX, maskToAxes: () => aX, parseSliceParams: () => lX, sliceInfo: () => mX, startForAxis: () => nT, startIndicesWithElidedDims: () => tT, stopForAxis: () => sT, stopIndicesWithElidedDims: () => rT, stridesForAxis: () => oT, stridesWithElidedDims: () => ZN });
var eS = -2;
var nX = -1;
function sX(r, e, t8) {
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}).`), $(o === t8.length, () => `Error in slice${o}D: Length of size ${t8} must match the rank of the array (${o}).`);
for (let n = 0; n < o; ++n)
$(e[n] + t8[n] <= r.shape[n], () => `Error in slice${o}D: begin[${n}] + size[${n}] (${e[n] + t8[n]}) would overflow input.shape[${n}] (${r.shape[n]})`);
}
function aX(r) {
let e = [], t8 = 0;
for (; r > 0; )
r & 1 && e.push(t8), r /= 2, t8++;
return e;
}
function iX(r, e, t8) {
let o = [];
for (let n = 0; n < r.length; n++)
o[n] = Math.ceil((e[n] - r[n]) / t8[n]);
return o;
}
function ZN(r, e, t8, o) {
let n = [...r];
for (let s = n.length; s < o.length; s++)
n.push(1);
for (let s = 0; s < t8; s++)
s === 0 ? n[e] = 1 : (n.splice(e, 0, 1), n.pop());
return n;
}
function JN(r, e, t8) {
return t8 <= r ? t8 : t8 - (e - 1);
}
function eT(r, e) {
let t8 = [];
for (let o = 0; o < r; o++)
t8.push(e + o);
return t8;
}
function uX(r, e, t8, 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 && t8 > 0) {
let d = e[0], f = t8 + 1;
c = tT(a, d, f, o, r), l = rT(i, d, f, n, r), m = ZN(s, d, f, r);
} else
for (let d = 0; d < u; d++)
c[d] = nT(a, o, s, r, d, p), l[d] = sT(i, n, s, r, d, p), m[d] = oT(s, d, p);
return { begin: c, end: l, strides: m };
}
function tT(r, e, t8, o, n) {
let s = [...n], a = eT(t8, e);
for (let i = 0; i < s.length; i++)
if (a.indexOf(i) > -1)
s[i] = 0;
else {
let p = JN(e, t8, i), u = o[p];
r & 1 << p && (u = 0), s[i] = u;
}
return s;
}
function rT(r, e, t8, o, n) {
let s = [...n], a = eT(t8, e);
for (let i = 0; i < s.length; i++)
if (a.indexOf(i) > -1)
s[i] = Number.MAX_SAFE_INTEGER;
else {
let p = JN(e, t8, 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] = Gp(0, s[i], n[i]);
}
return s;
}
function oT(r, e, t8) {
let o = r[e];
return (t8 & 1 << e || o == null) && (o = 1), o;
}
function nT(r, e, t8, o, n, s) {
let a = e[n], i = t8[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 = Gp(0, a, p - 1), a;
}
function sT(r, e, t8, o, n, s) {
let a = e[n], i = t8[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 = Gp(0, a, p) : a = Gp(-1, a, p - 1), a;
}
function pX(r, e, t8) {
let o = t8.length;
for (let n = 0; n < t8.length; n++)
if (t8[n] > 1) {
o = n;
break;
}
for (let n = o + 1; n < t8.length; n++)
if (e[n] > 0 || t8[n] !== r[n])
return false;
return true;
}
function cX(r, e) {
let t8 = r.length > 0 ? r[r.length - 1] : 1;
for (let o = 0; o < r.length - 1; o++)
t8 += r[o] * e[o];
return t8;
}
function lX(r, e, t8) {
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) => {
$(a !== -1, () => "slice() does not support negative begin indexing.");
});
let s;
return t8 == null ? s = new Array(n).fill(-1) : typeof t8 == "number" ? s = [t8, ...new Array(n - 1).fill(-1)] : t8.length < n ? s = t8.concat(new Array(n - t8.length).fill(-1)) : s = t8, s = s.map((a, i) => a >= 0 ? a : ($(a === -1, () => `Negative size values should be exactly -1 but got ${a} for the slice() size at index ${i}.`), r.shape[i] - o[i])), [o, s];
}
function mX(r, e, t8, o, n, s, a, i, p) {
let u;
if (o == null ? (u = new Array(e.length), u.fill(1)) : u = o, a != null && a & a - 1)
throw new Error("Multiple ellipses in slice is not allowed.");
let c = false, l = { dims: u.length, numAddAxisAfterEllipsis: 0, begin: e.slice(), end: t8.slice(), strides: u.slice(), beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p };
for (let C = 0; C < l.dims; C++)
c && 1 << C & i && l.numAddAxisAfterEllipsis++, 1 << C & a && (c = true);
c || (l.ellipsisMask |= 1 << l.dims, l.dims++);
let m = { dims: r.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false };
dX(l, m);
let d = true, f = true, h = true, g = [], x = [];
for (let C = 0; C < r.length; ++C) {
if (m.strides[C] === 0)
throw Error(`strides[${C}] must be non-zero`);
let S = !!(m.shrinkAxisMask & 1 << C), k = r[C];
if (k === -1) {
g.push(S ? 1 : -1);
continue;
}
let _ = [m.beginMask & 1 << C, m.endMask & 1 << C], E = [m.strides[C] > 0 ? 0 : -1, m.strides[C] > 0 ? k : k - 1];
if (S && m.strides[C] <= 0)
throw Error("only stride 1 allowed on non-range indexing.");
h = h && m.strides[C] === 1;
let R = !!(m.beginMask & 1 << C && m.endMask & 1 << C);
if (m.beginValid && m.endValid) {
if (S) {
let M = m.begin[C] < 0 ? k + m.begin[C] : m.begin[C];
if (m.begin[C] = M, m.end[C] = m.begin[C] + 1, M < 0 || M >= k)
throw Error(`slice index ${m.begin[C]} of dimension ${C} out of bounds.`);
} else
m.begin[C] = QN(m.begin[C], 0, m.strides[C], k, _, E), m.end[C] = QN(m.end[C], 1, m.strides[C], k, _, E);
let O = m.strides[C] === 1 && m.begin[C] === 0 && m.end[C] === k;
d = d && O, f = f && (C === 0 && m.strides[C] === 1 || O);
} else
d = d && m.strides[C] === 1 && R, f = f && (C === 0 && m.strides[C] === 1 || R);
let D, P = false;
if (m.beginValid && m.endValid ? (D = m.end[C] - m.begin[C], P = true) : S ? (D = 1, P = true) : R && k >= 0 && (m.strides[C] < 0 ? D = -k : D = k, P = true), P) {
let O;
D === 0 || D < 0 != m.strides[C] < 0 ? O = 0 : O = Math.trunc(D / m.strides[C]) + (D % m.strides[C] !== 0 ? 1 : 0), g.push(O);
} else
g.push(-1);
}
for (let C = 0; C < m.finalShapeGatherIndices.length; ++C) {
let S = m.finalShapeGatherIndices[C];
S >= 0 ? x.push(g[S]) : S === eS && x.push(1);
}
return { finalShapeSparse: x.filter((C, S) => m.finalShapeGatherIndices[S] !== eS), finalShape: x, isIdentity: d, sliceDim0: f, isSimpleSlice: h, begin: m.begin, end: m.end, strides: m.strides };
}
function dX(r, e) {
e.beginMask = 0, e.endMask = 0, e.shrinkAxisMask = 0;
let t8 = 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 (; t8 < n; t8++)
e.begin[t8] = 0, e.end[t8] = 0, e.strides[t8] = 1, e.beginMask |= 1 << t8, e.endMask |= 1 << t8, e.finalShapeGatherIndices.push(t8), e.finalShapeGatherIndicesSparse.push(-1), e.inputShapeGatherIndicesSparse[t8] = o;
} else if (1 << o & r.newAxisMask)
e.finalShapeGatherIndices.push(eS), e.finalShapeGatherIndicesSparse.push(-1);
else {
if (t8 === e.begin.length)
throw Error(`Index out of range using input dim ${t8}; input has only ${e.dims} dims, ${e.begin.length}.`);
r.begin != null && (e.begin[t8] = r.begin[o]), r.end != null && (e.end[t8] = r.end[o]), e.strides[t8] = r.strides[o], r.beginMask & 1 << o && (e.beginMask |= 1 << t8), r.endMask & 1 << o && (e.endMask |= 1 << t8), r.shrinkAxisMask & 1 << o ? (e.finalShapeGatherIndices.push(nX), e.finalShapeGatherIndicesSparse.push(-1), e.shrinkAxisMask |= 1 << t8) : (e.finalShapeGatherIndices.push(t8), e.finalShapeGatherIndicesSparse.push(o)), e.inputShapeGatherIndicesSparse[t8] = o, t8++;
}
}
function QN(r, e, t8, o, n, s) {
if (n[e])
return t8 > 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 fX = "4.7.0";
var Dl = class {
static sgd(e) {
return new ci(e);
}
static momentum(e, t8, o = false) {
return new np(e, t8, o);
}
static rmsprop(e, t8 = 0.9, o = 0, n = null, s = false) {
return new sp(e, t8, o, n, s);
}
static adam(e = 1e-3, t8 = 0.9, o = 0.999, n = null) {
return new rp(e, t8, o, n);
}
static adadelta(e = 1e-3, t8 = 0.95, o = null) {
return new ep(e, t8, o);
}
static adamax(e = 2e-3, t8 = 0.9, o = 0.999, n = null, s = 0) {
return new op(e, t8, o, n, s);
}
static adagrad(e, t8 = 0.1) {
return new tp(e, t8);
}
};
var uGe = Dl;
var hX = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (r) => r())();
function tS() {
return new Promise((r) => hX(() => r()));
}
var w = {};
Ke(w, { ERF_A1: () => DX, ERF_A2: () => AX, ERF_A3: () => FX, ERF_A4: () => PX, ERF_A5: () => OX, ERF_P: () => RX, PARALLELIZE_THRESHOLD: () => sf, RowPartitionType: () => Da, SELU_SCALE: () => EX, SELU_SCALEALPHA: () => $X, applyActivation: () => Zu, assertAndGetBroadcastShape: () => rt, assertAxesAreInnerMostDims: () => GH, assertParamsConsistent: () => gX, assignToTypedArray: () => WX, axesAreInnerMostDims: () => Dw, calculateShapes: () => F1, checkEinsumDimSizes: () => jX, checkPadOnDimRoundingMode: () => Lt, combineLocations: () => c2, combineRaggedTensorToTensorShapes: () => yX, complexWithEvenIndex: () => BX, complexWithOddIndex: () => zX, computeConv2DInfo: () => Vu, computeConv3DInfo: () => $k, computeDefaultPad: () => Rw, computeDilation2DInfo: () => W4, computeOptimalWindowSize: () => SX, computeOutAndReduceShapes: () => UH, computeOutShape: () => xX, computePool2DInfo: () => Ew, computePool3DInfo: () => U4, convertConv2DDataFormat: () => Ek, decodeEinsumEquation: () => KX, eitherStridesOrDilationsAreOne: () => gr, expandShapeToKeepDim: () => ni, exponent: () => GX, exponents: () => UX, fromStringArrayToUint8: () => h5, fromUint8ToStringArray: () => f5, getAxesPermutation: () => HH, getBroadcastDims: () => n2, getComplexWithIndex: () => VX, getEinsumComputePath: () => XX, getEinsumPermutation: () => qX, getFusedBiasGradient: () => Qu, getFusedDyActivation: () => Yu, getImageCenter: () => IX, getInnerMostAxes: () => qH, getPermuted: () => kX, getRaggedRank: () => CX, getReductionAxes: () => gd, getReshaped: () => vX, getReshapedPermuted: () => NX, getRowPartitionTypesHelper: () => bX, getSliceBeginCoords: () => TX, getSliceSize: () => _X, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => JX, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => e5, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => t5, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => n5, getSparseReshapeInputOutputMismatchErrorMessage: () => a5, getSparseReshapeInputOutputMultipleErrorMessage: () => s5, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => r5, getSparseReshapeNegativeOutputDimErrorMessage: () => o5, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => c5, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => i5, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => u5, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => p5, getUndoAxesPermutation: () => KH, isIdentityPermutation: () => YX, log: () => MG, mergeRealAndImagArrays: () => MX, prepareAndValidate: () => YN, prepareSplitSize: () => ZX, segment_util: () => oS, shouldFuse: () => Ju, slice_util: () => ct, splitRealAndImagArrays: () => LX, stridesOrDilationsArePositive: () => va, tupleValuesAreOne: () => zu, upcastType: () => dt, validateDefaultValueShape: () => wX, validateInput: () => dc, validateUpdateShape: () => Uw, warn: () => Ca });
function gX(r, e) {
let t8 = r[0].length;
r.forEach((n, s) => {
$(n.length === t8, () => `Error in concat${t8}D: rank of tensors[${s}] must be the same as the rank of the rest (${t8})`);
}), $(e >= 0 && e < t8, () => `Error in concat${t8}D: axis must be between 0 and ${t8 - 1}.`);
let o = r[0];
r.forEach((n, s) => {
for (let a = 0; a < t8; a++)
$(a === e || n[a] === o[a], () => `Error in concat${t8}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`);
});
}
function xX(r, e) {
let t8 = r[0].slice();
for (let o = 1; o < r.length; o++)
t8[e] += r[o][e];
return t8;
}
var Da;
(function(r) {
r[r.FIRST_DIM_SIZE = 0] = "FIRST_DIM_SIZE", r[r.VALUE_ROWIDS = 1] = "VALUE_ROWIDS", r[r.ROW_LENGTHS = 2] = "ROW_LENGTHS", r[r.ROW_SPLITS = 3] = "ROW_SPLITS", r[r.ROW_LIMITS = 4] = "ROW_LIMITS", r[r.ROW_STARTS = 5] = "ROW_STARTS";
})(Da || (Da = {}));
function yX(r, e, t8) {
let o = new Array();
if (t8 == null && e == null)
return o;
if (e == null)
for (; o.length < r + t8.length; )
o.push(-1);
else
o = e.slice();
if (t8 == null)
return o;
if (r + t8.length !== o.length)
throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.rank = ${r + t8.length}, but shape.rank = ${o.length}`);
for (let n = 1; n < t8.length; ++n) {
let s = t8[n], a = o[o.length - t8.length + n], i = o[a];
if (s >= 0)
if (i >= 0) {
if (i !== s)
throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.shape[${n + r}] = ${s} but shape[${n + r}] = ${i}`);
} else
o[a] = s;
}
return o;
}
function bX(r) {
let e = { FIRST_DIM_SIZE: Da.FIRST_DIM_SIZE, VALUE_ROWIDS: Da.VALUE_ROWIDS, ROW_LENGTHS: Da.ROW_LENGTHS, ROW_SPLITS: Da.ROW_SPLITS, ROW_LIMITS: Da.ROW_LIMITS, ROW_STARTS: Da.ROW_STARTS }, t8 = [];
for (let o of r)
if (o in e)
t8.push(e[o]);
else
break;
return t8;
}
function CX(r) {
return r.length === 0 ? 0 : r[0] === Da.FIRST_DIM_SIZE ? r.length - 1 : r.length;
}
function wX(r, e) {
if (r == null || e == null)
return;
let t8 = r.length, o = e.length;
if (t8 >= o)
throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t8} must be less than ragged tensor input flatValues.rank = ${o})`);
for (let n = 0; n < Math.min(t8, o - 1); ++n) {
let s = r[n], a = e[n + 1];
if (s >= 0 && a >= 0 && s !== 1 && s !== a)
throw new Error(`defaultValue.shape=${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 sf = 30;
function SX(r) {
return r <= sf ? r : Kp(r, Math.floor(Math.sqrt(r)));
}
function IX(r, e, t8) {
let o = t8 * (typeof r == "number" ? r : r[0]), n = e * (typeof r == "number" ? r : r[1]);
return [o, n];
}
function vX(r, e, t8, o = true) {
let n = [];
if (o)
n = n.concat(e.slice(0)), n.push(r[0] / t8), 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 kX(r, e, t8 = true) {
let o = [];
if (t8) {
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 NX(r, e, t8, o = true) {
let n = [];
o ? n.push(r[0] / t8) : n.push(r[0] * t8);
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 TX(r, e) {
let t8 = [0];
for (let o = 0; o < e; ++o)
t8.push(r[o][0]);
return t8;
}
function _X(r, e, t8) {
let o = r.slice(0, 1);
for (let n = 0; n < t8; ++n)
o.push(r[n + 1] - e[n][0] - e[n][1]);
return o;
}
var $X = 1.7580993408473768;
var EX = 1.0507009873554805;
var RX = 0.3275911;
var DX = 0.254829592;
var AX = -0.284496736;
var FX = 1.421413741;
var PX = -1.453152027;
var OX = 1.061405429;
function MX(r, e) {
if (r.length !== e.length)
throw new Error(`Cannot merge real and imag arrays of different lengths. real:${r.length}, imag: ${e.length}.`);
let t8 = new Float32Array(r.length * 2);
for (let o = 0; o < t8.length; o += 2)
t8[o] = r[o / 2], t8[o + 1] = e[o / 2];
return t8;
}
function LX(r) {
let e = new Float32Array(r.length / 2), t8 = new Float32Array(r.length / 2);
for (let o = 0; o < r.length; o += 2)
e[o / 2] = r[o], t8[o / 2] = r[o + 1];
return { real: e, imag: t8 };
}
function BX(r) {
let e = Math.ceil(r.length / 4), t8 = new Float32Array(e), o = new Float32Array(e);
for (let n = 0; n < r.length; n += 4)
t8[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1];
return { real: t8, imag: o };
}
function zX(r) {
let e = Math.floor(r.length / 4), t8 = new Float32Array(e), o = new Float32Array(e);
for (let n = 2; n < r.length; n += 4)
t8[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1];
return { real: t8, imag: o };
}
function VX(r, e) {
let t8 = r[e * 2], o = r[e * 2 + 1];
return { real: t8, imag: o };
}
function WX(r, e, t8, o) {
r[o * 2] = e, r[o * 2 + 1] = t8;
}
function UX(r, e) {
let t8 = new Float32Array(r / 2), o = new Float32Array(r / 2);
for (let n = 0; n < Math.ceil(r / 2); n++) {
let s = (e ? 2 : -2) * Math.PI * (n / r);
t8[n] = Math.cos(s), o[n] = Math.sin(s);
}
return { real: t8, imag: o };
}
function GX(r, e, t8) {
let o = (t8 ? 2 : -2) * Math.PI * (r / e), n = Math.cos(o), s = Math.sin(o);
return { real: n, imag: s };
}
var rS = "->";
var HX = /->/g;
var aT = ",";
var iT = "...";
function KX(r, e) {
r = r.replace(/\s/g, "");
let t8 = (r.length - r.replace(HX, "").length) / rS.length;
if (t8 < 1)
throw new Error("Equations without an arrow are not supported.");
if (t8 > 1)
throw new Error(`Equation must contain exactly one arrow ("${rS}").`);
let [o, n] = r.split(rS);
$(o.indexOf(iT) === -1, () => `The ellipsis notation ("${iT}") is not supported yet.`);
let s = o.split(aT), 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 !== aT && 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 qX(r, e) {
let t8 = new Array(r);
t8.fill(-1);
for (let n = 0; n < e.length; ++n)
t8[e[n]] = n;
let o = [];
for (let n = 0; n < r; ++n)
t8[n] === -1 && o.push(n);
return t8 = t8.filter((n) => n !== -1), { permutationIndices: t8, expandDims: o };
}
function jX(r, e, t8) {
let o = new Array(r);
for (let n = 0; n < t8.length; ++n) {
let s = t8[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 XX(r, e) {
let t8 = r, o = [], n = 0;
r.length === 0 && t8.push(-1), n = r.length + 1;
for (let a = 0; a < n; ++a)
o.push([]);
let s = [];
for (let a = 0; a < t8.length; ++a) {
let i = t8[a], p = QX(e, i);
for (let u of p)
s.indexOf(u) === -1 && (o[a].push(u), s.push(u));
}
return { path: t8, steps: o };
}
function YX(r) {
return r.every((e, t8) => e === t8);
}
function QX(r, e) {
let t8 = [];
for (let o = 0; o < r.length; ++o)
(r[o].length === 0 || r[o].indexOf(e) !== -1 || e === -1) && t8.push(o);
return t8;
}
function ZX(r, e, t8 = 0) {
let o = [];
if (typeof e == "number")
$(r.shape[t8] % e === 0, () => "Number of splits must evenly divide the axis."), o = new Array(e).fill(r.shape[t8] / 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[t8] - a;
}
$(r.shape[t8] === e.reduce((a, i) => a + i), () => "The sum of sizes must match the size of the axis dimension."), o = e;
}
return o;
}
function JX(r) {
return `Received SparseTensor with denseShape[0] = 0 but
indices.shape[0] = ${r}`;
}
function e5(r, e) {
return `indices(${r}, 0) is invalid: ${e} < 0`;
}
function t5(r, e, t8) {
return `indices(${r}, 0) is invalid: ${e} >= ${t8}`;
}
function r5(r, e) {
return `only one output dimension may be -1, not both ${r} and ${e}`;
}
function o5(r, e) {
return `size ${r} must be non-negative, not ${e}`;
}
function n5() {
return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
}
function s5(r, e) {
let t8 = Ge(r), o = Ge(e);
return `Input to reshape is a SparseTensor with ${t8}
dense values, but the requested shape requires a multiple of ${o}. inputShape=${r} outputShape= ${e}`;
}
function a5(r, e) {
let t8 = Ge(r), o = Ge(e);
return `Input to reshape is a tensor with ${t8} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`;
}
function i5() {
return "segment ids must be >= 0";
}
function u5() {
return "segment ids are not increasing";
}
function p5(r, e) {
return `Segment id ${r} out of range [0, ${e}), possibly because segmentIds input is not sorted.`;
}
function c5(r, e, t8) {
return `Bad: indices[${r}] == ${e} out of range [0, ${t8})`;
}
var oS = {};
Ke(oS, { collectGatherOpShapeInfo: () => d5, computeOutShape: () => m5, segOpComputeOptimalWindowSize: () => l5 });
function l5(r, e) {
let t8 = false, o;
for (r <= sf ? (o = r, t8 = true) : o = Kp(r, Math.floor(Math.sqrt(r))); !t8; )
o > e || o === r ? t8 = true : o = Kp(r, o + 1);
return o;
}
function m5(r, e, t8) {
let o = [], n = r.length;
for (let s = 0; s < n; s++)
s !== e ? o.push(r[s]) : o.push(t8);
return o;
}
function d5(r, e, t8, o) {
let n = e.shape.length, s = r.shape.length;
if (o !== 0 && (o < -n || o > n))
throw new Error(`Expect batchDims in the range of [-${n}, ${n}], but got ${o}`);
if (o < 0 && (o += n), o > s)
throw new Error(`batchDims (${o}) must be less than rank(x) (
${s}).`);
if (t8 < o)
throw new Error(`batchDims (${o}) must be less than or equal to axis (${t8}).`);
for (let l = 0; l < o; ++l)
if (r.shape[l] !== e.shape[l])
throw new Error(`x.shape[${l}]: ${r.shape[l]} should be equal to indices.shape[${l}]: ${e.shape[l]}.`);
let a = r.shape[t8], 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 < t8; 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 = t8 + 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 f5(r) {
try {
return r.map((e) => tc(e));
} catch (e) {
throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`);
}
}
function h5(r) {
return r.map((e) => eu(e));
}
var Wt = {};
Ke(Wt, { nonMaxSuppressionV3Impl: () => Zd, nonMaxSuppressionV4Impl: () => Jd, nonMaxSuppressionV5Impl: () => ef, whereImpl: () => jd });
PN();
var g5 = A();
g5.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 fo;
(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";
})(fo || (fo = {}));
var uT;
(function(r) {
let e;
(function(t8) {
t8[t8.LEGACY = 0] = "LEGACY", t8[t8.V1 = 1] = "V1", t8[t8.V2 = 2] = "V2";
})(e = r.CheckpointFormatVersion || (r.CheckpointFormatVersion = {}));
})(uT || (uT = {}));
var sS = {};
function y5(r, e) {
let t8 = { tfOpName: r, category: "custom", inputs: [], attrs: [], customExecutor: e };
sS[r] = t8;
}
function af(r) {
return sS[r];
}
function b5(r) {
delete sS[r];
}
function I(r, e, t8, 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], t8, 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, t8, o, n));
}
let c = Bt(e.inputNames[u], t8, 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, t8, o) {
let [n, s] = Tr(r, t8);
if (o != null) {
let i = o.getHashTableHandleByName(n);
if (i != null)
return i;
}
let a = t8.currentContextIds.find((i) => !!e[uf(n, i)]);
return a !== void 0 ? e[uf(n, a)][s] : void 0;
}
function aS(r, e, t8) {
return e[uf(r, t8.currentContextId)];
}
function Os(r, e) {
let [t8, o, n] = Tr(r, e);
return [uf(t8, e && e.currentContextId), o, n];
}
function uf(r, e) {
return e ? `${r}-${e}` : r;
}
function Tr(r, e) {
if (r === "")
return ["", 0, void 0];
let t8 = e != null && e.parseNodeNameCache != null;
if (t8) {
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 t8 && e.parseNodeNameCache.set(r, n), n;
}
function Al(r, e, t8) {
let o = I("pad", r, e, t8);
if (o === "explicit") {
o = I("explicitPaddings", r, e, t8);
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 Ms(r) {
return r.kept ? r : Ur(r);
}
var iS = {};
Ke(iS, { json: () => C5 });
var C5 = [{ 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 uS = {};
Ke(uS, { json: () => w5 });
var w5 = [{ 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: 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Ke(pS, { json: () => S5 });
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{ 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: 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var cS = {};
Ke(cS, { json: () => I5 });
var I5 = [{ 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: 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{ 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: 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"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 lS = {};
Ke(lS, { json: () => v5 });
var v5 = [{ 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 mS = {};
Ke(mS, { json: () => k5 });
var k5 = [{ 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 dS = {};
Ke(dS, { json: () => N5 });
var N5 = [{ 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 fS = {};
Ke(fS, { json: () => T5 });
var T5 = [{ 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 hS = {};
Ke(hS, { json: () => _5 });
var _5 = [{ 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 gS = {};
Ke(gS, { json: () => $5 });
var $5 = [{ 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 xS = {};
Ke(xS, { json: () => E5 });
var E5 = [{ 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 yS = {};
Ke(yS, { json: () => R5 });
var R5 = [{ 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 bS = {};
Ke(bS, { json: () => D5 });
var D5 = [{ 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 CS = {};
Ke(CS, { 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 wS = {};
Ke(wS, { json: () => F5 });
var F5 = [{ 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 SS = {};
Ke(SS, { json: () => P5 });
var P5 = [{ 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 IS = {};
Ke(IS, { json: () => O5 });
var O5 = [{ 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 vS = {};
Ke(vS, { json: () => M5 });
var M5 = [{ 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 kS = {};
Ke(kS, { 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 = [iS, uS, pS, cS, lS, mS, dS, fS, hS, gS, xS, yS, bS, CS, wS, SS, IS, vS, kS], t8 = [].concat(...e.map((o) => o.json));
this.opMappers = t8.reduce((o, n) => (o[n.tfOpName] = n, o), {});
}
transformGraph(e, t8 = {}) {
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 = {};
t8 != null && (c = this.mapSignatureEntries(t8.inputs), l = this.mapSignatureEntries(t8.outputs));
let m = Object.keys(i);
m.forEach((h) => {
let g = i[h];
g.inputNames.forEach((x, b) => {
let [C, , S] = Os(x), k = i[C];
if (k.outputs != null) {
let _ = k.outputs.indexOf(S);
if (_ !== -1) {
let E = `${C}:${_}`;
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] = Os(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] = Os(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: t8, functions: d };
return a.length > 0 && (f.initNodes = a), f;
}
mapSignatureEntries(e) {
return Object.keys(e || {}).reduce((t8, o) => (t8[e[o].name] = o, t8), {});
}
mapNode(e) {
let t8 = af(e.op) || this.opMappers[e.op] || {};
e.attr == null && (e.attr = {});
let o = { name: e.name, op: e.op, category: t8.category, inputNames: (e.input || []).map((n) => n.startsWith("^") ? n.slice(1) : n), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t8.outputs };
return t8.inputs != null && (o.inputParams = t8.inputs.reduce((n, s) => (n[s.name] = { type: s.type, inputIndexStart: s.start, inputIndexEnd: s.end }, n), {})), t8.attrs != null && (o.attrParams = t8.attrs.reduce((n, s) => {
let a = s.type, i;
switch (s.type) {
case "string":
i = pf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = pf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "string[]":
i = gf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = gf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "number":
i = lf(e.attr, s.tfName, s.defaultValue || 0), i === void 0 && s.tfDeprecatedName && (i = lf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "number[]":
i = hf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = hf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "bool":
i = cf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = cf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "bool[]":
i = yf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = yf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "shape":
i = ff(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = ff(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "shape[]":
i = xf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = xf(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 "dtype[]":
i = df(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = df(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "func":
i = pT(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = pT(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "tensor":
case "tensors":
break;
default:
throw new Error(`Unsupported param type: ${s.type} for op: ${e.op}`);
}
return n[s.name] = { value: i, type: a }, n;
}, {})), o;
}
mapFunction(e) {
let t8 = e.nodeDef, o = [], n = [], s = {};
t8 != null && (s = t8.reduce((l, m) => (l[m.name] = this.mapNode(m), m.op === "Const" && n.push(l[m.name]), l), {}));
let a = [], i = [];
e.signature.inputArg.forEach((l) => {
let [m] = Os(l.name), d = { name: m, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: NS(l.type), type: "dtype" } }, children: [] };
d.signatureKey = l.name, a.push(d), s[m] = d;
}), Object.keys(s).forEach((l) => {
let m = s[l];
m.inputNames.forEach((d, f) => {
let [h, , g] = Os(d), x = s[h];
if (x.outputs != null) {
let b = x.outputs.indexOf(g);
if (b !== -1) {
let C = `${h}:${b}`;
m.inputNames[f] = C;
}
}
m.inputs.push(x), x.children.push(m);
});
});
let u = e.ret;
e.signature.outputArg.forEach((l) => {
let [m, d] = Os(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((t8, o) => (t8[o.name] = this.mapArgToTensorInfo(o), t8), {}), outputs: e.signature.outputArg.reduce((t8, o) => (t8[o.name] = this.mapArgToTensorInfo(o, e.ret), t8), {}) };
}
mapArgToTensorInfo(e, t8) {
let o = e.name;
return t8 != null && (o = t8[o]), { name: o, dtype: e.type };
}
};
function B5(r) {
let e = A().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 cT(r, e) {
let t8 = Array.isArray(r) ? String.fromCharCode.apply(null, r) : B5(r);
return e ? t8 : t8.toLowerCase();
}
function pf(r, e, t8, o = false) {
let n = r[e];
return n != null ? cT(n.s, o) : t8;
}
function cf(r, e, t8) {
let o = r[e];
return o ? o.b : t8;
}
function lf(r, e, t8) {
let o = r[e] || {}, n = o.i != null ? o.i : o.f != null ? o.f : t8;
return typeof n == "number" ? n : parseInt(n, 10);
}
function NS(r) {
switch (typeof r == "string" && (r = fo[r]), r) {
case fo.DT_FLOAT:
case fo.DT_HALF:
return "float32";
case fo.DT_INT32:
case fo.DT_INT64:
case fo.DT_INT8:
case fo.DT_UINT8:
return "int32";
case fo.DT_BOOL:
return "bool";
case fo.DT_DOUBLE:
return "float32";
case fo.DT_STRING:
return "string";
default:
return null;
}
}
function pT(r, e, t8) {
let o = r[e];
return o && o.func ? o.func.name : t8;
}
function mf(r, e, t8) {
let o = r[e];
return o && o.type ? NS(o.type) : t8;
}
function df(r, e, t8) {
let o = r[e];
return o && o.list && o.list.type ? o.list.type.map((n) => NS(n)) : t8;
}
function lT(r) {
if (!r.unknownRank)
return r.dim != null ? r.dim.map((e) => typeof e.size == "number" ? e.size : parseInt(e.size, 10)) : [];
}
function ff(r, e, t8) {
let o = r[e];
return o && o.shape ? lT(o.shape) : t8;
}
function hf(r, e, t8) {
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)) : t8;
}
function gf(r, e, t8, o = false) {
let n = r[e];
return n && n.list && n.list.s ? n.list.s.map((s) => cT(s, o)) : t8;
}
function xf(r, e, t8) {
let o = r[e];
return o && o.list && o.list.shape ? o.list.shape.map((n) => lT(n)) : t8;
}
function yf(r, e, t8) {
let o = r[e];
return o && o.list && o.list.b ? o.list.b : t8;
}
var bf = class {
constructor(e, t8, o) {
this.node = e, this.tensorMap = t8, 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, t8) {
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 lf(this.node.rawAttrs, e, t8);
if (o.s != null)
return pf(this.node.rawAttrs, e, t8);
if (o.b != null)
return cf(this.node.rawAttrs, e, t8);
if (o.shape != null)
return ff(this.node.rawAttrs, e, t8);
if (o.type != null)
return mf(this.node.rawAttrs, e, t8);
if (o.list != null) {
if (o.list.i != null || o.list.f != null)
return hf(this.node.rawAttrs, e, t8);
if (o.list.s != null)
return gf(this.node.rawAttrs, e, t8);
if (o.list.shape != null)
return xf(this.node.rawAttrs, e, t8);
if (o.list.b != null)
return yf(this.node.rawAttrs, e, t8);
if (o.list.type != null)
return df(this.node.rawAttrs, e, t8);
}
return t8;
}
};
var Je = {};
Ke(Je, { OP_SCOPE_SUFFIX: () => yw, abs: () => Jt, acos: () => xk, acosh: () => yk, add: () => Ce, addN: () => bk, all: () => Ck, any: () => wk, argMax: () => Sk, argMin: () => Ik, asin: () => vk, asinh: () => kk, atan: () => Nk, atan2: () => Tk, atanh: () => _k, avgPool: () => md, avgPool3d: () => Rk, basicLSTMCell: () => Dk, batchNorm: () => su, batchNorm2d: () => Fk, batchNorm3d: () => Pk, batchNorm4d: () => Ok, batchToSpaceND: () => dd, bincount: () => fd, bitwiseAnd: () => Mk, booleanMaskAsync: () => b6, broadcastArgs: () => Lk, broadcastTo: () => au, buffer: () => me, cast: () => qe, ceil: () => Bk, clipByValue: () => zk, clone: () => Ur, complex: () => Er, concat: () => bt, concat1d: () => Vk, concat2d: () => Wk, concat3d: () => Uk, concat4d: () => Gk, conv1d: () => Hk, conv2d: () => iu, conv2dTranspose: () => Kk, conv3d: () => qk, conv3dTranspose: () => Xk, cos: () => Yk, cosh: () => Qk, cosineWindow: () => _l, cumprod: () => Zk, cumsum: () => Jk, denseBincount: () => e2, depthToSpace: () => t2, depthwiseConv2d: () => ic, diag: () => r2, dilation2d: () => o2, div: () => je, divNoNan: () => s2, dot: () => a2, dropout: () => R6, einsum: () => i2, elu: () => yd, enclosingPowerOfTwo: () => Gw, ensureShape: () => u2, equal: () => xd, erf: () => p2, euclideanNorm: () => m2, exp: () => _o, expandDims: () => ai, expm1: () => d2, eye: () => bd, fft: () => cc, fill: () => Ta, floor: () => Cd, floorDiv: () => ld, fused: () => Hw, gather: () => wd, gatherND: () => $6, greater: () => Uu, greaterEqual: () => Sd, ifft: () => Xu, imag: () => pu, image: () => Fj, inTopKAsync: () => A6, irfft: () => Gd, isFinite: () => f2, isInf: () => h2, isNaN: () => g2, leakyRelu: () => Id, less: () => kl, lessEqual: () => uc, linalg: () => Pj, linspace: () => x2, localResponseNormalization: () => y2, log: () => ii, log1p: () => vd, logSigmoid: () => b2, logSoftmax: () => C2, logSumExp: () => Td, logicalAnd: () => Gu, logicalNot: () => _d, logicalOr: () => $d, logicalXor: () => w2, losses: () => Oj, lowerBound: () => S2, matMul: () => Ze, max: () => _a, maxPool: () => Rd, maxPool3d: () => I2, maxPoolWithArgmax: () => v2, maximum: () => Dd, mean: () => Hu, meshgrid: () => k2, min: () => vl, minimum: () => Ku, mirrorPad: () => N2, mod: () => T2, moments: () => _2, movingAverage: () => S6, mul: () => se, multiRNNCell: () => $2, multinomial: () => E2, neg: () => pr, norm: () => Wu, notEqual: () => Ad, oneHot: () => Tl, ones: () => $a, onesLike: () => R2, op: () => N, outerProduct: () => D2, pad: () => Ea, pad1d: () => A2, pad2d: () => F2, pad3d: () => P2, pad4d: () => O2, pool: () => M2, pow: () => si, prelu: () => Pd, print: () => cd, prod: () => L2, raggedGather: () => B2, raggedRange: () => z2, raggedTensorToTensor: () => V2, rand: () => W2, randomGamma: () => p1, randomNormal: () => Vd, randomStandardNormal: () => c1, randomUniform: () => pc, randomUniformInt: () => l1, range: () => cu, real: () => ui, reciprocal: () => m1, relu: () => lu, relu6: () => Wd, reshape: () => W, reverse: () => lo, reverse1d: () => d1, reverse2d: () => f1, reverse3d: () => h1, reverse4d: () => g1, rfft: () => lc, round: () => Ud, rsqrt: () => x1, scalar: () => ke, scatterND: () => v6, searchSorted: () => Nl, selu: () => y1, separableConv2d: () => b1, setdiff1dAsync: () => C1, sigmoid: () => Na, sign: () => w1, signal: () => Aj, sin: () => S1, sinh: () => I1, slice: () => Xe, slice1d: () => v1, slice2d: () => k1, slice3d: () => N1, slice4d: () => T1, softmax: () => _1, softplus: () => Nd, spaceToBatchND: () => Fd, sparse: () => Mj, sparseToDense: () => T6, spectral: () => Dj, split: () => pi, sqrt: () => Dr, square: () => er, squaredDifference: () => Hd, squeeze: () => mc, stack: () => kr, step: () => Kd, stridedSlice: () => $1, string: () => Lj, sub: () => Te, sum: () => ot, tan: () => E1, tanh: () => Il, tensor: () => ir, tensor1d: () => xr, tensor2d: () => mu, tensor3d: () => qd, tensor4d: () => R1, tensor5d: () => D1, tensor6d: () => A1, tensorScatterUpdate: () => P1, tile: () => uu, topk: () => O1, transpose: () => fc, truncatedNormal: () => M1, unique: () => L1, unsortedSegmentSum: () => B1, unstack: () => mo, upperBound: () => z1, variable: () => V1, where: () => co, whereAsync: () => Xd, zeros: () => Gr, zerosLike: () => Ht });
var mT = (r, e, t8, o = Je) => {
switch (r.op) {
case "BiasAdd":
case "AddV2":
case "Add":
return [o.add(I("a", r, e, t8), I("b", r, e, t8))];
case "AddN":
return [o.addN(I("tensors", r, e, t8))];
case "FloorMod":
case "Mod":
return [o.mod(I("a", r, e, t8), I("b", r, e, t8))];
case "Mul":
return [o.mul(I("a", r, e, t8), I("b", r, e, t8))];
case "RealDiv":
case "Div":
return [o.div(I("a", r, e, t8), I("b", r, e, t8))];
case "DivNoNan":
return [o.divNoNan(I("a", r, e, t8), I("b", r, e, t8))];
case "FloorDiv":
return [o.floorDiv(I("a", r, e, t8), I("b", r, e, t8))];
case "Sub":
return [o.sub(I("a", r, e, t8), I("b", r, e, t8))];
case "Minimum":
return [o.minimum(I("a", r, e, t8), I("b", r, e, t8))];
case "Maximum":
return [o.maximum(I("a", r, e, t8), I("b", r, e, t8))];
case "Pow":
return [o.pow(I("a", r, e, t8), I("b", r, e, t8))];
case "SquaredDifference":
return [o.squaredDifference(I("a", r, e, t8), I("b", r, e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var dT = (r, e, t8, o = Je) => {
switch (r.op) {
case "Abs":
case "ComplexAbs":
return [o.abs(I("x", r, e, t8))];
case "Acos":
return [o.acos(I("x", r, e, t8))];
case "Acosh":
return [o.acosh(I("x", r, e, t8))];
case "Asin":
return [o.asin(I("x", r, e, t8))];
case "Asinh":
return [o.asinh(I("x", r, e, t8))];
case "Atan":
return [o.atan(I("x", r, e, t8))];
case "Atan2":
return [o.atan2(I("x", r, e, t8), I("y", r, e, t8))];
case "Atanh":
return [o.atanh(I("x", r, e, t8))];
case "Ceil":
return [o.ceil(I("x", r, e, t8))];
case "Complex":
return [o.complex(I("real", r, e, t8), I("imag", r, e, t8))];
case "Cos":
return [o.cos(I("x", r, e, t8))];
case "Cosh":
return [o.cosh(I("x", r, e, t8))];
case "Elu":
return [o.elu(I("x", r, e, t8))];
case "Erf":
return [o.erf(I("x", r, e, t8))];
case "Exp":
return [o.exp(I("x", r, e, t8))];
case "Expm1":
return [o.expm1(I("x", r, e, t8))];
case "Floor":
return [o.floor(I("x", r, e, t8))];
case "Log":
return [o.log(I("x", r, e, t8))];
case "Log1p":
return [o.log1p(I("x", r, e, t8))];
case "Imag":
return [o.imag(I("x", r, e, t8))];
case "Neg":
return [o.neg(I("x", r, e, t8))];
case "Reciprocal":
return [o.reciprocal(I("x", r, e, t8))];
case "Real":
return [o.real(I("x", r, e, t8))];
case "Relu":
return [o.relu(I("x", r, e, t8))];
case "Round":
return [o.round(I("x", r, e, t8))];
case "Selu":
return [o.selu(I("x", r, e, t8))];
case "Sigmoid":
return [o.sigmoid(I("x", r, e, t8))];
case "Sin":
return [o.sin(I("x", r, e, t8))];
case "Sign":
return [o.sign(I("x", r, e, t8))];
case "Sinh":
return [o.sinh(I("x", r, e, t8))];
case "Softplus":
return [o.softplus(I("x", r, e, t8))];
case "Sqrt":
return [o.sqrt(I("x", r, e, t8))];
case "Square":
return [o.square(I("x", r, e, t8))];
case "Tanh":
return [o.tanh(I("x", r, e, t8))];
case "Tan":
return [o.tan(I("x", r, e, t8))];
case "ClipByValue":
return [o.clipByValue(I("x", r, e, t8), I("clipValueMin", r, e, t8), I("clipValueMax", r, e, t8))];
case "Relu6":
return [o.relu6(I("x", r, e, t8))];
case "Rsqrt":
return [o.rsqrt(Bt(r.inputNames[0], e, t8))];
case "LeakyRelu":
return [o.leakyRelu(I("x", r, e, t8), I("alpha", r, e, t8))];
case "Prelu":
return [o.prelu(I("x", r, e, t8), I("alpha", r, e, t8))];
case "IsNan":
return [o.isNaN(Bt(r.inputNames[0], e, t8))];
case "IsInf":
return [o.isInf(Bt(r.inputNames[0], e, t8))];
case "IsFinite":
return [o.isFinite(Bt(r.inputNames[0], e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
function Hr(r, e, t8 = "") {
if (!(typeof r == "number" || typeof e == "number")) {
y.assert(r.length === e.length, () => t8 + ` 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, () => t8 + ` Shapes ${r} and ${e} must match`);
}
}
}
function fT(r) {
return !(typeof r == "number" || r.some((e) => e < 0));
}
function hc(r, e, t8) {
let o = Cf(r, t8), n = !fT(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 = Cf(s.shape, o);
}), !fT(o))
throw new Error(`Non-fully-defined elementShape: ${o}`);
return o;
}
function Cf(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 t8 = [];
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}`);
t8[o] = n >= 0 ? n : s;
}
return t8;
}
var wf = class {
constructor(e, t8, o, n, s, a, i) {
this.name = e, this.dtype = t8, this.maxSize = o, this.elementShape = n, this.identicalElementShapes = s, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = ke(0), Rr(this.idTensor);
}
get id() {
return this.idTensor.id;
}
get closed() {
return this.closed_;
}
clearAndClose(e) {
this.tensors.forEach((t8) => {
(e == null || !e.has(t8.tensor.id)) && t8.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 t8 = this.tensors[e];
if (t8.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 && (t8.cleared = true), t8.read = true, t8.tensor;
}
readMany(e) {
return e.map((t8) => this.read(t8));
}
write(e, t8) {
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 (t8.dtype !== this.dtype)
throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
because the value dtype is ${t8.dtype}, but TensorArray dtype is ${this.dtype}.`);
if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t8.shape), Hr(this.elementShape, t8.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 = t8, Rr(t8), o.written = true, this.tensors[e] = o;
}
writeMany(e, t8) {
if (e.length !== t8.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: ${t8.length}.`);
e.forEach((o, n) => this.write(o, t8[n]));
}
gather(e, t8) {
if (t8 && t8 !== this.dtype)
throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t8}`);
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 Hr(this.elementShape, o[0].shape, "TensorArray shape mismatch: "), kr(o, 0);
}
concat(e) {
if (e && e !== this.dtype)
throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);
if (this.size() === 0)
return ir([], [0].concat(this.elementShape));
let t8 = [];
for (let n = 0; n < this.size(); n++)
t8.push(n);
let o = this.readMany(t8);
return Hr(this.elementShape, o[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`), bt(o, 0);
}
scatter(e, t8) {
if (t8.dtype !== this.dtype)
throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t8.dtype}`);
if (e.length !== t8.shape[0])
throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t8.shape[0]}`);
let o = Math.max(...e);
if (!this.dynamicSize && o >= this.maxSize)
throw new Error(`Max index must be < array size (${o} vs. ${this.maxSize})`);
this.writeMany(e, mo(t8, 0));
}
split(e, t8) {
if (t8.dtype !== this.dtype)
throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t8.dtype}`);
let o = 0, n = e.map((p) => (o += p, o));
if (o !== t8.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: ${t8.shape}`);
if (!this.dynamicSize && e.length !== this.maxSize)
throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);
let s = o === 0 ? 0 : t8.size / o, a = [];
De(() => {
t8 = W(t8, [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(Xe(t8, c, l), this.elementShape);
}
return a;
});
let i = [];
for (let p = 0; p < e.length; p++)
i[p] = p;
this.writeMany(i, a);
}
};
var di = class {
get id() {
return this.idTensor.id;
}
constructor(e, t8, o, n = -1) {
this.tensors = e, this.elementShape = t8, 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}`);
Hr(t8, s.shape, "TensorList shape mismatch: "), Rr(s);
}), this.idTensor = ke(0), this.maxNumElements = n, Rr(this.idTensor);
}
copy() {
return new di([...this.tensors], this.elementShape, this.elementDtype);
}
clearAndClose(e) {
this.tensors.forEach((t8) => {
(e == null || !e.has(t8.id)) && t8.dispose();
}), this.tensors.length = 0, this.idTensor.dispose();
}
size() {
return this.tensors.length;
}
stack(e, t8, o = -1) {
if (t8 !== this.elementDtype)
throw new Error(`Invalid data types; op elements ${t8}, 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.`);
Hr(e, this.elementShape, "TensorList shape mismatch: ");
let n = hc(this.elementShape, this.tensors, e);
return De(() => {
let s = this.tensors.map((a) => W(a, n));
return kr(s, 0);
});
}
popBack(e, t8) {
if (t8 !== this.elementDtype)
throw new Error(`Invalid data types; op elements ${t8}, but list elements ${this.elementDtype}`);
if (this.size() === 0)
throw new Error("Trying to pop from an empty list.");
let o = hc(this.elementShape, this.tensors, e), n = this.tensors.pop();
return n.kept = false, Hr(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 (Hr(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size())
throw new Error("Trying to push element into a full list.");
Rr(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 t8 = new di([], this.elementShape, this.elementDtype, this.maxNumElements);
t8.tensors.length = e;
for (let o = 0; o < Math.min(this.tensors.length, e); ++o)
t8.tensors[o] = this.tensors[o];
return t8;
}
getItem(e, t8, 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.`);
Hr(this.tensors[e].shape, t8, "TensorList shape mismatch: ");
let n = hc(this.elementShape, this.tensors, t8);
return W(this.tensors[e], n);
}
setItem(e, t8) {
if (t8.dtype !== this.elementDtype)
throw new Error(`Invalid data types; op elements ${t8.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.`);
Hr(this.elementShape, t8.shape, "TensorList shape mismatch: "), Rr(t8), this.tensors[e] != null && (this.tensors[e].kept = false), this.tensors[e] = t8;
}
gather(e, t8, o) {
if (t8 !== this.elementDtype)
throw new Error(`Invalid data types; op elements ${t8}, but list elements ${this.elementDtype}`);
Hr(this.elementShape, o, "TensorList shape mismatch: "), e = e.slice(0, this.size());
let n = hc(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 kr(s, 0);
});
}
concat(e, t8) {
if (e && e !== this.elementDtype)
throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);
Hr(this.elementShape, t8, "TensorList shape mismatch: ");
let o = hc(this.elementShape, this.tensors, t8);
return this.size() === 0 ? ir([], [0].concat(o)) : De(() => {
let n = this.tensors.map((s) => W(s, o));
return bt(n, 0);
});
}
};
function hT(r, e, t8) {
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 !== t8)
throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t8}`);
let n = r.shape.slice(1);
Hr(n, e, "TensorList shape mismatch: ");
let s = mo(r);
return new di(s, e, o);
}
function gT(r, e, t8, o) {
return new di([], r, e, o);
}
function xT(r, e, t8, 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 di([], t8, r.dtype, o), a = mo(r, 0);
return e.forEach((i, p) => {
s.setItem(i, a[p]);
}), s;
}
function yT(r, e, t8) {
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 = Cf(s, t8), 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(Xe(r, d, f), a);
}
return r.dispose(), c;
}), u = new di([], t8, r.dtype, e.length);
for (let c = 0; c < p.length; c++)
u.setItem(c, p[c]);
return u;
}
var bT = async (r, e, t8) => {
switch (r.op) {
case "If":
case "StatelessIf": {
let o = I("thenBranch", r, e, t8), n = I("elseBranch", r, e, t8), s = I("cond", r, e, t8), a = I("args", r, e, t8);
return (await s.data())[0] ? t8.functionMap[o].executeFunctionAsync(a, t8.tensorArrayMap, t8.tensorListMap) : t8.functionMap[n].executeFunctionAsync(a, t8.tensorArrayMap, t8.tensorListMap);
}
case "While":
case "StatelessWhile": {
let o = I("body", r, e, t8), n = I("cond", r, e, t8), s = I("args", r, e, t8), a = await t8.functionMap[n].executeFunctionAsync(s, t8.tensorArrayMap, t8.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 t8.functionMap[o].executeFunctionAsync(u, t8.tensorArrayMap, t8.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 t8.functionMap[n].executeFunctionAsync(u, t8.tensorArrayMap, t8.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, t8);
return [Ms(o)];
}
case "Switch": {
let o = I("pred", r, e, t8), n = I("data", r, e, t8);
return n.kept || (n = Ms(n)), (await o.data())[0] ? [void 0, n] : [n, void 0];
}
case "Merge": {
let o = r.inputNames.find((n) => Bt(n, e, t8) !== void 0);
if (o) {
let n = Bt(o, e, t8);
return [Ms(n)];
}
return;
}
case "Enter": {
let o = I("frameName", r, e, t8), n = I("tensor", r, e, t8);
return t8.enterFrame(o), [Ms(n)];
}
case "Exit": {
let o = I("tensor", r, e, t8);
return t8.exitFrame(), [Ms(o)];
}
case "NextIteration": {
let o = I("tensor", r, e, t8);
return t8.nextIteration(), [Ms(o)];
}
case "TensorArrayV3": {
let o = I("size", r, e, t8), n = I("dtype", r, e, t8), s = I("elementShape", r, e, t8), a = I("dynamicSize", r, e, t8), i = I("clearAfterRead", r, e, t8), p = I("identicalElementShapes", r, e, t8), u = I("name", r, e, t8), c = new wf(u, n, o, s, p, a, i);
return t8.addTensorArray(c), [c.idTensor, ke(1)];
}
case "TensorArrayWriteV3": {
let o = I("tensorArrayId", r, e, t8), n = I("index", r, e, t8), s = I("tensor", r, e, t8), a = t8.getTensorArray(o.id);
return a.write(n, s), [a.idTensor];
}
case "TensorArrayReadV3": {
let o = I("tensorArrayId", r, e, t8), n = I("index", r, e, t8);
return [t8.getTensorArray(o.id).read(n)];
}
case "TensorArrayGatherV3": {
let o = I("tensorArrayId", r, e, t8), n = I("indices", r, e, t8), s = I("dtype", r, e, t8);
return [t8.getTensorArray(o.id).gather(n, s)];
}
case "TensorArrayScatterV3": {
let o = I("tensorArrayId", r, e, t8), n = I("indices", r, e, t8), s = I("tensor", r, e, t8), a = t8.getTensorArray(o.id);
return a.scatter(n, s), [a.idTensor];
}
case "TensorArrayConcatV3": {
let o = I("tensorArrayId", r, e, t8), n = t8.getTensorArray(o.id), s = I("dtype", r, e, t8);
return [n.concat(s)];
}
case "TensorArraySplitV3": {
let o = I("tensorArrayId", r, e, t8), n = I("tensor", r, e, t8), s = I("lengths", r, e, t8), a = t8.getTensorArray(o.id);
return a.split(s, n), [a.idTensor];
}
case "TensorArraySizeV3": {
let o = I("tensorArrayId", r, e, t8), n = t8.getTensorArray(o.id);
return [ke(n.size(), "int32")];
}
case "TensorArrayCloseV3": {
let o = I("tensorArrayId", r, e, t8), n = t8.getTensorArray(o.id);
return n.clearAndClose(), [n.idTensor];
}
case "TensorListSetItem": {
let o = I("tensorListId", r, e, t8), n = I("index", r, e, t8), s = I("tensor", r, e, t8), a = t8.getTensorList(o.id);
return a.setItem(n, s), [a.idTensor];
}
case "TensorListGetItem": {
let o = I("tensorListId", r, e, t8), n = I("index", r, e, t8), s = I("elementShape", r, e, t8), a = I("elementDType", r, e, t8);
return [t8.getTensorList(o.id).getItem(n, s, a)];
}
case "TensorListScatterV2":
case "TensorListScatter": {
let o = I("indices", r, e, t8), n = I("tensor", r, e, t8), s = I("elementShape", r, e, t8), a = I("numElements", r, e, t8), i = xT(n, o, s, a);
return t8.addTensorList(i), [i.idTensor];
}
case "TensorListReserve":
case "EmptyTensorList": {
let o = I("elementShape", r, e, t8), n = I("elementDType", r, e, t8), s;
r.op === "TensorListReserve" ? s = "numElements" : s = "maxNumElements";
let a = I(s, r, e, t8), i = r.op === "TensorListReserve" ? -1 : a, p = gT(o, n, a, i);
return t8.addTensorList(p), [p.idTensor];
}
case "TensorListGather": {
let o = I("tensorListId", r, e, t8), n = I("indices", r, e, t8), s = I("elementShape", r, e, t8), a = I("elementDType", r, e, t8);
return [t8.getTensorList(o.id).gather(n, a, s)];
}
case "TensorListStack": {
let o = I("tensorListId", r, e, t8), n = I("elementShape", r, e, t8), s = I("elementDType", r, e, t8), a = I("numElements", r, e, t8);
return [t8.getTensorList(o.id).stack(n, s, a)];
}
case "TensorListFromTensor": {
let o = I("tensor", r, e, t8), n = I("elementShape", r, e, t8), s = I("elementDType", r, e, t8), a = hT(o, n, s);
return t8.addTensorList(a), [a.idTensor];
}
case "TensorListConcat":
case "TensorListConcatV2": {
let o = I("tensorListId", r, e, t8), n = t8.getTensorList(o.id), s = I("dtype", r, e, t8), a = I("elementShape", r, e, t8);
return [n.concat(s, a)];
}
case "TensorListPushBack": {
let o = I("tensorListId", r, e, t8), n = I("tensor", r, e, t8), s = t8.getTensorList(o.id);
return s.pushBack(n), [s.idTensor];
}
case "TensorListPopBack": {
let o = I("tensorListId", r, e, t8), n = I("elementShape", r, e, t8), s = I("elementDType", r, e, t8);
return [t8.getTensorList(o.id).popBack(n, s)];
}
case "TensorListSplit": {
let o = I("tensor", r, e, t8), n = I("elementShape", r, e, t8), s = I("lengths", r, e, t8), a = yT(o, s, n);
return t8.addTensorList(a), [a.idTensor];
}
case "TensorListLength": {
let o = I("tensorListId", r, e, t8), n = t8.getTensorList(o.id);
return [ke(n.size(), "int32")];
}
case "TensorListResize": {
let o = I("tensorListId", r, e, t8), n = I("size", r, e, t8), a = t8.getTensorList(o.id).resize(n);
return t8.addTensorList(a), [a.idTensor];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
function CT(r, e, t8) {
let [o, n] = I("fusedOps", r, e, t8), s = o === "biasadd", a = !s, i = n === "prelu", p = o === "fusedbatchnorm", u = I("numArgs", r, e, t8);
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, t8), l = Al(r, e, t8), m = I("dataFormat", r, e, t8).toUpperCase(), d = I("dilations", r, e, t8), [f, h] = I("args", r, e, t8);
a && (h = f, f = void 0);
let g = I("leakyreluAlpha", r, e, t8);
return { stride: c, pad: l, dataFormat: m, dilations: d, biasArg: f, preluArg: h, activationFunc: n, leakyreluAlpha: g };
}
var wT = (r, e, t8, o = Je) => {
switch (r.op) {
case "Conv1D": {
let n = I("stride", r, e, t8), s = I("pad", r, e, t8), a = I("dataFormat", r, e, t8).toUpperCase(), i = I("dilation", r, e, t8);
return [o.conv1d(I("x", r, e, t8), I("filter", r, e, t8), n, s, a, i)];
}
case "Conv2D": {
let n = I("strides", r, e, t8), s = Al(r, e, t8), a = I("dataFormat", r, e, t8).toUpperCase(), i = I("dilations", r, e, t8);
return [o.conv2d(I("x", r, e, t8), I("filter", r, e, t8), [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 } = CT(r, e, t8);
return [o.fused.conv2d({ x: I("x", r, e, t8), filter: I("filter", r, e, t8), 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 } = CT(r, e, t8);
return [o.fused.depthwiseConv2d({ x: I("x", r, e, t8), filter: I("filter", r, e, t8), 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, t8), s = I("strides", r, e, t8), a = Al(r, e, t8);
return [o.conv2dTranspose(I("x", r, e, t8), I("filter", r, e, t8), n, [s[1], s[2]], a)];
}
case "DepthwiseConv2dNative":
case "DepthwiseConv2d": {
let n = I("strides", r, e, t8), s = Al(r, e, t8), a = I("dilations", r, e, t8), i = I("dataFormat", r, e, t8).toUpperCase();
return [o.depthwiseConv2d(I("input", r, e, t8), I("filter", r, e, t8), [n[1], n[2]], s, i, [a[1], a[2]])];
}
case "Conv3D": {
let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("dataFormat", r, e, t8).toUpperCase(), i = I("dilations", r, e, t8);
return [o.conv3d(I("x", r, e, t8), I("filter", r, e, t8), [n[1], n[2], n[3]], s, a, [i[1], i[2], i[3]])];
}
case "AvgPool": {
let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("kernelSize", r, e, t8);
return [o.avgPool(I("x", r, e, t8), [a[1], a[2]], [n[1], n[2]], s)];
}
case "MaxPool": {
let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("kernelSize", r, e, t8);
return [o.maxPool(I("x", r, e, t8), [a[1], a[2]], [n[1], n[2]], s)];
}
case "MaxPoolWithArgmax": {
let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("kernelSize", r, e, t8), i = I("includeBatchInIndex", r, e, t8), { result: p, indexes: u } = o.maxPoolWithArgmax(I("x", r, e, t8), [a[1], a[2]], [n[1], n[2]], s, i);
return [p, u];
}
case "AvgPool3D": {
let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("kernelSize", r, e, t8);
return [o.avgPool3d(I("x", r, e, t8), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)];
}
case "MaxPool3D": {
let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("kernelSize", r, e, t8);
return [o.maxPool3d(I("x", r, e, t8), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)];
}
case "Dilation2D": {
let n = I("strides", r, e, t8), s = I("pad", r, e, t8), a = I("dilations", r, e, t8), i = n[1], p = n[2], u = a[1], c = a[2];
return [o.dilation2d(I("x", r, e, t8), I("filter", r, e, t8), [i, p], s, [u, c], "NHWC")];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var ST = (r, e, t8, o = Je) => {
switch (r.op) {
case "Fill": {
let n = I("shape", r, e, t8), s = I("dtype", r, e, t8), a = I("value", r, e, t8);
return [o.fill(n, a, s)];
}
case "LinSpace": {
let n = I("start", r, e, t8), s = I("stop", r, e, t8), a = I("num", r, e, t8);
return [o.linspace(n, s, a)];
}
case "Multinomial": {
let n = I("logits", r, e, t8), s = I("numSamples", r, e, t8), a = I("seed", r, e, t8);
return [o.multinomial(n, s, a)];
}
case "OneHot": {
let n = I("indices", r, e, t8), s = I("depth", r, e, t8), a = I("onValue", r, e, t8), i = I("offValue", r, e, t8), p = I("dtype", r, e, t8);
return [o.oneHot(n, s, a, i, p)];
}
case "Ones":
return [o.ones(I("shape", r, e, t8), I("dtype", r, e, t8))];
case "OnesLike":
return [o.onesLike(I("x", r, e, t8))];
case "RandomStandardNormal":
return [o.randomStandardNormal(I("shape", r, e, t8), I("dtype", r, e, t8), I("seed", r, e, t8))];
case "RandomUniform":
return [o.randomUniform(I("shape", r, e, t8), I("minval", r, e, t8), I("maxval", r, e, t8), I("dtype", r, e, t8))];
case "RandomUniformInt":
return [o.randomUniformInt(I("shape", r, e, t8), I("minval", r, e, t8), I("maxval", r, e, t8), I("seed", r, e, t8))];
case "Range": {
let n = I("start", r, e, t8), s = I("stop", r, e, t8), a = I("step", r, e, t8);
return [o.range(n, s, a, I("dtype", r, e, t8))];
}
case "TruncatedNormal": {
let n = I("shape", r, e, t8), s = I("mean", r, e, t8), a = I("stdDev", r, e, t8), i = I("seed", r, e, t8);
return [o.truncatedNormal(n, s, a, I("dtype", r, e, t8), i)];
}
case "Zeros":
return [o.zeros(I("shape", r, e, t8), I("dtype", r, e, t8))];
case "ZerosLike":
return [o.zerosLike(I("x", r, e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
function TS(r, e, t8) {
let o = I("boxes", r, e, t8), n = I("scores", r, e, t8), s = I("maxOutputSize", r, e, t8), a = I("iouThreshold", r, e, t8), i = I("scoreThreshold", r, e, t8), p = I("softNmsSigma", r, e, t8);
return { boxes: o, scores: n, maxOutputSize: s, iouThreshold: a, scoreThreshold: i, softNmsSigma: p };
}
var IT = async (r, e, t8, o, n = Je) => {
switch (r.op) {
case "NonMaxSuppressionV5": {
let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u, softNmsSigma: c } = TS(r, e, t8), 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 } = TS(r, e, t8), c = I("padToMaxOutputSize", r, e, t8), 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 } = TS(r, e, t8);
return [await n.image.nonMaxSuppressionAsync(s, a, i, p, u)];
}
case "Where": {
let s = n.cast(I("condition", r, e, t8), "bool"), a = [await n.whereAsync(s)];
return s.dispose(), a;
}
case "ListDiff":
return n.setdiff1dAsync(I("x", r, e, t8), I("y", r, e, t8));
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var vT = (r, e, t8, o = Je) => {
switch (r.op) {
case "LowerBound": {
let n = I("sortedSequence", r, e, t8), s = I("values", r, e, t8);
return [o.lowerBound(n, s)];
}
case "TopKV2": {
let n = I("x", r, e, t8), s = I("k", r, e, t8), a = I("sorted", r, e, t8), i = o.topk(n, s, a);
return [i.values, i.indices];
}
case "UpperBound": {
let n = I("sortedSequence", r, e, t8), s = I("values", r, e, t8);
return [o.upperBound(n, s)];
}
case "Unique": {
let n = I("x", r, e, t8), s = o.unique(n);
return [s.values, s.indices];
}
case "UniqueV2": {
let n = I("x", r, e, t8), s = I("axis", r, e, t8), a = o.unique(n, s);
return [a.values, a.indices];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var kT = (r, e, t8, o = Je) => {
switch (r.op) {
case "Const":
return e[r.name];
case "PlaceholderWithDefault":
let n = I("default", r, e, t8);
return [Bt(r.name, e, t8) || n];
case "Placeholder":
return [Bt(r.name, e, t8)];
case "Identity":
case "StopGradient":
case "FakeQuantWithMinMaxVars": {
let c = I("x", r, e, t8);
return [Ms(c)];
}
case "IdentityN":
return I("x", r, e, t8).map((c) => Ms(c));
case "Snapshot":
let s = I("x", r, e, t8);
return [Ms(s)];
case "Shape":
return [o.tensor1d(I("x", r, e, t8).shape, "int32")];
case "ShapeN":
return I("x", r, e, t8).map((c) => o.tensor1d(c.shape));
case "Size":
return [o.scalar(I("x", r, e, t8).size, "int32")];
case "Rank":
return [o.scalar(I("x", r, e, t8).rank, "int32")];
case "NoOp":
return [o.scalar(1)];
case "Print":
let a = I("x", r, e, t8), i = I("data", r, e, t8), p = I("message", r, e, t8), u = I("summarize", r, e, t8);
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 Sf = class {
get id() {
return this.handle.id;
}
constructor(e, t8) {
this.keyDType = e, this.valueDType = t8, this.handle = ke(0), this.tensorMap = /* @__PURE__ */ new Map(), Rr(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, t8) {
this.checkKeyAndValueTensor(e, t8);
let o = await e.data();
return this.tensorMap.forEach((n) => n.dispose()), this.tensorMap.clear(), De(() => {
let n = mo(t8), 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];
Rr(u), this.tensorMap.set(p, u);
}
return this.handle;
});
}
async find(e, t8) {
this.checkKeyAndValueTensor(e, t8);
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, t8);
n.push(i);
}
return kr(n);
});
}
findWithDefault(e, t8) {
let o = this.tensorMap.get(e);
return o != null ? o : t8;
}
checkKeyAndValueTensor(e, t8) {
if (e.dtype !== this.keyDType)
throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);
if (t8.dtype !== this.valueDType)
throw new Error(`Expect value dtype ${this.valueDType}, but got ${t8.dtype}`);
}
};
var NT = async (r, e, t8, 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, t8), a = I("valueDType", r, e, t8), i = new Sf(s, a);
return o.addHashTable(r.name, i), [i.handle];
}
}
case "InitializeTable":
case "InitializeTableV2":
case "LookupTableImport":
case "LookupTableImportV2": {
let n = I("tableHandle", r, e, t8, o), s = I("keys", r, e, t8), a = I("values", r, e, t8);
return [await o.getHashTableById(n.id).import(s, a)];
}
case "LookupTableFind":
case "LookupTableFindV2": {
let n = I("tableHandle", r, e, t8, o), s = I("keys", r, e, t8), a = I("defaultValue", r, e, t8);
return [await o.getHashTableById(n.id).find(s, a)];
}
case "LookupTableSize":
case "LookupTableSizeV2": {
let n = I("tableHandle", r, e, t8, o);
return [o.getHashTableById(n.id).tensorSize()];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var TT = (r, e, t8, o = Je) => {
switch (r.op) {
case "ResizeBilinear": {
let n = I("images", r, e, t8), s = I("size", r, e, t8), a = I("alignCorners", r, e, t8), i = I("halfPixelCenters", r, e, t8);
return [o.image.resizeBilinear(n, [s[0], s[1]], a, i)];
}
case "ResizeNearestNeighbor": {
let n = I("images", r, e, t8), s = I("size", r, e, t8), a = I("alignCorners", r, e, t8), i = I("halfPixelCenters", r, e, t8);
return [o.image.resizeNearestNeighbor(n, [s[0], s[1]], a, i)];
}
case "CropAndResize": {
let n = I("image", r, e, t8), s = I("boxes", r, e, t8), a = I("boxInd", r, e, t8), i = I("cropSize", r, e, t8), p = I("method", r, e, t8), u = I("extrapolationValue", r, e, t8);
return [o.image.cropAndResize(n, s, a, i, p, u)];
}
case "ImageProjectiveTransformV3": {
let n = I("images", r, e, t8), s = I("transforms", r, e, t8), a = I("outputShape", r, e, t8), i = I("fillValue", r, e, t8), p = I("interpolation", r, e, t8), u = I("fillMode", r, e, t8);
return [o.image.transform(n, s, p.toLowerCase(), u.toLowerCase(), i, a)];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var _T = (r, e, t8, o = Je) => {
switch (r.op) {
case "Equal":
return [o.equal(I("a", r, e, t8), I("b", r, e, t8))];
case "NotEqual":
return [o.notEqual(I("a", r, e, t8), I("b", r, e, t8))];
case "Greater":
return [o.greater(I("a", r, e, t8), I("b", r, e, t8))];
case "GreaterEqual":
return [o.greaterEqual(I("a", r, e, t8), I("b", r, e, t8))];
case "Less":
return [o.less(I("a", r, e, t8), I("b", r, e, t8))];
case "LessEqual":
return [o.lessEqual(I("a", r, e, t8), I("b", r, e, t8))];
case "LogicalAnd":
return [o.logicalAnd(I("a", r, e, t8), I("b", r, e, t8))];
case "LogicalNot":
return [o.logicalNot(I("a", r, e, t8))];
case "LogicalOr":
return [o.logicalOr(I("a", r, e, t8), I("b", r, e, t8))];
case "Select":
case "SelectV2":
return [o.where(I("condition", r, e, t8), I("a", r, e, t8), I("b", r, e, t8))];
case "BitwiseAnd":
return [o.bitwiseAnd(I("a", r, e, t8), I("b", r, e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var $T = (r, e, t8, o = Je) => {
switch (r.op) {
case "BatchMatMul":
case "BatchMatMulV2":
case "MatMul":
return [o.matMul(I("a", r, e, t8), I("b", r, e, t8), I("transposeA", r, e, t8), I("transposeB", r, e, t8))];
case "Einsum":
return [o.einsum(I("equation", r, e, t8), ...I("tensors", r, e, t8))];
case "Transpose":
return [o.transpose(I("x", r, e, t8), I("perm", r, e, t8))];
case "_FusedMatMul":
let [n, s] = I("fusedOps", r, e, t8), a = n === "biasadd", i = s === "prelu", p = I("numArgs", r, e, t8), u = I("leakyreluAlpha", r, e, t8);
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, t8);
return [o.fused.matMul({ a: I("a", r, e, t8), b: I("b", r, e, t8), transposeA: I("transposeA", r, e, t8), transposeB: I("transposeB", r, e, t8), bias: c, activation: s, preluActivationWeights: l, leakyreluAlpha: u })];
case "MatrixBandPart":
return [o.linalg.bandPart(I("a", r, e, t8), I("numLower", r, e, t8), I("numUpper", r, e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var ET = (r, e, t8, o = Je) => {
switch (r.op) {
case "EuclideanNorm":
return [o.euclideanNorm(I("x", r, e, t8), I("axis", r, e, t8), I("keepDims", r, e, t8))];
case "FusedBatchNorm":
case "FusedBatchNormV2":
return [o.batchNorm(I("x", r, e, t8), I("mean", r, e, t8), I("variance", r, e, t8), I("offset", r, e, t8), I("scale", r, e, t8), I("epsilon", r, e, t8))];
case "FusedBatchNormV3":
return [o.batchNorm(I("x", r, e, t8), I("mean", r, e, t8), I("variance", r, e, t8), I("offset", r, e, t8), I("scale", r, e, t8), I("epsilon", r, e, t8))];
case "LRN":
return [o.localResponseNormalization(I("x", r, e, t8), I("radius", r, e, t8), I("bias", r, e, t8), I("alpha", r, e, t8), I("beta", r, e, t8))];
case "Softmax":
return [o.softmax(I("x", r, e, t8))];
case "LogSoftmax":
return [o.logSoftmax(I("x", r, e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var RT = (r, e, t8, o = Je) => {
switch (r.op) {
case "RaggedGather": {
let { outputNestedSplits: n, outputDenseValues: s } = o.raggedGather(I("paramsNestedSplits", r, e, t8), I("paramsDenseValues", r, e, t8), I("indices", r, e, t8), I("outputRaggedRank", r, e, t8));
return n.concat(s);
}
case "RaggedRange": {
let { rtNestedSplits: n, rtDenseValues: s } = o.raggedRange(I("starts", r, e, t8), I("limits", r, e, t8), I("splits", r, e, t8));
return [n, s];
}
case "RaggedTensorToTensor":
return [o.raggedTensorToTensor(I("shape", r, e, t8), I("values", r, e, t8), I("defaultValue", r, e, t8), I("rowPartitionTensors", r, e, t8), I("rowPartitionTypes", r, e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var DT = (r, e, t8, o = Je) => {
switch (r.op) {
case "Max": {
let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8);
return [o.max(I("x", r, e, t8), i, p)];
}
case "Mean": {
let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8);
return [o.mean(I("x", r, e, t8), i, p)];
}
case "Min": {
let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8);
return [o.min(I("x", r, e, t8), i, p)];
}
case "Sum": {
let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8);
return [o.sum(I("x", r, e, t8), i, p)];
}
case "All": {
let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8);
return [o.all(I("x", r, e, t8), i, p)];
}
case "Any": {
let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8);
return [o.any(I("x", r, e, t8), i, p)];
}
case "ArgMax": {
let i = I("axis", r, e, t8);
return [o.argMax(I("x", r, e, t8), i)];
}
case "ArgMin": {
let i = I("axis", r, e, t8);
return [o.argMin(I("x", r, e, t8), i)];
}
case "Prod": {
let i = I("axis", r, e, t8), p = I("keepDims", r, e, t8);
return [o.prod(I("x", r, e, t8), i, p)];
}
case "Cumprod": {
let i = I("axis", r, e, t8), p = I("exclusive", r, e, t8), u = I("reverse", r, e, t8);
return [o.cumprod(I("x", r, e, t8), i, p, u)];
}
case "Cumsum": {
let i = I("axis", r, e, t8), p = I("exclusive", r, e, t8), u = I("reverse", r, e, t8);
return [o.cumsum(I("x", r, e, t8), i, p, u)];
}
case "Bincount":
let n = I("x", r, e, t8), s = I("weights", r, e, t8), a = I("size", r, e, t8);
return [o.bincount(n, s, a)];
case "DenseBincount": {
let i = I("x", r, e, t8), p = I("weights", r, e, t8), u = I("size", r, e, t8), c = I("binaryOutput", r, e, t8);
return [o.denseBincount(i, p, u, c)];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var AT = (r, e, t8, o = Je) => {
switch (r.op) {
case "ConcatV2":
case "Concat": {
let n = I("n", r, e, t8), s = I("axis", r, e, t8), a = I("tensors", r, e, t8);
return a = a.slice(0, n), [o.concat(a, s)];
}
case "Gather": {
let n = I("x", r, e, t8), s = I("indices", r, e, t8);
return [o.gather(n, o.cast(s, "int32"), 0)];
}
case "GatherV2": {
let n = I("axis", r, e, t8), s = I("batchDims", r, e, t8), a = I("x", r, e, t8), i = I("indices", r, e, t8);
return [o.gather(a, o.cast(i, "int32"), n, s)];
}
case "Reverse": {
let n = I("dims", r, e, t8), s = [];
for (let i = 0; i < n.length; i++)
n[i] && s.push(i);
let a = I("x", r, e, t8);
return [o.reverse(a, s)];
}
case "ReverseV2": {
let n = I("axis", r, e, t8), s = I("x", r, e, t8);
return [o.reverse(s, n)];
}
case "Slice": {
let n = I("begin", r, e, t8), s = I("size", r, e, t8);
return [o.slice(I("x", r, e, t8), n, s)];
}
case "StridedSlice": {
let n = I("begin", r, e, t8), s = I("end", r, e, t8), a = I("strides", r, e, t8), i = I("beginMask", r, e, t8), p = I("endMask", r, e, t8), u = I("ellipsisMask", r, e, t8), c = I("newAxisMask", r, e, t8), l = I("shrinkAxisMask", r, e, t8), m = I("x", r, e, t8);
return [o.stridedSlice(m, n, s, a, i, p, u, c, l)];
}
case "Pack":
return De(() => {
let n = I("axis", r, e, t8), s = I("tensors", r, e, t8), 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, t8), s = I("tensor", r, e, t8);
return o.unstack(s, n);
}
case "Tile": {
let n = I("reps", r, e, t8);
return [o.tile(I("x", r, e, t8), n)];
}
case "Split":
case "SplitV": {
let n = I("axis", r, e, t8), s = I("numOrSizeSplits", r, e, t8), a = I("x", r, e, t8);
return o.split(a, s, n);
}
case "ScatterNd": {
let n = I("indices", r, e, t8), s = I("values", r, e, t8), a = I("shape", r, e, t8);
return [o.scatterND(n, s, a)];
}
case "GatherNd": {
let n = I("x", r, e, t8), s = I("indices", r, e, t8);
return [o.gatherND(n, s)];
}
case "SparseToDense": {
let n = I("sparseIndices", r, e, t8), s = I("outputShape", r, e, t8), a = I("sparseValues", r, e, t8), i = I("defaultValue", r, e, t8);
return [o.sparseToDense(n, a, s, a.dtype === i.dtype ? i : o.cast(i, a.dtype))];
}
case "TensorScatterUpdate": {
let n = I("indices", r, e, t8), s = I("values", r, e, t8), a = I("tensor", r, e, t8);
return [o.tensorScatterUpdate(a, n, s)];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var FT = (r, e, t8, o = Je) => {
switch (r.op) {
case "SparseFillEmptyRows": {
let { outputIndices: n, outputValues: s, emptyRowIndicator: a, reverseIndexMap: i } = o.sparse.sparseFillEmptyRows(I("indices", r, e, t8), I("values", r, e, t8), I("denseShape", r, e, t8), I("defaultValue", r, e, t8));
return [n, s, a, i];
}
case "SparseReshape": {
let { outputIndices: n, outputShape: s } = o.sparse.sparseReshape(I("inputIndices", r, e, t8), I("inputShape", r, e, t8), I("newShape", r, e, t8));
return [n, s];
}
case "SparseSegmentMean":
return [o.sparse.sparseSegmentMean(I("data", r, e, t8), I("indices", r, e, t8), I("segmentIds", r, e, t8))];
case "SparseSegmentSum":
return [o.sparse.sparseSegmentSum(I("data", r, e, t8), I("indices", r, e, t8), I("segmentIds", r, e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var PT = (r, e, t8, o = Je) => {
switch (r.op) {
case "FFT":
return [o.fft(I("x", r, e, t8))];
case "IFFT":
return [o.ifft(I("x", r, e, t8))];
case "RFFT":
return [o.rfft(I("x", r, e, t8))];
case "IRFFT":
return [o.irfft(I("x", r, e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var OT = (r, e, t8, o = Je) => {
switch (r.op) {
case "StaticRegexReplace":
return [o.string.staticRegexReplace(I("input", r, e, t8), I("pattern", r, e, t8), I("rewrite", r, e, t8), I("replaceGlobal", r, e, t8))];
case "StringNGrams": {
let { nGrams: n, nGramsSplits: s } = o.string.stringNGrams(I("data", r, e, t8), I("dataSplits", r, e, t8), I("separator", r, e, t8), I("nGramWidths", r, e, t8), I("leftPad", r, e, t8), I("rightPad", r, e, t8), I("padWidth", r, e, t8), I("preserveShortSequences", r, e, t8));
return [n, s];
}
case "StringSplit": {
let { indices: n, values: s, shape: a } = o.string.stringSplit(I("input", r, e, t8), I("delimiter", r, e, t8), I("skipEmpty", r, e, t8));
return [n, s, a];
}
case "StringToHashBucketFast":
return [o.string.stringToHashBucketFast(I("input", r, e, t8), I("numBuckets", r, e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var MT = (r, e, t8, o = Je) => {
switch (r.op) {
case "Cast":
return [o.cast(I("x", r, e, t8), I("dtype", r, e, t8))];
case "ExpandDims": {
let n = I("axis", r, e, t8);
return [o.expandDims(I("x", r, e, t8), n)];
}
case "Squeeze": {
let n = I("axis", r, e, t8);
return [o.squeeze(I("x", r, e, t8), n)];
}
case "Reshape":
return [o.reshape(I("x", r, e, t8), I("shape", r, e, t8))];
case "EnsureShape":
return [o.ensureShape(I("x", r, e, t8), I("shape", r, e, t8))];
case "MirrorPad":
return [o.mirrorPad(I("x", r, e, t8), I("padding", r, e, t8), I("mode", r, e, t8))];
case "PadV2":
case "Pad":
return [o.pad(I("x", r, e, t8), I("padding", r, e, t8), I("constantValue", r, e, t8))];
case "SpaceToBatchND": {
let n = I("blockShape", r, e, t8), s = I("paddings", r, e, t8);
return [o.spaceToBatchND(I("x", r, e, t8), n, s)];
}
case "BatchToSpaceND": {
let n = I("blockShape", r, e, t8), s = I("crops", r, e, t8);
return [o.batchToSpaceND(I("x", r, e, t8), n, s)];
}
case "DepthToSpace": {
let n = I("blockSize", r, e, t8), s = I("dataFormat", r, e, t8).toUpperCase();
return [o.depthToSpace(I("x", r, e, t8), n, s)];
}
case "BroadcastTo":
return [o.broadcastTo(I("x", r, e, t8), I("shape", r, e, t8))];
case "BroadcastArgs":
return [o.broadcastArgs(I("s0", r, e, t8), I("s1", r, e, t8))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
function _S(r, e, t8, o, n = De) {
let s = ((a, i, p) => {
switch (a.category) {
case "arithmetic":
return n(() => mT(a, i, p));
case "basic_math":
return n(() => dT(a, i, p));
case "control":
return bT(a, i, p);
case "convolution":
return n(() => wT(a, i, p));
case "creation":
return n(() => ST(a, i, p));
case "dynamic":
return IT(a, i, p);
case "evaluation":
return n(() => vT(a, i, p));
case "image":
return n(() => TT(a, i, p));
case "graph":
return n(() => kT(a, i, p));
case "logical":
return n(() => _T(a, i, p));
case "matrices":
return n(() => $T(a, i, p));
case "normalization":
return n(() => ET(a, i, p));
case "ragged":
return n(() => RT(a, i, p));
case "reduction":
return n(() => DT(a, i, p));
case "slice_join":
return n(() => AT(a, i, p));
case "sparse":
return n(() => FT(a, i, p));
case "spectral":
return n(() => PT(a, i, p));
case "string":
return n(() => OT(a, i, p));
case "transformation":
return n(() => MT(a, i, p));
case "hash_table":
return NT(a, i, p, o);
case "custom":
let u = af(a.op);
if (u && u.customExecutor)
return u.customExecutor(new bf(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, t8);
return y.isPromise(s) ? s.then((a) => [].concat(a)) : [].concat(s);
}
var Pl = class {
constructor(e = {}, t8 = {}, o = {}, n = {}, s) {
this.weightMap = e, this.tensorArrayMap = t8, 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, t8) {
return { id: e, frameName: t8, 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 t8 = 0; t8 < this.contexts.length - 1; t8++) {
let o = this.contexts.slice(0, this.contexts.length - t8);
e.push(this.contextIdforContexts(o));
}
e.push(""), this._currentContextIds = e;
}
contextIdforContexts(e) {
return e ? e.map((t8) => t8.id === 0 && t8.iterationId === 0 ? "" : `${t8.frameName}-${t8.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 t8 in this.tensorArrayMap)
this.tensorArrayMap[t8].clearAndClose(e);
for (let t8 in this.tensorListMap)
this.tensorListMap[t8].clearAndClose(e);
}
};
function $S(r, e, t8, o) {
let n = /* @__PURE__ */ new Set(), s = [], a = null, i = null, p = /* @__PURE__ */ new Set(), u = new Set(Object.keys(r).map((m) => Tr(m)[0]));
o = o || [];
let c = new Set(o.map((m) => Tr(m.name)[0])), l = [...e];
for (; l.length > 0; ) {
let m = l.pop();
if ((fu(m) || l8(m) || m8(m)) && a == null && (a = m, i = a.children.map((d) => d.name).filter((d) => n.has(d))), n.add(m.name), t8[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 LT(r, e) {
let { usedNodes: t8, inputs: o } = e, n = Object.keys(o).map((g) => Tr(g)[0]).map((g) => r.nodes[g]), s = r.initNodes || [], a = (g) => t8.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 = a8(f, p);
return i8(h, p), h;
}
function a8(r, e) {
let t8 = 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 = t8.get(a);
for (let p of i.children)
!t8.has(p.name) || n.has(p.name) || (n.add(p.name), o.push(p.name));
}
return r.filter((a) => n.has(a.name));
}
var gc = class extends Error {
constructor(e) {
super(`NodesExecutionOrderError: ${e}`);
}
};
function i8(r, e) {
let t8 = 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 (!t8.has(p.name))
throw new gc(`Child ${p.name} of node ${i.name} is unreachable.`);
if (t8.get(i.name) > t8.get(p.name))
throw new gc(`Node ${i.name} is scheduled to run after its child ${p.name}.`);
}
if (!n(i))
for (let p of i.inputs) {
if (!t8.has(p.name))
throw new gc(`Input ${p.name} of node ${i.name} is unreachable.`);
if (t8.get(p.name) > t8.get(i.name))
throw new gc(`Node ${i.name} is scheduled to run before its input ${p.name}.`);
}
}
}
function BT(r) {
let e = new Map(r.map((i, p) => [i.name, p])), t8 = Number.MAX_SAFE_INTEGER, o = r.map((i, p) => fu(i) ? t8 : 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 === t8)
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 u8 = /* @__PURE__ */ new Set(["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"]);
var p8 = /* @__PURE__ */ new Set(["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"]);
var c8 = /* @__PURE__ */ new Set(["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"]);
function fu(r) {
return u8.has(r.op);
}
function l8(r) {
return p8.has(r.op);
}
function m8(r) {
return c8.has(r.op);
}
var ip = 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 t8 = Object.keys(e).map((o) => e[o].map((n) => n.id));
this._weightIds = [].concat(...t8), 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 t8 = e.signatureKey || e.name;
return e.defaultOutput ? `${t8}:${e.defaultOutput}` : t8;
});
}
get functions() {
return Object.keys(this._functions).reduce((e, t8) => (e[t8] = this._functions[t8].signature, e), {});
}
constructor(e, t8) {
this.graph = e, this.parent = t8, 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 ip(e.functions[o], this);
});
}
getCompilationKey(e, t8) {
let o = e.map((s) => s.name).sort(), n = t8.map((s) => s.name).sort();
return o.join(this.SEPARATOR) + "--" + n.join(this.SEPARATOR);
}
compile(e, t8) {
let o = $S(e, t8, this.weightMap, this._initNodes), { missingInputs: n, dynamicNode: s, syncInputs: a } = o;
if (s != null)
throw new Error(`This execution contains the node '${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 = t8.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: [${n}]`);
}
let i = LT(this.graph, o), p = BT(i);
return { orderedNodes: i, nodeLiveUntilMap: p };
}
cloneAndKeepTensor(e) {
if (e == null)
return null;
let t8 = e.clone();
return Rr(t8), t8;
}
cloneTensorList(e) {
return e ? e.map((o) => this.cloneAndKeepTensor(o)) : null;
}
cloneTensorMap(e) {
return Object.fromEntries(Object.entries(e).map(([t8, o]) => [t8, this.cloneTensorList(o)]));
}
execute(e, t8) {
this.disposeIntermediateTensors(), e = this.mapInputs(e);
let o = Object.keys(e).sort();
this.checkInputs(e), this.checkInputShapeAndType(e), t8 = this.mapOutputs(t8), this.checkOutputs(t8);
let n = o.map((m) => this.graph.nodes[Tr(m)[0]]), s = t8.map((m) => Tr(m)[0]), a = new Set(s), i = s.map((m) => this.graph.nodes[m]);
i.length === 0 && (i = this._outputs);
let p = this.getCompilationKey(n, i), u = this.compiledMap.get(p);
u == null && (u = this.compile(e, i), this.compiledMap.set(p, u));
try {
this.keepIntermediateTensors = A().getBool("KEEP_INTERMEDIATE_TENSORS");
} catch (m) {
this.keepIntermediateTensors = false, console.warn(m.message);
}
let c = {}, l = {};
return De(() => {
let m = new Pl(this.weightMap, c, l, this.functionExecutorMap, this.parseNodeNameCache), d = Object.assign({}, this.weightMap);
this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap)), Object.keys(e).forEach((x) => {
let [b, C] = Tr(x, m), S = [];
S[C] = e[x], d[b] = S, this.keepIntermediateTensors && (this.clonedTensorsMap[b] = this.cloneTensorList(S));
});
let f = this.getFrozenTensorIds(d), { orderedNodes: h, nodeLiveUntilMap: g } = u;
for (let x of h) {
if (d[x.name])
continue;
let b = _S(x, d, m, this._resourceManager);
if (y.isPromise(b))
throw new Error(`The execution of the op '${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), t8.map((x) => Bt(x, d, m));
});
}
getFrozenTensorIds(e) {
let t8 = [].concat.apply([], Object.keys(e).map((o) => e[o]).map((o) => o.map((n) => n.id)));
return new Set(t8);
}
checkTensorForDisposal(e, t8, o, n, s, a, i) {
if (!(fu(t8) || a.has(e))) {
for (let p of o[e])
p != null && (i[p.id] = (i[p.id] || 0) + t8.children.length);
for (let p of t8.inputs) {
if (fu(p))
continue;
let u = aS(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, t8, o, n, s, a) {
function i(p) {
return fu(p) || s.has(p.name);
}
if (!(fu(e) || a == null))
for (let p of a) {
if (i(p))
continue;
let u = aS(p.name, t8, o);
for (let c of u)
!c || c.kept || n.has(c.id) || c.dispose();
}
}
async executeAsync(e, t8) {
return this._executeAsync(e, t8);
}
disposeIntermediateTensors() {
this.clonedTensorsMap && (Object.values(this.clonedTensorsMap).forEach((e) => {
for (let t8 of e)
t8 && !t8.isDisposed && t8.dispose();
}), this.clonedTensorsMap = null);
}
getIntermediateTensors() {
return this.clonedTensorsMap;
}
async _executeAsync(e, t8, o = false, n = {}, s = {}) {
this.disposeIntermediateTensors(), o || (e = this.mapInputs(e), this.checkInputs(e), this.checkInputShapeAndType(e), t8 = this.mapOutputs(t8), this.checkOutputs(t8));
try {
this.keepIntermediateTensors = A().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, t8, o), p = t8.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, t8, o) {
let n = e.reduce((s, a, i) => (s[this.inputs[i].name] = a, s), {});
return this._executeAsync(n, this.outputNodes, true, t8, o);
}
async executeWithControlFlow(e, t8, o, n) {
let s = Object.keys(e), a = s.map((S) => this.graph.nodes[Tr(S)[0]]), i = o.map((S) => Tr(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 } = $S(e, u, this.weightMap, this._initNodes), f = [...a, ...this.graph.weights, ...this._initNodes || []].map((S) => ({ node: S, contexts: t8.currentContext })), h = Object.assign({}, this.weightMap);
Object.keys(e).forEach((S) => {
let [k, _] = Tr(S), E = [];
E[_] = e[S], h[k] = E;
});
let g = {}, x = this.getFrozenTensorIds(h), b = {};
for (; f.length > 0; ) {
let S = this.processStack(a, f, t8, h, b, x, p, g, c);
await Promise.all(S);
}
m == null && !n && console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");
let C = u.filter((S) => !fu(S) && !Bt(S.name, h, t8)).map((S) => S.name);
if (C.length > 0) {
let S = "";
throw m != null && (S = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`), new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`);
}
return h;
}
processStack(e, t8, o, n, s, a, i, p, u) {
let c = [];
for (; t8.length > 0; ) {
let l = t8.pop();
o.currentContext = l.contexts;
let m = "";
if (l.node.op === "Enter" && I("isConstant", l.node, n, o) && ([m] = Os(l.node.name, o)), n[l.node.name] == null) {
let d = _S(l.node, n, o, this._resourceManager);
m || ([m] = Os(l.node.name, o));
let f = o.currentContext;
y.isPromise(d) ? c.push(d.then((h) => (n[m] = h, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(h)), o.currentContext = f, this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t8, o, n, s, u), h))) : (n[m] = d, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(d)), this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t8, o, n, s, u));
} else
this.processChildNodes(l.node, t8, o, n, s, u);
}
return c;
}
processChildNodes(e, t8, o, n, s, a) {
e.children.forEach((i) => {
let [p] = Os(i.name, o);
s[p] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!Bt(u, n, o)) && (s[p] = true, t8.push({ contexts: o.currentContext, node: i })) : i.inputNames.every((u) => !!Bt(u, n, o)) && (s[p] = true, t8.push({ contexts: o.currentContext, node: i })));
});
}
dispose() {
Object.keys(this.weightMap).forEach((e) => this.weightMap[e].forEach((t8) => t8.dispose()));
}
checkInputShapeAndType(e) {
Object.keys(e).forEach((t8) => {
let o = e[t8], [n] = Tr(t8), s = this.graph.nodes[n];
if (s.attrParams.shape && s.attrParams.shape.value) {
let a = s.attrParams.shape.value, i = a.length === o.shape.length && o.shape.every((p, u) => a[u] === -1 || a[u] === p);
y.assert(i, () => `The shape of dict['${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 ${o.dtype}`);
});
}
mapInputs(e) {
var t8, o;
let n = {};
for (let s in e) {
let a = (o = (t8 = this._signature) === null || t8 === void 0 ? void 0 : t8.inputs) === null || o === void 0 ? void 0 : o[s];
a != null ? n[a.name] = e[s] : n[s] = e[s];
}
return n;
}
checkInputs(e) {
let t8 = Object.keys(e).filter((o) => {
let [n] = Tr(o);
return this.graph.nodes[n] == null;
});
if (t8.length > 0)
throw new Error(`The dict provided in model.execute(dict) has keys: [${t8}] that are not part of graph`);
}
mapOutputs(e) {
return e.map((t8) => {
var o, n;
let s = (n = (o = this._signature) === null || o === void 0 ? void 0 : o.outputs) === null || n === void 0 ? void 0 : n[t8];
return s != null ? s.name : t8;
}, {});
}
checkOutputs(e) {
e.forEach((t8) => {
let [o] = Tr(t8);
if (!this.graph.nodes[o])
throw new Error(`The output '${t8}' is not found in the graph`);
});
}
};
var If = class {
constructor(e = {}, t8 = {}) {
this.hashTableNameToHandle = e, this.hashTableMap = t8;
}
addHashTable(e, t8) {
this.hashTableNameToHandle[e] = t8.handle, this.hashTableMap[t8.id] = t8;
}
getHashTableHandleByName(e) {
return this.hashTableNameToHandle[e];
}
getHashTableById(e) {
return this.hashTableMap[e];
}
dispose() {
for (let e in this.hashTableMap)
this.hashTableMap[e].clearAndClose(), delete this.hashTableMap[e];
for (let e in this.hashTableNameToHandle)
this.hashTableNameToHandle[e].dispose(), delete this.hashTableNameToHandle[e];
}
};
var d8 = "?tfjs-format=file";
var f8 = "model.json";
var Ol = class {
get modelVersion() {
return this.version;
}
get inputNodes() {
return this.executor.inputNodes;
}
get outputNodes() {
return this.executor.outputNodes;
}
get inputs() {
return this.executor.inputs;
}
get outputs() {
return this.executor.outputs;
}
get weights() {
return this.executor.weightMap;
}
get metadata() {
return this.artifacts.userDefinedMetadata;
}
get modelSignature() {
return this.signature;
}
get modelStructuredOutputKeys() {
return this.structuredOutputKeys;
}
constructor(e, t8 = {}, o = mi) {
this.modelUrl = e, this.loadOptions = t8, this.version = "n/a", this.io = o, t8 == null && (this.loadOptions = {}), this.resourceManager = new If();
}
findIOHandler() {
let e = this.modelUrl;
if (e.load != null)
this.handler = e;
else if (this.loadOptions.requestInit != null)
this.handler = this.io.browserHTTPRequest(e, this.loadOptions);
else {
let t8 = this.io.getLoadHandlers(e, this.loadOptions);
if (t8.length === 0)
t8.push(this.io.browserHTTPRequest(e, this.loadOptions));
else if (t8.length > 1)
throw new Error(`Found more than one (${t8.length}) load handlers for URL '${[e]}'`);
this.handler = t8[0];
}
}
load() {
if (this.findIOHandler(), this.handler.load == null)
throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
let e = this.handler.load();
return y.isPromise(e) ? e.then((t8) => this.loadSync(t8)) : this.loadSync(e);
}
loadSync(e) {
this.artifacts = e;
let t8 = this.artifacts.modelTopology, o = this.artifacts.signature;
if (this.artifacts.userDefinedMetadata != null) {
let s = this.artifacts.userDefinedMetadata;
s.signature != null && (o = s.signature), s.structuredOutputKeys != null && (this.structuredOutputKeys = s.structuredOutputKeys);
}
this.signature = o, this.version = `${t8.versions.producer}.${t8.versions.minConsumer}`;
let n = this.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
if (this.executor = new ip(Fl.Instance.transformGraph(t8, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
let s = Fl.Instance.transformGraph(e.modelInitializer);
this.initializer = new ip(s), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializerSignature = e.initializerSignature;
}
return true;
}
async save(e, t8) {
if (typeof e == "string") {
let o = this.io.getSaveHandlers(e);
if (o.length === 0)
throw new Error(`Cannot find any save handlers for URL '${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 t8 = e instanceof ut ? [e] : e, o = {};
return t8.forEach((n, s) => o[this.structuredOutputKeys[s]] = n), o;
}
return e;
}
predict(e, t8) {
let o = this.execute(e, this.outputNodes);
return this.addStructuredOutputNames(o);
}
async predictAsync(e, t8) {
let o = await this.executeAsync(e, this.outputNodes);
return this.addStructuredOutputNames(o);
}
normalizeInputs(e) {
var t8;
if (!(e instanceof ut) && !Array.isArray(e)) {
let s = (t8 = this.signature) === null || t8 === void 0 ? void 0 : t8.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 t8 = this.initializerSignature.outputs, o = Object.keys(t8);
for (let n = 0; n < o.length; n++) {
let s = o[n], a = t8[s];
this.resourceIdToCapturedInput[a.resourceId] = e[n];
}
}
}
execute(e, t8) {
this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(this.executeInitializerGraph()), e = this.normalizeInputs(e), t8 = this.normalizeOutputs(t8);
let o = this.executor.execute(e, t8);
return o.length > 1 ? o : o[0];
}
async executeAsync(e, t8) {
this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()), e = this.normalizeInputs(e), t8 = this.normalizeOutputs(t8);
let o = await this.executor.executeAsync(e, t8);
return o.length > 1 ? o : o[0];
}
getIntermediateTensors() {
return this.executor.getIntermediateTensors();
}
disposeIntermediateTensors() {
this.executor.disposeIntermediateTensors();
}
convertTensorMapToTensorsMap(e) {
return Object.keys(e).reduce((t8, o) => (t8[o] = [e[o]], t8), {});
}
dispose() {
this.executor.dispose(), this.initializer && (this.initializer.dispose(), this.resourceIdToCapturedInput && Ot(this.resourceIdToCapturedInput)), this.resourceManager.dispose();
}
};
async function h8(r, e = {}, t8 = mi) {
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 = x8(r));
let o = new Ol(r, e, t8);
return await o.load(), o;
}
function g8(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 = mi.getWeightSpecs(o.weightsManifest), a = mi.getModelArtifactsForJSONSync(o, s, n);
e = mi.fromMemorySync(a);
} else if ("load" in r)
e = r;
else if ("modelTopology" in r && "weightSpecs" in r && "weightData" in r)
e = mi.fromMemorySync(r);
else
throw new Error("Unknown model format");
let t8 = new Ol(e);
return t8.load(), t8;
}
function x8(r) {
return r.endsWith("/") || (r = r + "/"), `${r}${f8}${d8}`;
}
var y8 = "4.7.0";
function Q(r, e) {
Array.isArray(r) || (r = [r]), r.forEach((t8) => {
t8 != null && y.assert(t8.dtype !== "complex64", () => `${e} does not support complex64 tensors in the CPU backend.`);
});
}
var b8 = Wt.whereImpl;
var hu = class extends so {
nextDataId() {
return hu.nextDataId++;
}
constructor() {
super(), this.blockSize = 48, this.firstUse = true, this.data = new Bo(this, ur());
}
write(e, t8, o) {
this.firstUse && (this.firstUse = false, A().get("IS_NODE") && w.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, t8, o) {
let n;
if (t8 === "string" && o != null && o.length > 0 && y.isString(o[0])) {
let s = o.map((a) => y.encodeString(a));
n = this.write(s, e, t8);
} else
n = this.write(o, e, t8);
return { dataId: n, shape: e, dtype: t8 };
}
refCount(e) {
return this.data.has(e) ? this.data.get(e).refCount : 0;
}
incRef(e) {
let t8 = this.data.get(e);
t8.refCount++;
}
decRef(e) {
if (this.data.has(e)) {
let t8 = this.data.get(e);
t8.refCount--;
}
}
move(e, t8, o, n, s) {
this.data.set(e, { values: t8, dtype: n, refCount: s });
}
numDataIds() {
return this.data.numDataIds();
}
async read(e) {
return this.readSync(e);
}
readSync(e) {
let { dtype: t8, complexTensorInfos: o } = this.data.get(e);
if (t8 === "complex64") {
let n = this.readSync(o.real.dataId), s = this.readSync(o.imag.dataId);
return w.mergeRealAndImagArrays(n, s);
}
return y.convertBackendValuesAndArrayBuffer(this.data.get(e).values, t8);
}
bufferSync(e) {
let t8 = this.readSync(e.dataId);
if (e.dtype === "string")
try {
let o = t8.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, t8);
}
makeOutput(e, t8, o) {
return ur().makeTensorFromTensorInfo(this.makeTensorInfo(t8, o, e), this);
}
disposeData(e, t8 = false) {
if (this.data.has(e)) {
if (this.data.get(e).refCount--, !t8 && 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 t8 = y.now();
return e(), { kernelMs: y.now() - t8 };
}
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) {
Q([e], "where");
let t8 = this.readSync(e.dataId);
return b8(e.shape, t8);
}
dispose() {
}
floatPrecision() {
return 32;
}
epsilon() {
return super.epsilon();
}
};
hu.nextDataId = 0;
var Ic = {};
Ke(Ic, { addImpl: () => DS, bincountImpl: () => bc, bincountReduceImpl: () => vf, bitwiseAndImpl: () => AS, castImpl: () => RS, ceilImpl: () => FS, concatImpl: () => up, equalImpl: () => PS, expImpl: () => MS, expm1Impl: () => BS, floorDivImpl: () => VS, floorImpl: () => zS, gatherNdImpl: () => kf, gatherV2Impl: () => Nf, greaterEqualImpl: () => US, greaterImpl: () => WS, lessEqualImpl: () => HS, lessImpl: () => GS, linSpaceImpl: () => Tf, logImpl: () => KS, maxImpl: () => _f, maximumImpl: () => qS, minimumImpl: () => jS, multiplyImpl: () => Ml, negImpl: () => XS, notEqualImpl: () => YS, prodImpl: () => QS, raggedGatherImpl: () => $f, raggedRangeImpl: () => Ef, raggedTensorToTensorImpl: () => Rf, rangeImpl: () => cp, rsqrtImpl: () => ZS, scatterImpl: () => Ls, sigmoidImpl: () => g_, simpleAbsImpl: () => ES, sliceImpl: () => lp, sparseFillEmptyRowsImpl: () => Df, sparseReshapeImpl: () => Af, sparseSegmentReductionImpl: () => Sc, sqrtImpl: () => b_, squaredDifferenceImpl: () => eI, staticRegexReplaceImpl: () => tI, stridedSliceImpl: () => Ff, stringNGramsImpl: () => mp, stringSplitImpl: () => dp, stringToHashBucketFastImpl: () => fp, subImpl: () => oI, tileImpl: () => Pf, topKImpl: () => Of, transposeImpl: () => Cc, uniqueImpl: () => hp });
function ES(r) {
let e = new Float32Array(r.length);
for (let t8 = 0; t8 < r.length; ++t8)
e[t8] = Math.abs(r[t8]);
return e;
}
var C8 = (r) => {
let { x: e } = r.inputs, t8 = r.backend;
Q(e, "abs");
let o = new Float32Array(y.sizeFromShape(e.shape)), n = t8.data.get(e.dataId).values;
return o = ES(n), t8.makeOutput(o, e.shape, e.dtype);
};
var zT = { kernelName: js, backendName: "cpu", kernelFunc: C8 };
function ze(r) {
return (e, t8, o, n, s) => {
let a = w.assertAndGetBroadcastShape(e, t8), i = a.length, p = y.computeStrides(a), u = y.sizeFromShape(a), c = y.getTypedArrayFromDType(s, u), l = e.length, m = t8.length, d = y.computeStrides(e), f = y.computeStrides(t8), h = w.getBroadcastDims(e, a), g = w.getBroadcastDims(t8, 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), C = b.slice(-l);
h.forEach((E) => C[E] = 0);
let S = y.locToIndex(C, 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: t8 } = r, { real: o, imag: n } = e, s = t8.data.get(o.dataId).values, a = t8.data.get(n.dataId).values, i = t8.makeTensorInfo(o.shape, "complex64"), p = t8.data.get(i.dataId);
return p.complexTensorInfos = { real: t8.makeTensorInfo(o.shape, "float32", s), imag: t8.makeTensorInfo(n.shape, "float32", a) }, i;
}
var VT = { kernelName: Ri, backendName: "cpu", kernelFunc: Kt };
function xc(r, e, t8 = "float32") {
if (t8 === "complex64") {
let n = xc(r, e, "float32"), s = xc(r, e, "float32");
return Kt({ inputs: { real: n, imag: s }, backend: r });
}
let o = y.makeZerosTypedArray(y.sizeFromShape(e), t8);
return r.makeTensorInfo(e, t8, o);
}
function lr(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
return t8.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
var WT = { kernelName: Co, backendName: "cpu", kernelFunc: lr };
function Eo(r) {
let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.data.get(o.dataId).complexTensorInfos.real, s = t8.data.get(n.dataId).values;
return t8.makeTensorInfo(n.shape, n.dtype, s);
}
var UT = { kernelName: Gi, backendName: "cpu", kernelFunc: Eo };
function RS(r, e, t8, o) {
if (o === "int32") {
let n = Int32Array.from(r);
return [e, "int32", n];
}
if (o === "bool") {
let n = y.toTypedArray([0], t8), [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 ${t8} to ${o}`);
}
function Ro(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dtype: s } = o;
if (s === "complex64") {
if (n.dtype === "complex64")
return lr({ inputs: { x: n }, backend: t8 });
let c = xc(t8, n.shape, n.dtype), l = Ro({ inputs: { x: n }, backend: t8, attrs: { dtype: "float32" } }), m = Kt({ inputs: { real: l, imag: c }, backend: t8 });
return t8.disposeIntermediateTensorInfo(c), t8.disposeIntermediateTensorInfo(l), m;
}
if (n.dtype === "complex64") {
let c = Eo({ inputs: { input: n }, backend: t8 }), l = Ro({ inputs: { x: c }, backend: t8, attrs: { dtype: s } });
return t8.disposeIntermediateTensorInfo(c), l;
}
if (!y.hasEncodingLoss(n.dtype, s)) {
let c = lr({ inputs: { x: n }, backend: t8 });
return { dataId: c.dataId, shape: c.shape, dtype: s };
}
let a = t8.data.get(n.dataId).values, [i, p, u] = RS(a, n.shape, n.dtype, s);
return t8.makeTensorInfo(i, p, u);
}
var GT = { kernelName: yo, backendName: "cpu", kernelFunc: Ro };
function Ye(r, e, t8, o) {
return t8 == null ? ({ inputs: n, backend: s }) => {
let { a, b: i } = n, p = s;
Q([a, i], r);
let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = a.dtype === "string" ? w.fromUint8ToStringArray(u) : u, m = a.dtype === "string" ? w.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 = Ro({ 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 = Ro({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), x = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, C = p.data.get(x.dataId).values, S = p.data.get(b.dataId).values, [k, _, E] = t8(a.shape, i.shape, d, f, C, S), R = p.makeTensorInfo(E, "float32", k), D = p.makeTensorInfo(E, "float32", _), P = Kt({ inputs: { real: R, imag: D }, backend: p });
return p.disposeIntermediateTensorInfo(u), p.disposeIntermediateTensorInfo(h), p.disposeIntermediateTensorInfo(R), p.disposeIntermediateTensorInfo(D), P;
} 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 yc(r) {
return (e, t8, o, n, s, a) => {
let i = w.assertAndGetBroadcastShape(e, t8), p = y.sizeFromShape(i), u = i.length, c = y.computeStrides(i), l = y.getTypedArrayFromDType("float32", p), m = y.getTypedArrayFromDType("float32", p), d = w.getBroadcastDims(e, i), f = w.getBroadcastDims(t8, i), h = w.mergeRealAndImagArrays(o, n), g = w.mergeRealAndImagArrays(s, a), x = e.length, b = y.computeStrides(e), C = t8.length, S = y.computeStrides(t8);
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(-C);
f.forEach((M) => D[M] = 0);
let P = y.locToIndex(D, C, S), O = r(h[R * 2], h[R * 2 + 1], g[P * 2], g[P * 2 + 1]);
l[k] = O.real, m[k] = O.imag;
}
return [l, m, i];
};
}
var DS = ze((r, e) => r + e);
var w8 = yc((r, e, t8, o) => ({ real: r + t8, imag: e + o }));
var Aa = Ye(io, DS, w8);
var HT = { kernelName: io, backendName: "cpu", kernelFunc: Aa };
function bc(r, e, t8, o, n) {
let s = y.sizeFromShape(o), a = y.makeZerosTypedArray(n, t8);
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 vf(r, e, t8, o = false) {
let n = r.shape[0], s = r.shape[1], a = me([n, t8], 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 >= t8 || (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 AS = ze((r, e) => r & e);
var S8 = Ye(Ha, AS);
var KT = { kernelName: Ha, backendName: "cpu", kernelFunc: S8 };
function Xt(r) {
return (e, t8, o) => {
let n = y.getArrayFromDType(t8, e.length);
for (let s = 0; s < e.length; ++s)
n[s] = r(e[s], o);
return n;
};
}
function Ie(r, e, t8) {
let o = Xt(e);
return Ar(r, o, t8);
}
function Ar(r, e, t8) {
return ({ inputs: o, attrs: n, backend: s }) => {
let { x: a } = o;
Q(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 = w.fromUint8ToStringArray(p);
} else
u = p;
let c = t8 || a.dtype, l = e(u, c, n);
return i.makeTensorInfo(a.shape, c, l);
};
}
var FS = Xt((r) => Math.ceil(r));
var I8 = Ar(en, FS);
var qT = { kernelName: en, backendName: "cpu", kernelFunc: I8 };
function up(r, e, t8, o) {
let n = y.getArrayFromDType(t8, y.sizeFromShape(e));
if (o && t8 !== "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 = t8 === "string" ? w.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 PS = ze((r, e) => r === e ? 1 : 0);
var OS = Ye(xn, PS, null, "bool");
var jT = { kernelName: xn, backendName: "cpu", kernelFunc: OS };
var MS = Xt((r) => Math.exp(r));
var LS = Ar(yn, MS, "float32");
var XT = { kernelName: yn, backendName: "cpu", kernelFunc: LS };
var BS = Xt((r) => Math.expm1(r));
var v8 = Ar(bn, BS);
var YT = { kernelName: bn, backendName: "cpu", kernelFunc: v8 };
var zS = Xt((r) => Math.floor(r));
var k8 = Ar(wn, zS);
var QT = { kernelName: wn, backendName: "cpu", kernelFunc: k8 };
var VS = ze((r, e) => Math.floor(r / e));
var N8 = Ye(Sn, VS, null, "int32");
var ZT = { kernelName: Sn, backendName: "cpu", kernelFunc: N8 };
function kf(r, e, t8, o, n, s, a, i, p) {
let u = me([o, s], t8);
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 Nf(r, e, t8) {
let o = me(t8, 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 WS = ze((r, e) => r > e ? 1 : 0);
var T8 = Ye(kn, WS, null, "bool");
var JT = { kernelName: kn, backendName: "cpu", kernelFunc: T8 };
var US = ze((r, e) => r >= e ? 1 : 0);
var _8 = Ye(Nn, US, null, "bool");
var e_ = { kernelName: Nn, backendName: "cpu", kernelFunc: _8 };
var GS = ze((r, e) => r < e ? 1 : 0);
var $8 = Ye(Rn, GS, null, "bool");
var t_ = { kernelName: Rn, backendName: "cpu", kernelFunc: $8 };
var HS = ze((r, e) => r <= e ? 1 : 0);
var E8 = Ye(Dn, HS, null, "bool");
var r_ = { kernelName: Dn, backendName: "cpu", kernelFunc: E8 };
function Tf(r, e, t8) {
let o = (e - r) / (t8 - 1), n = y.makeZerosTypedArray(t8, "float32");
n[0] = r;
for (let s = 1; s < n.length; s++)
n[s] = n[s - 1] + o;
return n;
}
var KS = Xt((r) => Math.log(r));
var R8 = Ar(Fn, KS);
var o_ = { kernelName: Fn, backendName: "cpu", kernelFunc: R8 };
function _f(r, e, t8, o) {
let n = y.getTypedArrayFromDType(o, y.sizeFromShape(t8));
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 qS = ze((r, e) => Math.max(r, e));
var D8 = Ye(Vn, qS);
var n_ = { kernelName: Vn, backendName: "cpu", kernelFunc: D8 };
var jS = ze((r, e) => Math.min(r, e));
var A8 = Ye(Hn, jS);
var s_ = { kernelName: Hn, backendName: "cpu", kernelFunc: A8 };
var Ml = ze((r, e) => r * e);
var F8 = yc((r, e, t8, o) => ({ real: r * t8 - e * o, imag: r * o + e * t8 }));
var pp = Ye(Xn, Ml, F8);
var a_ = { kernelName: Xn, backendName: "cpu", kernelFunc: pp };
function XS(r, e, t8) {
let o = y.createScalarValue(-1, t8);
return Ml([], e, o, r, t8);
}
function P8(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
Q(o, "neg");
let n = t8.data.get(o.dataId).values, [s, a] = XS(n, o.shape, o.dtype);
return t8.makeTensorInfo(a, o.dtype, s);
}
var i_ = { kernelName: ua, backendName: "cpu", kernelFunc: P8 };
var YS = ze((r, e) => r !== e ? 1 : 0);
var O8 = Ye(Yn, YS, null, "bool");
var u_ = { kernelName: Yn, backendName: "cpu", kernelFunc: O8 };
function Cc(r, e, t8, o, n) {
let s = e.length, a = y.sizeFromShape(e), i = y.computeStrides(e), p = y.computeStrides(n), u = y.getTypedArrayFromDType(t8, 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 It(r) {
let { inputs: e, attrs: t8, backend: o } = r, { x: n } = e, { perm: s } = t8;
Q(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 = Cc(p, n.shape, n.dtype, s, i);
return { dataId: o.write(u, i, n.dtype), shape: i, dtype: n.dtype };
}
var p_ = { kernelName: po, backendName: "cpu", kernelFunc: It };
function QS(r, e, t8, o) {
let [n, s] = w.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 *= t8[c + m];
i[u] = l;
}
return { outVals: i, outShape: n, outDtype: a };
}
function M8(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
Q(n, "prod");
let i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = w.getAxesPermutation(p, i), c = p, l = n, m = [];
u != null && (l = It({ inputs: { x: n }, backend: t8, attrs: { perm: u } }), m.push(l), c = w.getInnerMostAxes(c.length, i));
let d = t8.data.get(l.dataId).values, { outVals: f, outShape: h, outDtype: g } = QS(l.shape, l.dtype, d, c), x = h;
return a && (x = w.expandShapeToKeepDim(h, p)), m.forEach((b) => t8.disposeIntermediateTensorInfo(b)), t8.makeTensorInfo(x, g, f);
}
var c_ = { kernelName: os, backendName: "cpu", kernelFunc: M8 };
function L8(r, e, t8) {
r.forEach((o, n) => {
if (o < 0 || o >= t8) {
let s = y.indexToLoc(n, e.length, y.computeStrides(e)).join(",");
throw new Error(`indices[${s}] = ${o} is not in [0, ${t8})`);
}
});
}
function B8(r, e) {
for (let t8 = 0; t8 < r.length; ++t8) {
let o = r[t8], n = t8 === r.length - 1 ? e : r[t8 + 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 z8(r, e, t8, o) {
let n = [], s = 0, a = e.length - 1 + t8.length, i = new Array(a).fill(null).map(() => [0]);
B8(t8, 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 < t8.length; ++m) {
let d = t8[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 V8(r) {
let e = [];
for (let t8 = 0; t8 < r.length; ++t8) {
let o = r[t8].length, n = y.getArrayFromDType("int32", o);
e.push(n), r[t8].forEach((s, a) => n[a] = s);
}
return e;
}
function l_(r, e) {
let t8 = r.slice(0, e);
for (; t8.length < e; )
t8.push(1);
for (let o = e; o < r.length; o++)
t8[e - 1] *= r[o];
return t8;
}
function W8(r, e, t8, o, n, s) {
let a = l_(e, 2)[1], i = l_(s, 2)[1], p = 0;
for (let u of t8)
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 U8(r, e, t8, o, n) {
let s = e.slice();
s[0] = n;
let a = y.getArrayFromDType(t8, y.sizeFromShape(s)), i = r.length, p = i === 0 ? 0 : i / e[0];
return W8(r, e, o, p, a, s), [a, s];
}
function $f(r, e, t8, 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 } = z8(s, a, r, u), d = V8(c), f = U8(t8, o, n, l, m);
return [d, f[0], f[1]];
}
var m_ = 2147483647;
function Ef(r, e, t8, 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], C = u ? s[0] : s[g];
if (C === 0)
throw new Error("Requires delta != 0");
let S;
if (C > 0 && b < x || C < 0 && b > x)
S = 0;
else if (S = Math.ceil(Math.abs((b - x) / C)), S > m_)
throw new Error(`Requires ((limit - start) / delta) <= ${m_}`);
m[g + 1] = m[g] + S;
}
let d = m[l], f = y.getArrayFromDType(t8, d), h = 0;
for (let g = 0; g < l; ++g) {
let x = m[g + 1] - m[g], b = i ? r[0] : r[g], C = u ? s[0] : s[g];
for (let S = 0; S < x; ++S)
f[h++] = b, b += C;
}
return [m, f];
}
var Do = w.RowPartitionType;
var wc = class {
constructor(e, t8, o, n, s, a, i, p, u, c) {
this.shape = e, this.shapeShape = t8, this.values = o, this.valuesShape = n, this.valuesDType = s, this.defaultValue = a, this.defaultValueShape = i, this.rowPartitionValues = p, this.rowPartitionValuesShapes = u, this.rowPartitionTypes = w.getRowPartitionTypesHelper(c), this.raggedRank = w.getRaggedRank(this.rowPartitionTypes);
}
getRowPartitionTypeByDimension(e) {
return this.rowPartitionTypes[0] === Do.FIRST_DIM_SIZE ? this.rowPartitionTypes[e + 1] : this.rowPartitionTypes[e];
}
getRowPartitionTensor(e) {
return this.rowPartitionTypes[0] === Do.FIRST_DIM_SIZE ? this.rowPartitionValues[e + 1] : this.rowPartitionValues[e];
}
getMaxWidth(e) {
let t8 = this.getRowPartitionTensor(e - 1);
switch (this.getRowPartitionTypeByDimension(e - 1)) {
case Do.VALUE_ROWIDS:
return wc.getMaxWidthValueRowID(t8);
case Do.ROW_SPLITS:
return wc.getMaxWidthRowSplit(t8);
default:
throw new Error(`Cannot handle partition type ${Do[this.getRowPartitionTypeByDimension(e - 1)]}`);
}
}
static getMaxWidthRowSplit(e) {
let t8 = e.length;
if (t8 === 0 || t8 === 1)
return 0;
let o = 0;
for (let n = 0; n < t8 - 1; ++n) {
let s = e[n + 1] - e[n];
s > o && (o = s);
}
return o;
}
static getMaxWidthValueRowID(e) {
let t8 = e.length;
if (t8 === 0)
return 0;
let o = 0, n = e[0], s = 0;
for (let a = 1; a < t8; ++a) {
let i = e[a];
i !== n && (n = i, s = Math.max(a - o, s), o = a);
}
return Math.max(t8 - o, s);
}
tensorShapeFromTensor(e, t8, o = true) {
if (t8.length === 0) {
if (e[0] === -1)
return [];
throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.");
}
return f_(e, o);
}
calculateOutputSize(e) {
let t8 = this.valuesShape, o = this.defaultValueShape;
w.validateDefaultValueShape(o, t8);
let n = this.tensorShapeFromTensor(this.shape, this.shapeShape), a = w.combineRaggedTensorToTensorShapes(this.raggedRank, n, t8);
a[0] < 0 && (a[0] = e);
for (let i = 1; i <= this.raggedRank; ++i)
a[i] < 0 && (a[i] = this.getMaxWidth(i));
return a;
}
calculateFirstParentOutputIndex(e, t8, o) {
let n = Math.min(e, o), s = [], a = 0;
for (let i = 0; i < n; ++i, a += t8)
s.push(a);
for (let i = n; i < e; ++i)
s.push(-1);
return y.assert(s.length === e, () => "Final length of result must be equal to firstDimension."), s;
}
calculateOutputIndexRowSplit(e, t8, o, n) {
let s = e.length, a = [];
for (let i = 0; i < s - 1; ++i) {
let p = e[i + 1] - e[i], u = Math.min(n, p), c = t8[i];
c === -1 && (u = 0);
for (let l = 0; l < u; ++l)
a.push(c), c += o;
for (let l = 0; l < p - u; ++l)
a.push(-1);
}
if (s > 0 && a.length !== e[s - 1])
throw new Error("Invalid row split size.");
return a;
}
calculateOutputIndexValueRowID(e, t8, o, n) {
let s = e.length, a = [];
if (s === 0)
return [];
let i = 0, p = e[0];
if (p >= t8.length)
throw new Error(`Got currentValueRowId=${p}, which is not less than ${t8.length}`);
let u = t8[p];
a.push(u);
for (let c = 1; c < s; ++c) {
let l = e[c];
if (l === p)
u >= 0 && (++i, i < n ? u += o : u = -1);
else {
if (i = 0, p = l, l >= t8.length)
throw new Error(`Got nextValueRowId=${l} which is not less than ${t8.length}`);
u = t8[l];
}
a.push(u);
}
if (a.length !== e.length)
throw new Error("Invalid row ids.");
return a;
}
calculateOutputIndex(e, t8, o, n) {
let s = this.getRowPartitionTensor(e), a = this.getRowPartitionTypeByDimension(e);
switch (a) {
case Do.VALUE_ROWIDS:
return this.calculateOutputIndexValueRowID(s, t8, o, n);
case Do.ROW_SPLITS:
if (s.length - 1 > t8.length)
throw new Error(`Row partition size is greater than output size: ${s.length - 1} > ${t8.length}`);
return this.calculateOutputIndexRowSplit(s, t8, o, n);
default:
throw new Error(`Unsupported partition type: ${Do[a]}`);
}
}
getFirstDimensionSize() {
let e = this.rowPartitionValues[0];
if (this.rowPartitionTypes.length === 0)
throw new Error("No row_partition_types given.");
let t8 = this.rowPartitionTypes[0];
switch (t8) {
case Do.FIRST_DIM_SIZE:
return e[0];
case Do.VALUE_ROWIDS:
throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");
case Do.ROW_SPLITS:
return this.rowPartitionValuesShapes[0][0] - 1;
default:
throw new Error(`Cannot handle type ${Do[t8]}`);
}
}
compute() {
if (this.rowPartitionValues[0].length <= 0)
throw new Error("Invalid first partition input. Tensor requires at least one element.");
let t8 = this.getFirstDimensionSize(), o = this.calculateOutputSize(t8), 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 = f_(o, false), a = y.getArrayFromDType(this.valuesDType, y.sizeFromShape(s));
if (n[0] * o[0] > 0) {
let p = this.calculateFirstParentOutputIndex(t8, 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, t8, 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 = t8.length, c = this.defaultValue;
if (c.length !== p && c.length !== 1) {
let f = this.defaultValueShape;
De(() => {
let h = W(c, f);
c = au(h, i).dataSync();
});
}
let l = 0, m = 0, d = 0;
for (let f = 0; f <= u; ++f) {
let h = f < u ? t8[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;
d_(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);
d_(g, c, p), ++d;
}
h < 0 ? (l = f + 1, m = d) : (l = f, m = d, d = m + 1);
}
}
};
function d_(r, e, t8) {
for (let o = 0; o < t8; o++)
r[o] = e[o];
}
function f_(r, e) {
let t8 = [];
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;
}
t8.push(o);
}
return t8;
}
function Rf(r, e, t8, o, n, s, a, i, p, u) {
return new wc(r, e, t8, o, n, s, a, i, p, u).compute();
}
function cp(r, e, t8, o) {
let n = r === e, s = r < e && t8 < 0, a = e < r && t8 > 1;
if (n || s || a)
return y.makeZerosTypedArray(0, o);
let i = Math.abs(Math.ceil((e - r) / t8)), p = y.makeZerosTypedArray(i, o);
e < r && t8 === 1 && (t8 = -1), p[0] = r;
for (let u = 1; u < p.length; u++)
p[u] = p[u - 1] + t8;
return p;
}
var ZS = Xt((r) => 1 / Math.sqrt(r));
var G8 = Ar(ls, ZS);
var h_ = { kernelName: ls, backendName: "cpu", kernelFunc: G8 };
function Ls(r, e, t8, o, n, s, a, i, p, u) {
let c = [o / n, n], l = r.values, m = e.values;
if (o === 0)
return me(t8, 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 ${t8}`);
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 g_ = Xt((r) => 1 / (1 + Math.exp(-r)));
var JS = Ie(bs, (r) => 1 / (1 + Math.exp(-r)));
var x_ = { kernelName: bs, backendName: "cpu", kernelFunc: JS };
function lp(r, e, t8, o, n) {
let s = ct.isSliceContinous(o, e, t8), a = y.sizeFromShape(t8), 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" ? w.fromUint8ToStringArray(r) : r, u = me(o, n, p), c = me(t8, 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" ? w.fromStringArrayToUint8(c.values) : c.values;
}
function Ao(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o;
Q(n, "slice");
let [i, p] = ct.parseSliceParams(n, s, a);
ct.assertParamsValid(n, i, p);
let u = t8.data.get(n.dataId).values, c = lp(u, i, p, n.shape, n.dtype);
return t8.makeTensorInfo(p, n.dtype, c);
}
var y_ = { kernelName: fa, backendName: "cpu", kernelFunc: Ao };
function Df(r, e, t8, 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(w.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));
let g = y.getArrayFromDType(t8, 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(w.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, x));
if (x >= p)
throw new Error(w.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(t8, g * l), b = y.getArrayFromDType(n, g), C = new Array(p).fill(0);
for (let S = 0; S < i; ++S) {
let k = r[S * l], _ = C[k], E = (k === 0 ? 0 : f[k - 1]) + _;
C[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 (C[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 Af(r, e, t8, 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(w.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c, g));
c = g, p.push(1);
} else {
if (x < 0)
throw new Error(w.getSparseReshapeNegativeOutputDimErrorMessage(g, x));
u *= x, p.push(x);
}
}
if (c !== -1) {
if (u <= 0)
throw new Error(w.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
let g = Math.trunc(s / u);
if (u * g !== s)
throw new Error(w.getSparseReshapeInputOutputMultipleErrorMessage(o, p));
p[c] = g;
}
if (y.sizeFromShape(p) !== s)
throw new Error(w.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(t8, 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 Sc(r, e, t8, 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(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
let m = e.slice();
m[0] = l;
let d = m.reduce((C, S) => C * S, 1), f = y.getArrayFromDType(t8, d);
if (i === 0)
return l > 0 && f.fill(a), [f, m];
if (l <= 0)
throw new Error(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
let h = 0, g = 1, x = 0, b = n[h];
for (; ; ) {
let C = 0;
if (g < i) {
if (C = n[g], b === C) {
++g;
continue;
}
if (b >= C)
throw new Error(w.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
}
if (b < 0 || b >= l)
throw new Error(w.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(w.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 = C, g > i)
break;
}
return x < l && f.fill(a, x * u, l * u), [f, m];
}
var b_ = Xt((r) => Math.sqrt(r));
var H8 = Ie(ws, (r) => Math.sqrt(r));
var C_ = { kernelName: ws, backendName: "cpu", kernelFunc: H8 };
var eI = ze((r, e) => {
let t8 = r - e;
return t8 * t8;
});
var K8 = Ye(ks, eI);
var w_ = { kernelName: ks, backendName: "cpu", kernelFunc: K8 };
var tI = Xt((r, e) => {
let { pattern: t8, replaceGlobal: o, rewrite: n } = e;
return r.replace(new RegExp(t8, o ? "g" : ""), n);
});
var q8 = Ar(Du, tI);
var S_ = { kernelName: Du, backendName: "cpu", kernelFunc: q8 };
function Ff(r, e, t8, 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] * t8[p] + o[p];
n.set(e.get(...i), ...a);
}
return n;
}
var rI = class {
constructor(e, t8, o, n, s, a) {
this.separator = y.encodeString(e), this.nGramWidths = t8, 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, t8) {
let o = this.getPadWidth(t8);
return Math.max(0, e + 2 * o - t8 + 1);
}
createNGrams(e, t8, 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 = t8 + (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((C) => h[g++] = C);
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, t8) {
let o = e.length, n = t8.length;
if (n > 0) {
let p = t8[0];
if (p !== 0)
throw new Error(`First split value must be 0, got ${p}`);
for (let u = 1; u < n; ++u) {
let c = t8[u] >= p;
if (c = c && t8[u] <= o, !c)
throw new Error(`Invalid split value ${t8[u]}, must be in [${p}, ${o}]`);
p = t8[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 = t8[p] - t8[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 = t8[p], c = a[p];
if (this.nGramWidths.forEach((l) => {
let m = t8[p + 1] - t8[p], d = this.getNumNGrams(m, l);
this.createNGrams(e, u, i, c, d, l), c += d;
}), this.preserveShort && c === a[p]) {
let l = t8[p + 1] - t8[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 mp(r, e, t8, o, n, s, a, i) {
return new rI(t8, o, n, s, a, i).compute(r, e);
}
function j8(r, e, t8, 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);
(!t8 || i.length !== 0) && o.push(i), r = r.subarray(a + 1), a = r.indexOf(s);
}
(!t8 || 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);
(!t8 || a.length !== 0) && o.push(a), n = s + 1;
}
}
function dp(r, e, t8) {
let o = r.length, n = [], s = 0, a = 0, i = new Array(o);
for (let m = 0; m < o; ++m) {
let d = n.length;
j8(r[m], e, t8, 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 fp(r, e) {
let t8 = y.getArrayFromDType("int32", r.length);
for (let o = 0; o < r.length; ++o)
t8[o] = y.fingerPrint64(r[o]).modulo(e).getLowBitsUnsigned();
return t8;
}
var oI = ze((r, e) => r - e);
var X8 = yc((r, e, t8, o) => ({ real: r - t8, imag: e - o }));
var Ll = Ye(Ts, oI, X8);
var I_ = { kernelName: Ts, backendName: "cpu", kernelFunc: Ll };
function Pf(r, e) {
let t8 = new Array(r.rank);
for (let n = 0; n < t8.length; n++)
t8[n] = r.shape[n] * e[n];
let o = me(t8, 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 t8 = e.value - r.value;
return t8 === 0 ? r.index - e.index : t8;
};
function v_(r, e, t8 = 0, o = r.length - 1) {
for (; o > t8; ) {
if (o - t8 > 600) {
let i = o - t8 + 1, p = e - t8 + 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(t8, Math.floor(e - p * c / i + l)), d = Math.min(o, Math.floor(e + (i - p) * c / i + l));
v_(r, e, m, d);
}
let n = r[e], s = t8, a = o;
for (y.swap(r, t8, e), Bl(r[o], n) > 0 && y.swap(r, t8, 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[t8], n) === 0 ? y.swap(r, t8, a) : (a = a + 1, y.swap(r, a, o)), a <= e && (t8 = a + 1), e <= a && (o = a - 1);
}
}
function Of(r, e, t8, o, n) {
let s = e[e.length - 1], [a, i] = [r.length / s, s], p = y.getTypedArrayFromDType(t8, 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, C) => f[C] = { value: b, index: C }), o < f.length && (v_(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, t8, p), me(c, "int32", u)];
}
function hp(r, e, t8, o) {
let n = y.parseAxisParam(e, t8)[0], s = [1, t8[0], 1];
for (let f = 0; f < n; f++)
s[0] *= t8[f];
s[1] = t8[n];
for (let f = n + 1; f < t8.length; f++)
s[2] *= t8[f];
let a = /* @__PURE__ */ new Map(), i = new Int32Array(t8[n]), p = new tt(s, o, r), u = [], c = s[0] === 1 && s[2] === 1;
for (let f = 0; f < t8[n]; f++) {
let h;
if (c)
h = r[f].toString();
else {
let x = [];
for (let b = 0; b < s[0]; b++)
for (let C = 0; C < s[2]; C++)
x.push(p.get(b, f, C));
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 = t8.slice();
return d[n] = l[1], { outputValues: m.values, outputShape: d, indices: i };
}
var Y8 = "4.7.0";
nu("cpu", () => new hu(), 1);
var nI = Ie(hn, (r) => r >= 0 ? r : Math.exp(r) - 1);
var k_ = { kernelName: hn, backendName: "cpu", kernelFunc: nI };
function sI(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { alpha: s } = o;
Q([n], "leakyRelu");
let a = y.sizeFromShape(n.shape), i = t8.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 t8.makeTensorInfo(n.shape, "float32", p);
}
var N_ = { kernelName: En, backendName: "cpu", kernelFunc: sI };
var Q8 = ze((r, e) => r < 0 ? e * r : r);
function aI(r) {
let { inputs: e, backend: t8 } = r, { x: o, alpha: n } = e;
Q([o, n], "prelu");
let s = t8.data.get(o.dataId).values, a = t8.data.get(n.dataId).values, [i, p] = Q8(o.shape, n.shape, s, a, "float32");
return t8.makeTensorInfo(p, "float32", i);
}
var T_ = { kernelName: rs, backendName: "cpu", kernelFunc: aI };
var iI = Ie(ss, (r) => Math.max(0, r));
var __ = { kernelName: ss, backendName: "cpu", kernelFunc: iI };
var uI = Ie(us, (r) => Math.min(Math.max(0, r), 6));
var $_ = { kernelName: us, backendName: "cpu", kernelFunc: uI };
function gp(r, e, t8, o, n) {
if (t8 === "linear")
return lr({ inputs: { x: e }, backend: r });
if (t8 === "relu")
return iI({ inputs: { x: e }, backend: r });
if (t8 === "elu")
return nI({ inputs: { x: e }, backend: r });
if (t8 === "relu6")
return uI({ inputs: { x: e }, backend: r });
if (t8 === "prelu")
return aI({ inputs: { x: e, alpha: o }, backend: r });
if (t8 === "leakyrelu")
return sI({ inputs: { x: e }, backend: r, attrs: { alpha: n } });
if (t8 === "sigmoid")
return JS({ inputs: { x: e }, backend: r });
throw new Error(`Activation ${t8} has not been implemented for the CPU backend.`);
}
function Ve(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { shape: s } = o, a = y.sizeFromShape(n.shape), i = y.inferFromImplicitShape(s, a), p = y.sizeFromShape(i);
y.assert(a === p, () => `The new shape (${i}) has ${p} elements and the old shape (${n.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), t8.incRef(n.dataId);
let u = t8.data.get(n.dataId);
if (u.complexTensorInfos != null) {
let c = u.complexTensorInfos.real, l = u.complexTensorInfos.imag;
c.shape = i, l.shape = i;
}
return { dataId: n.dataId, shape: i, dtype: n.dtype };
}
var E_ = { kernelName: ma, backendName: "cpu", kernelFunc: Ve };
function pI(r) {
let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
Q([n, s], "matMul");
let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = Ir.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]);
y.assert(c === l, () => `Error in matMul: inner shapes (${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: t8, attrs: { shape: S } }), E = Ve({ inputs: { x: s }, backend: t8, attrs: { shape: k } }), R = a ? _.shape[1] : _.shape[2], D = a ? _.shape[2] : _.shape[1], P = i ? E.shape[1] : E.shape[2], O = Math.max(g, x), M = t8.data.get(_.dataId).values, L = t8.data.get(E.dataId).values, B = y.computeStrides(_.shape), z = y.computeStrides(E.shape), [U, j, q] = a ? [B[0], 1, B[1]] : [B[0], B[1], 1], [Y, J, re] = i ? [1, z[1], z[0]] : [z[1], 1, z[0]], ne = D * P, ee = me([O, D, P], _.dtype), oe = ee.values, ie = t8.blockSize;
for (let le = 0; le < O; le++) {
let be = 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 < P; Pe += ie) {
let st = Math.min(Pe + ie, P);
for (let lt = 0; lt < R; lt += ie) {
let Ue = Math.min(lt + ie, R);
for (let mt = ve; mt < Fe; mt++)
for (let it = Pe; it < st; it++) {
let gt = 0;
for (let xt = lt; xt < Ue; xt++) {
let Lr = M[be * U + mt * j + xt * q], Mt = L[xt * Y + it * J + _e * re];
gt += Lr * Mt;
}
oe[le * ne + (mt * P + it)] += gt;
}
}
}
}
}
return t8.disposeIntermediateTensorInfo(_), t8.disposeIntermediateTensorInfo(E), t8.makeTensorInfo(C, ee.dtype, ee.values);
}
var R_ = { kernelName: Zo, backendName: "cpu", kernelFunc: pI };
function Z8(r) {
let { inputs: e, backend: t8, 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 = pI({ inputs: { a: n, b: s }, attrs: { transposeA: p, transposeB: u }, backend: t8 }), a && (d = Aa({ inputs: { a: m, b: a }, backend: t8 }), h.push(m), m = d), c && (f = gp(t8, m, c, i, l), h.push(m), m = f);
for (let x of h)
t8.disposeIntermediateTensorInfo(x);
return m;
}
var D_ = { kernelName: So, backendName: "cpu", kernelFunc: Z8 };
var J8 = Ie(Vo, (r) => Math.acos(r));
var A_ = { kernelName: Vo, backendName: "cpu", kernelFunc: J8 };
var eY = Ie(Wo, (r) => Math.acosh(r));
var F_ = { kernelName: Wo, backendName: "cpu", kernelFunc: eY };
function tY(r) {
let { inputs: e, backend: t8 } = r, o = e;
Q(e, "addN");
let n = o.map((i) => t8.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 t8.makeTensorInfo(s.shape, s.dtype, s.values);
}
var P_ = { kernelName: Uo, backendName: "cpu", kernelFunc: tY };
function rY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
Q(n, "all");
let i = y.parseAxisParam(s, n.shape), p = i, u = w.getAxesPermutation(p, n.shape.length), c = n;
u != null && (c = It({ inputs: { x: n }, backend: t8, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("all", p, c.shape.length);
let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t8.data.get(c.dataId).values;
for (let x = 0; x < f.length; ++x) {
let b = x * d, C = h[b];
for (let S = 0; S < d; ++S) {
let k = h[b + S];
C = C && k;
}
f[x] = C;
}
u != null && t8.disposeIntermediateTensorInfo(c);
let g = t8.makeTensorInfo(l, c.dtype, f);
if (a) {
let x = w.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t8, attrs: { shape: x } });
return t8.disposeIntermediateTensorInfo(g), b;
}
return g;
}
var O_ = { kernelName: Go, backendName: "cpu", kernelFunc: rY };
function oY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
Q(n, "any");
let i = y.parseAxisParam(s, n.shape), p = i, u = w.getAxesPermutation(p, n.shape.length), c = n;
u != null && (c = It({ inputs: { x: n }, backend: t8, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("any", p, c.shape.length);
let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t8.data.get(c.dataId).values;
for (let x = 0; x < f.length; ++x) {
let b = x * d, C = h[b];
for (let S = 0; S < d; ++S) {
let k = h[b + S];
C = C || k;
}
f[x] = C;
}
u != null && t8.disposeIntermediateTensorInfo(c);
let g = t8.makeTensorInfo(l, c.dtype, f);
if (a) {
let x = w.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t8, attrs: { shape: x } });
return t8.disposeIntermediateTensorInfo(g), b;
}
return g;
}
var M_ = { kernelName: Ho, backendName: "cpu", kernelFunc: oY };
function nY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o;
Q(n, "argMax");
let a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = It({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], w.assertAxesAreInnerMostDims("argMax", a, p.shape.length);
let [c, l] = w.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t8.data.get(p.dataId).values;
for (let g = 0; g < d.length; ++g) {
let x = g * f, b = h[x], C = 0;
for (let S = 0; S < f; ++S) {
let k = h[x + S];
k > b && (b = k, C = S);
}
d[g] = C;
}
return u.forEach((g) => t8.disposeIntermediateTensorInfo(g)), t8.makeTensorInfo(c, "int32", d);
}
var L_ = { kernelName: Xs, backendName: "cpu", kernelFunc: nY };
function sY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o;
Q(n, "argMin");
let a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = It({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], w.assertAxesAreInnerMostDims("argMin", a, p.shape.length);
let [c, l] = w.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t8.data.get(p.dataId).values;
for (let g = 0; g < d.length; ++g) {
let x = g * f, b = h[x], C = 0;
for (let S = 0; S < f; ++S) {
let k = h[x + S];
k < b && (b = k, C = S);
}
d[g] = C;
}
return u.forEach((g) => t8.disposeIntermediateTensorInfo(g)), t8.makeTensorInfo(c, "int32", d);
}
var B_ = { kernelName: Ys, backendName: "cpu", kernelFunc: sY };
var aY = Ie(Ko, (r) => Math.asin(r));
var z_ = { kernelName: Ko, backendName: "cpu", kernelFunc: aY };
var iY = Ie(qo, (r) => Math.asinh(r));
var V_ = { kernelName: qo, backendName: "cpu", kernelFunc: iY };
var uY = Ie(jo, (r) => Math.atan(r));
var W_ = { kernelName: jo, backendName: "cpu", kernelFunc: uY };
var pY = ze((r, e) => Math.atan2(r, e));
var cY = Ye(Yo, pY);
var U_ = { kernelName: Yo, backendName: "cpu", kernelFunc: cY };
var lY = Ie(Xo, (r) => Math.atanh(r));
var G_ = { kernelName: Xo, backendName: "cpu", kernelFunc: lY };
function vc(r, e, t8, 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, t8), g = h.values, x = n.outShape[1] * n.outShape[2] * n.outShape[3], b = n.outShape[2] * n.outShape[3], C = 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, P = 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, q = 0, Y = 0;
for (let re = P; 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" && (q += ie, Y++);
}
if (isNaN(j))
break;
}
let J = M + L * C + E;
g[J] = s === "avg" ? q / Y : j;
}
}
}
return h;
}
function Mf(r, e, t8, 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, t8, 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 C = b * i - d, S = C;
for (; S < 0; )
S += u;
let k = Math.min(o.inHeight, l + C);
for (let _ = 0; _ < o.outWidth; ++_) {
let E = _ * p - f, R = E;
for (; R < 0; )
R += c;
let D = Math.min(o.inWidth, m + E), P = Number.NEGATIVE_INFINITY, O = -1;
for (let M = S; M < k; M += u) {
let L = M - C;
for (let B = R; B < D; B += c) {
let z = B - E, U = h.get(g, M, B, x);
U > P && (P = 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 Lf(r, e, t8, 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, C = me(n.outShape, t8), S = C.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 P = 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 = P + L * _;
for (let q = 0; q < n.outHeight; ++q) {
let Y = q * i - g, J = Y;
for (; J < 0; )
J += c;
let re = Math.min(n.inHeight, d + Y), ne = j + q * 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), be = 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 Ue = J; Ue < re; Ue += c) {
let mt = lt + Ue * o[2];
for (let it = ie; it < le; it += l) {
let gt = mt + it * o[3], xt = r[gt + M];
if (s === "max" && xt > _e ? _e = xt : s === "avg" && (ve += xt, Fe++), isNaN(_e))
break;
}
if (isNaN(_e))
break;
}
if (isNaN(_e))
break;
}
let Pe = be + M;
S[Pe] = s === "avg" ? ve / Math.max(Fe, 1) : _e;
}
}
}
}
return C;
}
function H_(r, e) {
let t8 = 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, C = b;
for (; C < 0; )
C += 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 P = D * s - f, O = P;
for (; O < 0; )
O += p;
let M = Math.min(e.inWidth, l + P), L = Number.NEGATIVE_INFINITY, B = -1;
for (let z = C; z < S; z += a) {
let U = z - b;
for (let j = E; j < R; j += i) {
let q = j - _;
for (let Y = O; Y < M; Y += p) {
let J = Y - P, re = r.get(h, z, j, Y, g);
re >= L && (L = re, B = U * c * l + q * c + J);
}
}
}
t8.set(B, h, x, k, D, g);
}
}
}
return t8;
}
function mY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e;
Q(n, "avgPool");
let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
let c = w.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: t8 });
else {
let m = t8.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = vc(m, n.shape, n.dtype, d, c, "avg");
l = t8.makeTensorInfo(c.outShape, n.dtype, f.values);
}
return l;
}
var K_ = { kernelName: Qo, backendName: "cpu", kernelFunc: mY };
function dY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o;
Q(n, "avgPool3d");
let c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t8.data.get(n.dataId).values, m = Lf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "avg");
return t8.makeTensorInfo(m.shape, "float32", m.values);
}
var q_ = { kernelName: Qs, backendName: "cpu", kernelFunc: dY };
function fY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o;
Q([n, s], "avgPool3DGrad");
let c = w.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, C = 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, P = me(s.shape, "float32"), O = 1 / (f * h * g), M = t8.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 q = z - E, Y = U - D, J = j - R, re = 0;
for (let ne = 0; ne < S; ne += x) {
let ee = (q + ne) / l;
if (!(ee < 0 || ee >= c.outDepth || Math.floor(ee) !== ee))
for (let oe = 0; oe < k; oe += b) {
let ie = (Y + oe) / m;
if (!(ie < 0 || ie >= c.outHeight || Math.floor(ie) !== ie))
for (let le = 0; le < _; le += C) {
let be = (J + le) / d;
if (be < 0 || be >= c.outWidth || Math.floor(be) !== be)
continue;
let _e = M.get(L, ee, ie, be, B);
re += _e;
}
}
}
P.set(re * O, L, z, U, j, B);
}
return t8.makeTensorInfo(P.shape, P.dtype, P.values);
}
var j_ = { kernelName: Ei, backendName: "cpu", kernelFunc: fY };
function hY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s;
Q([n, s], "avgPoolGrad");
let { filterSize: i, strides: p, pad: u } = o, c = w.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, C = b - 1 - c.padInfo.left, S = x - 1 - c.padInfo.top, k = me(a.shape, "float32"), _ = 1 / (d * f), E = t8.data.get(n.dataId).values, R = me(n.shape, "float32", E);
for (let D = 0; D < c.batchSize; ++D)
for (let P = 0; P < c.inChannels; ++P)
for (let O = 0; O < c.inHeight; ++O)
for (let M = 0; M < c.inWidth; ++M) {
let L = O - S, B = M - C, 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 q = 0; q < b; q += g) {
let Y = (B + q) / m;
if (Y < 0 || Y >= c.outWidth || Math.floor(Y) !== Y)
continue;
let J = R.get(D, j, Y, P);
z += J;
}
}
k.set(z * _, D, O, M, P);
}
return t8.makeTensorInfo(k.shape, k.dtype, k.values);
}
var X_ = { kernelName: $i, backendName: "cpu", kernelFunc: hY };
function gY(r) {
let { inputs: e, backend: t8, 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."), Q([n, i, p, s, a], "batchNorm");
let { varianceEpsilon: u } = o;
u == null && (u = 1e-3);
let c = t8.data.get(n.dataId).values, l = t8.data.get(i.dataId).values, m = t8.data.get(p.dataId).values, d = s ? t8.data.get(s.dataId).values : new Float32Array([1]), f = a ? t8.data.get(a.dataId).values : new Float32Array([0]), h = new Float32Array(c.length), g = f.length, x = d.length, b = m.length, C = 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 >= C && (k = 0), _ >= x && (_ = 0), E >= b && (E = 0);
return t8.makeTensorInfo(n.shape, n.dtype, h);
}
var Y_ = { kernelName: In, backendName: "cpu", kernelFunc: gY };
function xY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o;
Q([n], "batchToSpaceND");
let i = s.reduce((x, b) => x * b), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = Ve({ inputs: { x: n }, backend: t8, attrs: { shape: p } }), f = It({ inputs: { x: d }, backend: t8, attrs: { perm: u } }), h = Ve({ inputs: { x: f }, backend: t8, attrs: { shape: c } }), g = Ao({ inputs: { x: h }, backend: t8, attrs: { begin: l, size: m } });
return t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), t8.disposeIntermediateTensorInfo(h), g;
}
var Q_ = { kernelName: Zs, backendName: "cpu", kernelFunc: xY };
function yY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t8.data.get(n.dataId).values, p = t8.data.get(s.dataId).values, u = bc(i, p, s.dtype, s.shape, a);
return t8.makeTensorInfo([a], s.dtype, u);
}
var Z_ = { kernelName: Jo, backendName: "cpu", kernelFunc: yY };
function bY(r) {
let { inputs: e, backend: t8 } = r, { s0: o, s1: n } = e, s = t8.data.get(o.dataId).values, a = t8.data.get(n.dataId).values, i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a));
return t8.makeTensorInfo([i.length], "int32", Int32Array.from(i));
}
var J_ = { kernelName: Js, backendName: "cpu", kernelFunc: bY };
var CY = Ie(bo, (r, e) => {
let t8 = e;
return r > t8.clipValueMax ? t8.clipValueMax : r < t8.clipValueMin ? t8.clipValueMin : r;
});
var e$ = { kernelName: bo, backendName: "cpu", kernelFunc: CY };
var wY = (r) => {
let { x: e } = r.inputs, t8 = r.backend, o = new Float32Array(y.sizeFromShape(e.shape)), n = t8.data.get(e.dataId), s = n.complexTensorInfos.real, a = n.complexTensorInfos.imag, i = t8.data.get(s.dataId).values, p = t8.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 t8.makeOutput(o, e.shape, "float32");
};
var t$ = { kernelName: Di, backendName: "cpu", kernelFunc: wY };
function Fa(r) {
let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.data.get(o.dataId).complexTensorInfos.imag, s = t8.data.get(n.dataId).values;
return t8.makeTensorInfo(n.shape, n.dtype, s);
}
var r$ = { kernelName: Vi, backendName: "cpu", kernelFunc: Fa };
function gu(r) {
let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((h) => h.shape);
w.assertParamsConsistent(a, s);
let i = w.computeOutShape(e.map((h) => h.shape), s);
if (y.sizeFromShape(i) === 0)
return t8.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: t8 });
if (p[0].dtype === "complex64") {
let h = p.map((S) => Eo({ inputs: { input: S }, backend: t8 })), g = p.map((S) => Fa({ inputs: { input: S }, backend: t8 })), x = gu({ inputs: h, backend: t8, attrs: { axis: s } }), b = gu({ inputs: g, backend: t8, attrs: { axis: s } }), C = Kt({ inputs: { real: x, imag: b }, backend: t8 });
return h.forEach((S) => t8.disposeIntermediateTensorInfo(S)), g.forEach((S) => t8.disposeIntermediateTensorInfo(S)), t8.disposeIntermediateTensorInfo(x), t8.disposeIntermediateTensorInfo(b), C;
}
let u = p.map((h) => {
let x = [-1, y.sizeFromShape(h.shape.slice(s))];
return Ve({ inputs: { x: h }, backend: t8, attrs: { shape: x } });
}), c = u.map((h) => ({ vals: t8.data.get(h.dataId).values, shape: h.shape }));
i = w.computeOutShape(u.map((h) => h.shape), 1);
let l = u[0].shape[0] === 1, m = up(c, i, e[0].dtype, l), d = w.computeOutShape(p.map((h) => h.shape), s), f = t8.makeTensorInfo(d, e[0].dtype, m);
return u.forEach((h) => t8.disposeIntermediateTensorInfo(h)), f;
}
var o$ = { kernelName: ea, backendName: "cpu", kernelFunc: gu };
function cI(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o;
Q([n, s], "conv2d");
let l = w.convertConv2DDataFormat(p), m = w.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, C = m.dataFormat === "channelsLast", S = new tt(m.outShape, n.dtype), k = y.computeStrides(n.shape), _ = y.computeStrides(s.shape), E = k[0], R = C ? k[1] : k[2], D = C ? k[2] : 1, P = C ? 1 : k[1], O = S.strides[0], M = C ? S.strides[1] : S.strides[2], L = C ? S.strides[2] : 1, B = C ? 1 : S.strides[1], z = t8.data.get(n.dataId).values, U = t8.data.get(s.dataId).values, j = S.values;
for (let q = 0; q < m.batchSize; ++q) {
let Y = q * E, J = q * 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], be = Y + 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], Ue = be + st * D, mt = lt;
for (let it = 0; it < m.inChannels; ++it) {
let gt = z[Ue + it * P];
for (let xt = 0; xt < m.outChannels; ++xt)
j[ve + xt * B] += gt * U[mt + xt];
mt += m.outChannels;
}
}
}
}
}
}
return t8.makeTensorInfo(S.shape, S.dtype, j);
}
var n$ = { kernelName: tn, backendName: "cpu", kernelFunc: cI };
function SY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o;
Q([n, s], "conv2dBackpropFilter");
let l = w.convertConv2DDataFormat(p), m = w.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"), C = m.padInfo.left, S = m.padInfo.top, k = t8.data.get(n.dataId).values, _ = t8.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 P = 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((C - M) / f)), B = Math.min(m.outWidth, (m.inWidth + C - M) / f);
for (let z = 0; z < m.inChannels; ++z)
for (let U = 0; U < m.outChannels; ++U) {
let j = 0;
for (let q = 0; q < m.batchSize; ++q)
for (let Y = P; Y < O; ++Y) {
let J = D + Y * d - S;
for (let re = L; re < B; ++re) {
let ne = M + re * f - C;
x ? j += E.get(q, J, ne, z) * R.get(q, Y, re, U) : j += E.get(q, z, J, ne) * R.get(q, U, Y, re);
}
}
b.set(j, D, M, z, U);
}
}
}
return t8.makeTensorInfo(b.shape, b.dtype, b.values);
}
var s$ = { kernelName: Ai, backendName: "cpu", kernelFunc: SY };
function IY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o;
Q([n, s], "conv2dBackpropInput");
let l = y.computeStrides(s.shape), m = y.computeStrides(n.shape), d = w.convertConv2DDataFormat(u), f = w.computeConv2DInfo(a, s.shape, i, 1, p, c, false, d), h = new tt(f.inShape, "float32"), g = h.values, x = t8.data.get(n.dataId).values, b = t8.data.get(s.dataId).values, [C, S, k] = l, { batchSize: _, filterHeight: E, filterWidth: R, inChannels: D, inHeight: P, inWidth: O, outChannels: M, outHeight: L, outWidth: B, strideHeight: z, strideWidth: U } = f;
d = f.dataFormat;
let j = E - 1 - f.padInfo.top, q = R - 1 - f.padInfo.left, Y = d === "channelsLast", J = h.strides[0], re = Y ? h.strides[1] : h.strides[2], ne = Y ? h.strides[2] : 1, ee = Y ? 1 : h.strides[1], oe = m[0], ie = Y ? m[1] : m[2], le = Y ? m[2] : 1, be = Y ? 1 : m[1];
for (let _e = 0; _e < _; ++_e)
for (let ve = 0; ve < D; ++ve)
for (let Fe = 0; Fe < P; ++Fe) {
let Pe = Fe - j, st = Math.max(0, Math.ceil(Pe / z)), lt = Math.min(L, (E + Pe) / z);
for (let Ue = 0; Ue < O; ++Ue) {
let mt = Ue - q, it = Math.max(0, Math.ceil(mt / U)), gt = Math.min(B, (R + mt) / U), xt = 0;
for (let Mt = st; Mt < lt; ++Mt) {
let eo = Mt * z - Pe;
for (let or = it; or < gt; ++or) {
let Tt = or * U - mt, nr = oe * _e + ie * Mt + le * or, sr = C * (E - 1 - eo) + S * (R - 1 - Tt) + k * ve;
for (let to = 0; to < M; ++to) {
let ro = x[nr + be * to], fr = b[sr + to];
xt += ro * fr;
}
}
}
let Lr = J * _e + re * Fe + ne * Ue + ee * ve;
g[Lr] = xt;
}
}
return t8.makeTensorInfo(h.shape, h.dtype, h.values);
}
var a$ = { kernelName: rn, backendName: "cpu", kernelFunc: IY };
function vY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o;
Q([n, s], "conv3d");
let u = w.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, C = g.top, S = new tt(u.outShape, n.dtype), k = t8.data.get(n.dataId).values, _ = t8.data.get(s.dataId).values, E = S.values, R = y.computeStrides(n.shape), D = y.computeStrides(s.shape);
for (let P = 0; P < u.batchSize; ++P) {
let O = P * R[0], M = P * 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 q = U * D[0], Y = O + j * R[1];
for (let J = 0; J < u.outHeight; ++J) {
let re = B + J * S.strides[2], ne = J * u.strideHeight - C;
for (let ee = 0; ee < l; ++ee) {
let oe = ne + ee * f;
if (oe < 0 || oe >= u.inHeight)
continue;
let ie = q + ee * D[1], le = Y + oe * R[2];
for (let be = 0; be < u.outWidth; ++be) {
let _e = re + be * u.outChannels, ve = be * 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, Ue = st;
for (let mt = 0; mt < u.inChannels; ++mt) {
let it = k[lt + mt];
for (let gt = 0; gt < u.outChannels; ++gt)
E[_e + gt] += it * _[Ue + gt];
Ue += u.outChannels;
}
}
}
}
}
}
}
}
return t8.makeTensorInfo(S.shape, S.dtype, S.values);
}
var i$ = { kernelName: on, backendName: "cpu", kernelFunc: vY };
function kY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o;
Q([n, s], "conv3dBackpropFilterV2");
let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = w.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"), C = b.values, [S, k, _, E] = b.strides, R = t8.data.get(s.dataId).values, [D, P, O, M] = c, L = t8.data.get(n.dataId).values, [B, z, U, j] = u, q = l.padInfo.front, Y = l.padInfo.left, J = l.padInfo.top;
for (let re = 0; re < h; ++re) {
let ne = Math.max(0, Math.ceil((q - re) / m)), ee = Math.min(l.outDepth, (l.inDepth + q - re) / m), oe = re * S;
for (let ie = 0; ie < g; ++ie) {
let le = Math.max(0, Math.ceil((J - ie) / d)), be = 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((Y - ve) / f)), Pe = Math.min(l.outWidth, (l.inWidth + Y - ve) / f), st = ve * _ + _e;
for (let lt = 0; lt < l.inChannels; ++lt) {
let Ue = lt * E + st;
for (let mt = 0; mt < l.outChannels; ++mt) {
let it = 0;
for (let gt = 0; gt < l.batchSize; ++gt) {
let xt = gt * B, Lr = gt * D;
for (let Mt = ne; Mt < ee; ++Mt) {
let or = (re + Mt * m - q) * z + xt, Tt = Mt * P + Lr;
for (let nr = le; nr < be; ++nr) {
let to = (ie + nr * d - J) * U + or, ro = nr * O + Tt;
for (let fr = Fe; fr < Pe; ++fr) {
let Lo = (ve + fr * f - Y) * j + to, Hs = fr * M + ro;
it += L[Lo + lt] * R[Hs + mt];
}
}
}
}
C[Ue + mt] = it;
}
}
}
}
}
return t8.makeTensorInfo(b.shape, b.dtype, b.values);
}
var u$ = { kernelName: Ka, backendName: "cpu", kernelFunc: kY };
function NY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o;
Q([n], "conv3dBackpropInputV2");
let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = w.computeConv3DInfo(p, s.shape, i, 1, a), m = new tt(l.inShape, "float32"), d = m.values, [f, h, g, x] = m.strides, b = t8.data.get(n.dataId).values, [C, S, k, _] = u, E = t8.data.get(s.dataId).values, [R, D, P, O] = c, { batchSize: M, filterDepth: L, filterHeight: B, filterWidth: z, inChannels: U, inDepth: j, inHeight: q, inWidth: Y, outChannels: J, outDepth: re, outHeight: ne, outWidth: ee, strideDepth: oe, strideHeight: ie, strideWidth: le } = l, be = 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 - be, Ue = Math.max(0, Math.ceil(lt / oe)), mt = Math.min(re, (L + lt) / oe);
for (let it = 0; it < q; ++it) {
let gt = it - _e, xt = Math.max(0, Math.ceil(gt / ie)), Lr = Math.min(ne, (B + gt) / ie);
for (let Mt = 0; Mt < Y; ++Mt) {
let eo = Mt - ve, or = Math.max(0, Math.ceil(eo / le)), Tt = Math.min(ee, (z + eo) / le), nr = 0;
for (let sr = Ue; sr < mt; ++sr) {
let to = sr * oe - lt;
for (let ro = xt; ro < Lr; ++ro) {
let fr = ro * ie - gt;
for (let Ba = or; Ba < Tt; ++Ba) {
let Lo = Ba * le - eo, Hs = C * Fe + S * sr + k * ro + _ * Ba, Yt = R * (L - 1 - to) + D * (B - 1 - fr) + P * (z - 1 - Lo) + O * Pe;
for (let za = 0; za < J; ++za) {
let tl = b[Hs + za], rl = E[Yt + za];
nr += tl * rl;
}
}
}
}
d[f * Fe + h * st + g * it + x * Mt + Pe] = nr;
}
}
}
return t8.makeTensorInfo(m.shape, m.dtype, m.values);
}
var p$ = { kernelName: nn, backendName: "cpu", kernelFunc: NY };
var TY = Ie(sn, (r) => Math.cos(r));
var c$ = { kernelName: sn, backendName: "cpu", kernelFunc: TY };
var _Y = Ie(an, (r) => Math.cosh(r));
var l$ = { kernelName: an, backendName: "cpu", kernelFunc: _Y };
function $Y(r) {
let { inputs: e, backend: t8, 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 = t8.data.get(s.dataId).values, C = t8.data.get(a.dataId).values, S = t8.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], P = b[R + 1], O = b[R + 2], M = b[R + 3], L = C[E];
if (L >= c)
continue;
let B = h > 1 ? (O - D) * (l - 1) / (h - 1) : 0, z = g > 1 ? (M - P) * (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 q = 0; q < g; q++)
for (let Y = 0; Y < d; Y++) {
let J = Y + q * _[2] + U * _[1] + E * _[0];
x.values[J] = u;
}
continue;
}
if (p === "bilinear") {
let q = Math.floor(j), Y = Math.ceil(j), J = j - q;
for (let re = 0; re < g; re++) {
let ne = g > 1 ? P * (m - 1) + re * z : 0.5 * (P + M) * (m - 1);
if (ne < 0 || ne > m - 1) {
for (let le = 0; le < d; le++) {
let be = le + re * _[2] + U * _[1] + E * _[0];
x.values[be] = u;
}
continue;
}
let ee = Math.floor(ne), oe = Math.ceil(ne), ie = ne - ee;
for (let le = 0; le < d; le++) {
let be = le + ee * k[2] + q * k[1] + L * k[0], _e = S[be];
be = le + oe * k[2] + q * k[1] + L * k[0];
let ve = S[be];
be = le + ee * k[2] + Y * k[1] + L * k[0];
let Fe = S[be];
be = le + oe * k[2] + Y * k[1] + L * k[0];
let Pe = S[be], st = _e + (ve - _e) * ie, lt = Fe + (Pe - Fe) * ie;
be = le + re * _[2] + U * _[1] + E * _[0], x.values[be] = st + (lt - st) * J;
}
}
} else
for (let q = 0; q < g; ++q) {
let Y = g > 1 ? P * (m - 1) + q * z : 0.5 * (P + M) * (m - 1);
if (Y < 0 || Y > m - 1) {
for (let ne = 0; ne < d; ne++) {
let ee = ne + q * _[2] + U * _[1] + E * _[0];
x.values[ee] = u;
}
continue;
}
let J = Math.round(Y), 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 + q * _[2] + U * _[1] + E * _[0];
x.values[oe] = S[ee];
}
}
}
}
return t8.makeTensorInfo(x.shape, x.dtype, x.values);
}
var m$ = { kernelName: cn, backendName: "cpu", kernelFunc: $Y };
function EY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
Q(n, "cumprod");
let p = w.getAxesPermutation([s], n.shape.length), u = n;
p != null && (u = It({ inputs: { x: n }, backend: t8, attrs: { perm: p } }));
let c = w.getInnerMostAxes(1, n.shape.length)[0];
if (c !== u.shape.length - 1)
throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`);
let l = dt(u.dtype, "int32"), m = y.makeOnesTypedArray(y.sizeFromShape(u.shape), l), d = t8.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b;
for (let x = 0; x < d.length; x += f)
for (let b = 0; b < f; b++) {
let C = h(x, b);
if (b === 0)
m[C] = a ? 1 : d[C];
else {
let S = h(x, b - 1);
m[C] = a ? d[S] * m[S] : d[C] * m[S];
}
}
let g = t8.makeTensorInfo(u.shape, l, m);
if (p != null) {
let x = w.getUndoAxesPermutation(p), b = It({ inputs: { x: g }, backend: t8, attrs: { perm: x } });
return t8.disposeIntermediateTensorInfo(g), t8.disposeIntermediateTensorInfo(u), b;
}
return g;
}
var d$ = { kernelName: un, backendName: "cpu", kernelFunc: EY };
function RY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
Q(n, "cumsum");
let p = w.getAxesPermutation([s], n.shape.length), u = n;
p != null && (u = It({ inputs: { x: n }, backend: t8, attrs: { perm: p } }));
let c = w.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 = t8.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 C = h(x, b);
if (b === 0)
m[C] = a ? 0 : d[C];
else {
let S = h(x, b - 1);
m[C] = a ? d[S] + m[S] : d[C] + m[S];
}
}
let g = t8.makeTensorInfo(u.shape, l, m);
if (p != null) {
let x = w.getUndoAxesPermutation(p), b = It({ inputs: { x: g }, backend: t8, attrs: { perm: x } });
return t8.disposeIntermediateTensorInfo(g), t8.disposeIntermediateTensorInfo(u), b;
}
return g;
}
var f$ = { kernelName: pn, backendName: "cpu", kernelFunc: RY };
function DY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o;
if (n.shape.length === 1) {
let p = t8.data.get(n.dataId).values, u = t8.data.get(s.dataId).values, c = bc(p, u, s.dtype, s.shape, a);
return t8.makeTensorInfo([a], s.dtype, c);
} else if (n.shape.length === 2) {
let p = t8.bufferSync(n), u = t8.bufferSync(s), c = vf(p, u, a, i);
return t8.makeTensorInfo(c.shape, s.dtype, c.values);
}
throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`);
}
var h$ = { kernelName: ta, backendName: "cpu", kernelFunc: DY };
function AY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o;
y.assert(a === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);
let i = n.shape[0], p = n.shape[1], u = n.shape[2], c = n.shape[3], l = p * s, m = u * s, d = c / (s * s), f = t8.data.get(n.dataId).values, h = new Float32Array(i * l * m * d), g = 0;
for (let x = 0; x < i; ++x)
for (let b = 0; b < l; ++b) {
let C = Math.floor(b / s), S = b % s;
for (let k = 0; k < m; ++k) {
let _ = Math.floor(k / s), E = k % s, R = (S * s + E) * d;
for (let D = 0; D < d; ++D) {
let O = D + R + c * (_ + u * (C + p * x));
h[g++] = f[O];
}
}
}
return t8.makeTensorInfo([i, l, m, d], n.dtype, h);
}
var g$ = { kernelName: ln, backendName: "cpu", kernelFunc: AY };
function lI(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o;
Q([n, s], "depthwiseConv2DNative");
let c = y.computeStrides(n.shape), l = y.computeStrides(s.shape), m = p;
m == null && (m = [1, 1]), y.assert(w.eitherStridesOrDilationsAreOne(a, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);
let d = w.computeConv2DInfo(n.shape, s.shape, a, m, i, u, true), { filterHeight: f, filterWidth: h, dilationHeight: g, dilationWidth: x, padInfo: b } = d, C = b.left, S = b.top, k = d.outChannels / d.inChannels, _ = new tt(d.outShape, n.dtype), E = t8.data.get(n.dataId).values, R = t8.data.get(s.dataId).values, D = _.values;
for (let P = 0; P < d.batchSize; ++P) {
let O = P * c[0], M = P * _.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 q = U * l[0], Y = O + j * c[1];
for (let J = 0; J < d.outWidth; ++J) {
let re = B + J * _.strides[2], ne = J * d.strideWidth - C;
for (let ee = 0; ee < h; ++ee) {
let oe = ne + ee * x;
if (oe < 0 || oe >= d.inWidth)
continue;
let ie = q + ee * l[1], le = Y + oe * d.inChannels, be = re, _e = ie;
for (let ve = 0; ve < d.inChannels; ++ve) {
let Fe = E[le + ve];
for (let Pe = 0; Pe < k; ++Pe)
D[be + Pe] += Fe * R[_e + Pe];
be += k, _e += k;
}
}
}
}
}
}
return t8.makeTensorInfo(_.shape, _.dtype, _.values);
}
var x$ = { kernelName: mn, backendName: "cpu", kernelFunc: lI };
function FY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o;
Q([n, s], "depthwiseConv2dNativeBackpropFilter");
let l = w.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, C = l.outChannels / l.inChannels, S = t8.data.get(n.dataId).values, k = new tt(n.shape, n.dtype, S), _ = t8.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)), P = 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 / C), U = B % C, j = 0;
for (let q = 0; q < l.batchSize; ++q)
for (let Y = D; Y < P; ++Y) {
let J = R + Y * m - b;
for (let re = M; re < L; ++re) {
let ne = O + re * d - x;
j += k.get(q, J, ne, z) * E.get(q, Y, re, B);
}
}
g.set(j, R, O, z, U);
}
}
}
return t8.makeTensorInfo(g.shape, g.dtype, g.values);
}
var y$ = { kernelName: Fi, backendName: "cpu", kernelFunc: FY };
function PY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o;
Q([n, s], "depthwiseConv2DNativeBackpropInput");
let l = y.computeStrides(n.shape), m = y.computeStrides(s.shape), d = w.computeConv2DInfo(c, s.shape, a, i, p, u, true), f = new tt(d.inShape, "float32"), h = f.values, [g, x, b] = f.strides, C = t8.data.get(n.dataId).values, [S, k, _] = l, E = t8.data.get(s.dataId).values, [R, D, P] = m, { batchSize: O, filterHeight: M, filterWidth: L, inChannels: B, inHeight: z, inWidth: U, outChannels: j, outHeight: q, outWidth: Y, 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 be = 0; be < z; ++be) {
let _e = be - ne, ve = Math.max(0, Math.ceil(_e / J)), Fe = Math.min(q, (M + _e) / J);
for (let Pe = 0; Pe < U; ++Pe) {
let st = Pe - ee, lt = Math.max(0, Math.ceil(st / re)), Ue = Math.min(Y, (L + st) / re), mt = 0;
for (let it = ve; it < Fe; ++it) {
let gt = it * J - _e;
for (let xt = lt; xt < Ue; ++xt) {
let Lr = xt * re - st, Mt = S * ie + k * it + _ * xt, eo = R * (M - 1 - gt) + D * (L - 1 - Lr) + P * le;
for (let or = 0; or < oe; ++or) {
let Tt = le * oe + or, nr = C[Mt + Tt], sr = E[eo + or];
mt += nr * sr;
}
}
}
h[g * ie + x * be + b * Pe + le] = mt;
}
}
return t8.makeTensorInfo(f.shape, f.dtype, f.values);
}
var b$ = { kernelName: Pi, backendName: "cpu", kernelFunc: PY };
function OY(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t8.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 t8.makeTensorInfo(p, a.dtype, a.values);
}
var C$ = { kernelName: ra, backendName: "cpu", kernelFunc: OY };
var w$ = { kernelName: dn, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t8 }) => {
let { x: o, filter: n } = r, { strides: s, pad: a, dilations: i } = t8, 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: C, strideHeight: S, strideWidth: k, filterHeight: _, filterWidth: E, dilationHeight: R, dilationWidth: D, outShape: P } = w.computeDilation2DInfo(o.shape, n.shape, s, a, "NHWC", i), O = y.sizeFromShape(P), M = P.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 - C.top;
for (let q = 0; q < b; ++q) {
let Y = q * k - C.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 = Y + ie * D;
if (le >= 0 && le < h) {
let be = y.locToIndex([z, oe, le, J], c, y.computeStrides(o.shape)), _e = y.locToIndex([ee, ie, J], m, y.computeStrides(n.shape)), ve = u[be] + l[_e];
ve > re && (re = ve);
}
}
}
let ne = y.locToIndex([z, U, q, J], M, y.computeStrides(P));
L[ne] = re;
}
}
}
return { dataId: p.write(y.toTypedArray(L, o.dtype), P, o.dtype), shape: P, dtype: o.dtype };
} };
var S$ = { kernelName: Mi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t8 }) => {
let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t8, 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: C, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = w.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p);
y.assert(s.rank === D.length, () => `Error in ${Mi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);
let P = y.toNestedArray(D, u.data.get(s.dataId).values), O = y.makeZerosNestedTypedArray(n.shape, n.dtype);
for (let L = 0; L < m; ++L)
for (let B = 0; B < g; ++B) {
let z = B * C - b.top;
for (let U = 0; U < x; ++U) {
let j = U * S - b.left;
for (let q = 0; q < h; ++q) {
let Y = Number.MIN_SAFE_INTEGER, J = 0, re = 0;
for (let ne = 0; ne < k; ++ne) {
let ee = z + ne * E;
if (ee >= 0 && ee < d)
for (let oe = 0; oe < _; ++oe) {
let ie = j + oe * R;
if (ie >= 0 && ie < f) {
let le = c[L][ee][ie][q] + l[ne][oe][q];
le > Y && (Y = le, J = ne, re = oe);
}
}
}
O[J][re][q] += P[L][B][U][q];
}
}
}
return { dataId: u.write(y.toTypedArray(O, o.dtype), n.shape, n.dtype), shape: n.shape, dtype: n.dtype };
} };
var I$ = { kernelName: Oi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t8 }) => {
let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t8, 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: C, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = w.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p);
y.assert(s.rank === D.length, () => `Error in ${Oi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);
let P = 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 * C - b.top;
for (let U = 0; U < x; ++U) {
let j = U * S - b.left;
for (let q = 0; q < h; ++q) {
let Y = 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][q] + l[ne][oe][q];
le > Y && (Y = le, J = ee, re = ie);
}
}
}
O[L][J][re][q] += P[L][B][U][q];
}
}
}
return { dataId: u.write(y.toTypedArray(O, o.dtype), o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
function MY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { image: n } = e, { canvas: s, options: a } = o, { contextOptions: i, imageOptions: p } = a || {}, u = (p == null ? void 0 : p.alpha) || 1, c = (i == null ? void 0 : i.contextType) || "2d";
if (c !== "2d")
throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);
let l = s.getContext(c, (i == null ? void 0 : i.contextAttributes) || {});
if (l == null)
throw new Error(`Could not get the context with ${c} type.`);
let [m, d] = n.shape.slice(0, 2), f = n.shape.length === 2 ? 1 : n.shape[2], h = t8.data.get(n.dataId).values, g = n.dtype === "float32" ? 255 : 1, x = new Uint8ClampedArray(d * m * 4);
for (let C = 0; C < m * d; ++C) {
let S = [0, 0, 0, 255 * u];
for (let _ = 0; _ < f; _++) {
let E = h[C * f + _];
if (n.dtype === "float32") {
if (E < 0 || E > 1)
throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${E}.`);
} else if (n.dtype === "int32" && (E < 0 || E > 255))
throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${E}.`);
f === 1 ? (S[0] = E * g, S[1] = E * g, S[2] = E * g) : S[_] = E * g;
}
let k = C * 4;
x[k + 0] = Math.round(S[0]), x[k + 1] = Math.round(S[1]), x[k + 2] = Math.round(S[2]), x[k + 3] = Math.round(S[3]);
}
s.width = d, s.height = m;
let b = new ImageData(x, d, m);
return l.putImageData(b, 0, 0), n;
}
var v$ = { kernelName: qm, backendName: "cpu", kernelFunc: MY };
function fi(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
Q(n, "sum");
let i;
n.dtype === "bool" ? i = Ro({ inputs: { x: n }, backend: t8, attrs: { dtype: "int32" } }) : i = lr({ inputs: { x: n }, backend: t8 });
let p = i.shape.length, u = y.parseAxisParam(s, i.shape), c = w.getAxesPermutation(u, p), l = u, m = i;
c != null && (m = It({ inputs: { x: i }, backend: t8, attrs: { perm: c } }), l = w.getInnerMostAxes(l.length, p)), w.assertAxesAreInnerMostDims("sum", l, m.shape.length);
let [d, f] = w.computeOutAndReduceShapes(m.shape, l), h = w.upcastType(m.dtype, "int32"), g = xc(t8, d, h), x = y.sizeFromShape(f), b = t8.data.get(g.dataId).values, C = t8.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)
_ += C[k + E];
b[S] = _;
}
if (a) {
let S = w.expandShapeToKeepDim(g.shape, u), k = g;
g = Ve({ inputs: { x: g }, backend: t8, attrs: { shape: S } }), t8.disposeIntermediateTensorInfo(k);
}
return t8.disposeIntermediateTensorInfo(i), c != null && t8.disposeIntermediateTensorInfo(m), g;
}
var k$ = { kernelName: Ss, backendName: "cpu", kernelFunc: fi };
function LY(r) {
let { inputs: e, backend: t8, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length);
w.checkEinsumDimSizes(a.length, p, s);
let { path: u, steps: c } = w.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 } = w.getEinsumPermutation(d, p[g]), C;
w.isIdentityPermutation(x) ? C = s[g] : (C = It({ inputs: { x: s[g] }, backend: t8, attrs: { perm: x } }), f.push(C));
let S = C.shape.slice();
for (let k = 0; k < b.length; ++k)
S.splice(b[k], 0, 1);
y.arraysEqual(C.shape, S) || (C = Ve({ inputs: { x: C }, backend: t8, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = pp({ inputs: { a: C, b: m }, backend: t8 }), f.push(m));
}
h < l - 1 && (u[h] >= 0 && (m = fi({ inputs: { x: m }, backend: t8, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--);
}
for (let h of f)
h !== m && t8.disposeIntermediateTensorInfo(h);
return m;
}
var N$ = { kernelName: Li, backendName: "cpu", kernelFunc: LY };
function BY(r) {
let { inputs: e, backend: t8 } = r, { dy: o, y: n } = e;
Q([o, n], "eluGrad");
let s = new Float32Array(y.sizeFromShape(n.shape)), a = t8.data.get(n.dataId).values, i = t8.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 t8.makeTensorInfo(n.shape, "float32", s);
}
var T$ = { kernelName: qa, backendName: "cpu", kernelFunc: BY };
var zY = w.ERF_P;
var VY = w.ERF_A1;
var WY = w.ERF_A2;
var UY = w.ERF_A3;
var GY = w.ERF_A4;
var HY = w.ERF_A5;
var KY = Ie(gn, (r) => {
let e = Math.sign(r), t8 = Math.abs(r), o = 1 / (1 + zY * t8);
return e * (1 - ((((HY * o + GY) * o + UY) * o + WY) * o + VY) * o * Math.exp(-t8 * t8));
});
var _$ = { kernelName: gn, backendName: "cpu", kernelFunc: KY };
function kc(r) {
let { inputs: e, backend: t8, 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: t8, attrs: { shape: i } });
}
var $$ = { kernelName: oa, backendName: "cpu", kernelFunc: kc };
var qY = ze((r, e) => r / e);
var zl = Ye(fn, qY);
var Vl = { kernelName: fn, backendName: "cpu", kernelFunc: zl };
function Bf(r, e, t8) {
let o = r.shape, n = o[0], s = o[1], a = t8.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 = Ao({ inputs: { x: i }, backend: t8, attrs: { begin: [g, 0], size: [1, s] } }), b = Ao({ inputs: { x: p }, backend: t8, attrs: { begin: [g, 0], size: [1, s] } }), C = Kt({ inputs: { real: x, imag: b }, backend: t8 }), { real: S, imag: k } = jY(C, e, t8), _ = w.mergeRealAndImagArrays(S, k);
for (let E = 0; E < s; E++) {
let R = w.getComplexWithIndex(_, E);
l[g * s + E] = R.real, m[g * s + E] = R.imag;
}
t8.disposeIntermediateTensorInfo(x), t8.disposeIntermediateTensorInfo(b), t8.disposeIntermediateTensorInfo(C);
}
let d = t8.makeTensorInfo(u, "float32", l), f = t8.makeTensorInfo(u, "float32", m), h = Kt({ inputs: { real: d, imag: f }, backend: t8 });
return t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), h;
}
function jY(r, e, t8) {
let o = y.sizeFromShape(r.shape), n = t8.data.get(r.dataId), s = t8.data.get(n.complexTensorInfos.real.dataId).values, a = t8.data.get(n.complexTensorInfos.imag.dataId).values;
if (XY(o)) {
let i = mI(s, a, o, e, t8), p = [r.shape[0], r.shape[1]];
if (e) {
let u = t8.makeTensorInfo(p, "float32", i.real), c = t8.makeTensorInfo(p, "float32", i.imag), l = t8.makeTensorInfo([], "float32", y.createScalarValue(o, "float32")), m = lr({ inputs: { x: l }, backend: t8 }), d = Vl.kernelFunc({ inputs: { a: u, b: l }, backend: t8 }), f = Vl.kernelFunc({ inputs: { a: c, b: m }, backend: t8 }), h = t8.data.get(d.dataId).values, g = t8.data.get(f.dataId).values;
return t8.disposeIntermediateTensorInfo(u), t8.disposeIntermediateTensorInfo(c), t8.disposeIntermediateTensorInfo(l), t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), { real: h, imag: g };
}
return i;
} else {
let i = w.mergeRealAndImagArrays(s, a), p = YY(i, o, e);
return w.splitRealAndImagArrays(p);
}
}
function XY(r) {
return (r & r - 1) === 0;
}
function mI(r, e, t8, o, n) {
if (t8 === 1)
return { real: r, imag: e };
let s = w.mergeRealAndImagArrays(r, e), a = t8 / 2, i = w.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 = w.complexWithOddIndex(s), h = f.real, g = f.imag, x = [h.length], b = n.makeTensorInfo(x, "float32", h), C = n.makeTensorInfo(x, "float32", g), S = Kt({ inputs: { real: b, imag: C }, backend: n }), k = mI(p, u, a, o, n), _ = k.real, E = k.imag, R = [_.length], D = n.makeTensorInfo(R, "float32", _), P = n.makeTensorInfo(R, "float32", E), O = Kt({ inputs: { real: D, imag: P }, backend: n }), M = mI(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), q = Kt({ inputs: { real: U, imag: j }, backend: n }), Y = w.exponents(t8, o), J = [Y.real.length], re = n.makeTensorInfo(J, "float32", Y.real), ne = n.makeTensorInfo(J, "float32", Y.imag), ee = Kt({ inputs: { real: re, imag: ne }, backend: n }), oe = pp({ inputs: { a: ee, b: q }, backend: n }), ie = Aa({ inputs: { a: O, b: oe }, backend: n }), le = Ll({ inputs: { a: O, b: oe }, backend: n }), be = Eo({ inputs: { input: ie }, backend: n }), _e = Eo({ inputs: { input: le }, backend: n }), ve = Fa({ inputs: { input: ie }, backend: n }), Fe = Fa({ inputs: { input: le }, backend: n }), Pe = gu({ inputs: [be, _e], backend: n, attrs: { axis: 0 } }), st = gu({ inputs: [ve, Fe], backend: n, attrs: { axis: 0 } }), lt = n.data.get(Pe.dataId).values, Ue = n.data.get(st.dataId).values;
return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(C), n.disposeIntermediateTensorInfo(S), n.disposeIntermediateTensorInfo(D), n.disposeIntermediateTensorInfo(P), n.disposeIntermediateTensorInfo(O), n.disposeIntermediateTensorInfo(U), n.disposeIntermediateTensorInfo(j), n.disposeIntermediateTensorInfo(q), n.disposeIntermediateTensorInfo(re), n.disposeIntermediateTensorInfo(ne), n.disposeIntermediateTensorInfo(ee), n.disposeIntermediateTensorInfo(oe), n.disposeIntermediateTensorInfo(ie), n.disposeIntermediateTensorInfo(le), n.disposeIntermediateTensorInfo(be), n.disposeIntermediateTensorInfo(ve), n.disposeIntermediateTensorInfo(_e), n.disposeIntermediateTensorInfo(Fe), n.disposeIntermediateTensorInfo(Pe), n.disposeIntermediateTensorInfo(st), { real: lt, imag: Ue };
}
function YY(r, e, t8) {
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 = w.exponent(n * i, e, t8), u = w.getComplexWithIndex(r, i);
s += u.real * p.real - u.imag * p.imag, a += u.real * p.imag + u.imag * p.real;
}
t8 && (s /= e, a /= e), w.assignToTypedArray(o, s, a, n);
}
return o;
}
function QY(r) {
let { inputs: e, backend: t8 } = 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: t8, attrs: { shape: [a, s] } }), p = Bf(i, false, t8), u = Ve({ inputs: { x: p }, backend: t8, attrs: { shape: o.shape } });
return t8.disposeIntermediateTensorInfo(i), t8.disposeIntermediateTensorInfo(p), u;
}
var E$ = { kernelName: Bi, backendName: "cpu", kernelFunc: QY };
function Wl(r) {
let { backend: e, attrs: t8 } = r, { shape: o, value: n, dtype: s } = t8, a = s || y.inferDtype(n), i = y.getArrayFromDType(a, y.sizeFromShape(o));
return ZY(i, n, a), e.makeTensorInfo(o, a, i);
}
var R$ = { kernelName: na, backendName: "cpu", kernelFunc: Wl };
function ZY(r, e, t8) {
r.fill(e);
}
var D$ = { kernelName: Cn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => {
let { image: o } = r, n = t8, 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 C = Math.round(p - g - 1), S = d + h + x + b, k = c[S];
if (C >= 0 && C < p) {
let _ = C * 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 JY(r) {
let { inputs: e, backend: t8, 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 = cI({ inputs: { x: n, filter: s }, backend: t8, 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: t8, attrs: { shape: [a.shape[0], 1, 1] } });
h = Aa({ inputs: { a: h, b: x }, backend: t8 }), t8.disposeIntermediateTensorInfo(x);
} else
h = Aa({ inputs: { a: h, b: a }, backend: t8 });
t8.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: t8, attrs: { shape: [i.shape[0], 1, 1] } });
h = gp(t8, h, d, x, f), t8.disposeIntermediateTensorInfo(x);
} else
h = gp(t8, h, d, i, f);
t8.disposeIntermediateTensorInfo(g);
}
return h;
}
var A$ = { kernelName: Io, backendName: "cpu", kernelFunc: JY };
function e7(r) {
let { inputs: e, backend: t8, 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 = lI({ inputs: { x: n, filter: s }, backend: t8, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } });
if (a) {
let g = h;
h = Aa({ inputs: { a: h, b: a }, backend: t8 }), t8.disposeIntermediateTensorInfo(g);
}
if (d) {
let g = h;
h = gp(t8, h, d, i, f), t8.disposeIntermediateTensorInfo(g);
}
return h;
}
var F$ = { kernelName: vo, backendName: "cpu", kernelFunc: e7 };
function t7(r) {
let { inputs: e, backend: t8 } = r, { params: o, indices: n } = e, s = y.sizeFromShape(o.shape), a = n.shape, i = a[a.length - 1], [p, u, c, l] = w.prepareAndValidate(o, n);
if (u === 0)
return t8.makeTensorInfo(p, o.dtype, []);
let m = t8.data.get(n.dataId).values, d = t8.bufferSync(o), f = kf(m, d, o.dtype, u, i, c, l, o.shape, s);
return t8.makeTensorInfo(p, o.dtype, f.values);
}
var P$ = { kernelName: vn, backendName: "cpu", kernelFunc: t7 };
function r7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o;
Q([n, s], "gatherV2");
let p = y.parseAxisParam(a, n.shape)[0], u = t8.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 = w.segment_util.collectGatherOpShapeInfo(n, s, p, l), f = Ve({ inputs: { x: n }, backend: t8, attrs: { shape: [d.batchSize, d.outerSize, d.dimSize, d.sliceSize] } }), h = Ve({ inputs: { x: s }, backend: t8, attrs: { shape: [d.batchSize, m / d.batchSize] } }), g = [d.batchSize, d.outerSize, m / d.batchSize, d.sliceSize], x = t8.bufferSync(h), b = t8.bufferSync(f), C = Nf(b, x, g);
return t8.disposeIntermediateTensorInfo(f), t8.disposeIntermediateTensorInfo(h), t8.makeTensorInfo(d.outputShape, C.dtype, C.values);
}
var O$ = { kernelName: sa, backendName: "cpu", kernelFunc: r7 };
function o7(r) {
let { inputs: e, backend: t8 } = 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: t8, attrs: { shape: [a, s] } }), p = Bf(i, true, t8), u = Ve({ inputs: { x: p }, backend: t8, attrs: { shape: o.shape } });
return t8.disposeIntermediateTensorInfo(i), t8.disposeIntermediateTensorInfo(p), u;
}
var M$ = { kernelName: zi, backendName: "cpu", kernelFunc: o7 };
var n7 = Ie(Tn, (r) => Number.isFinite(r) ? 1 : 0, "bool");
var L$ = { kernelName: Tn, backendName: "cpu", kernelFunc: n7 };
var s7 = Ie(_n, (r) => Math.abs(r) === 1 / 0 ? 1 : 0, "bool");
var B$ = { kernelName: _n, backendName: "cpu", kernelFunc: s7 };
var a7 = Ie($n, (r) => Number.isNaN(r) ? 1 : 0, "bool");
var z$ = { kernelName: $n, backendName: "cpu", kernelFunc: a7 };
function i7(r) {
let { backend: e, attrs: t8 } = r, { start: o, stop: n, num: s } = t8, a = Tf(o, n, s);
return e.makeTensorInfo([a.length], "float32", a);
}
var V$ = { kernelName: An, backendName: "cpu", kernelFunc: i7 };
var u7 = Ie(Pn, (r) => Math.log1p(r));
var W$ = { kernelName: Pn, backendName: "cpu", kernelFunc: u7 };
var p7 = ze((r, e) => r && e);
var c7 = Ye(On, p7, null, "bool");
var U$ = { kernelName: On, backendName: "cpu", kernelFunc: c7 };
var l7 = Ie(Mn, (r) => r ? 0 : 1, "bool");
var G$ = { kernelName: Mn, backendName: "cpu", kernelFunc: l7 };
var m7 = ze((r, e) => r || e);
var d7 = Ye(Ln, m7, null, "bool");
var H$ = { kernelName: Ln, backendName: "cpu", kernelFunc: d7 };
function f7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o;
Q(n, "LRN");
let u = n.shape[3], c = u - 1, l = t8.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), C = 0;
for (; x <= b; x++) {
let S = l[x];
C += S * S;
}
return C;
}
for (let h = 0; h < m; h++) {
let g = f(h), x = l[h] * Math.pow(a + i * g, -p);
d[h] = x;
}
return t8.makeTensorInfo(n.shape, n.dtype, d);
}
var K$ = { kernelName: Bn, backendName: "cpu", kernelFunc: f7 };
function h7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o;
Q(a, "LRNGrad");
let l = y.sizeFromShape(a.shape), m = a.shape[3], d = t8.data.get(a.dataId).values, f = t8.data.get(n.dataId).values, h = t8.data.get(s.dataId).values, g = new Float32Array(l), x = l;
for (let b = 0; b < x; b++) {
let C = b % m, S = b - C + Math.max(0, C - i), k = b - C + Math.min(m, C + 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 t8.makeTensorInfo(a.shape, n.dtype, g);
}
var q$ = { kernelName: ja, backendName: "cpu", kernelFunc: h7 };
function dI(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = t8, p = n.shape, u = p.length, c = y.parseAxisParam(s, p), l = c, m = w.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 = Cc(d, p, n.dtype, m, S), l = w.getInnerMostAxes(l.length, u), p = S;
}
Q(n, "max"), w.assertAxesAreInnerMostDims("max", l, u);
let [f, h] = w.computeOutAndReduceShapes(p, l), g = y.sizeFromShape(h), x = _f(d, g, f, n.dtype), b = i.write(x, f, n.dtype), C = f;
return a && (C = w.expandShapeToKeepDim(f, c)), { dataId: b, shape: C, dtype: n.dtype };
}
var j$ = { kernelName: zn, backendName: "cpu", kernelFunc: dI };
function g7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e;
Q(n, "maxPool");
let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
let c = w.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: t8 });
else {
let m = t8.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = vc(m, n.shape, n.dtype, d, c, "max");
l = t8.makeTensorInfo(c.outShape, n.dtype, f.values);
}
return l;
}
var X$ = { kernelName: Wn, backendName: "cpu", kernelFunc: g7 };
function x7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o;
Q(n, "maxPool3d");
let c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t8.data.get(n.dataId).values, m = Lf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "max");
return t8.makeTensorInfo(m.shape, "float32", m.values);
}
var Y$ = { kernelName: aa, backendName: "cpu", kernelFunc: x7 };
function y7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o;
Q([n, s], "maxPool3DGrad");
let c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = t8.bufferSync(s), m = H_(l, c), d = c.strideDepth, f = c.strideHeight, h = c.strideWidth, g = c.dilationDepth, x = c.dilationHeight, b = c.dilationWidth, C = c.effectiveFilterDepth, S = c.effectiveFilterHeight, k = c.effectiveFilterWidth, _ = C - 1 - c.padInfo.front, E = k - 1 - c.padInfo.left, R = S - 1 - c.padInfo.top, D = me(s.shape, "float32"), P = t8.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, q = z - E, Y = 0;
for (let J = 0; J < C; 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 = (q + oe) / h;
if (ie < 0 || ie >= c.outWidth || Math.floor(ie) !== ie)
continue;
let le = C * S * k - 1 - m.get(O, re, ee, ie, M), be = J * S * k + ne * k + oe, _e = le === be ? 1 : 0;
if (_e === 0)
continue;
let ve = P.get(O, re, ee, ie, M);
Y += ve * _e;
}
}
}
D.set(Y, O, L, B, z, M);
}
return t8.makeTensorInfo(D.shape, D.dtype, D.values);
}
var Q$ = { kernelName: Ui, backendName: "cpu", kernelFunc: y7 };
function b7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s;
Q([s, a], "maxPoolGrad");
let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = t8.data.get(i.dataId).values, f = me(m.outShape, i.dtype, Mf(d, i.shape, i.dtype, m).values), h = m.strideHeight, g = m.strideWidth, x = m.dilationHeight, b = m.dilationWidth, C = m.effectiveFilterHeight, S = m.effectiveFilterWidth, k = S - 1 - m.padInfo.left, _ = C - 1 - m.padInfo.top, E = me(i.shape, "float32"), R = t8.data.get(n.dataId).values, D = me(n.shape, "float32", R);
for (let P = 0; P < m.batchSize; ++P)
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 < C; j += x) {
let q = (B + j) / h;
if (!(q < 0 || q >= m.outHeight || Math.floor(q) !== q))
for (let Y = 0; Y < S; Y += b) {
let J = (z + Y) / g;
if (J < 0 || J >= m.outWidth || Math.floor(J) !== J)
continue;
let re = C * S - 1 - f.get(P, q, J, O), ne = j * S + Y, ee = re === ne ? 1 : 0;
if (ee === 0)
continue;
let oe = D.get(P, q, J, O);
U += oe * ee;
}
}
E.set(U, P, M, L, O);
}
return t8.makeTensorInfo(E.shape, E.dtype, E.values);
}
var Z$ = { kernelName: Wi, backendName: "cpu", kernelFunc: b7 };
function J$(r, e, t8, o, n) {
let s = y.computeStrides(e), a = vc(r, e, t8, s, n, "max"), i = Mf(r, e, t8, n, true, o);
return [a.values, i.values];
}
var eE = { kernelName: ia, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => {
let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t8;
Q(o, "MaxPoolWithArgmax");
let u = p.data.get(o.dataId).values, c = w.computePool2DInfo(o.shape, n, s, [1, 1], a), [l, m] = J$(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 C7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = y.parseAxisParam(s, n.shape), u = w.computeOutAndReduceShapes(n.shape, i)[1], c = y.sizeFromShape(u), l = [], m = t8.makeTensorInfo([], "float32", new Float32Array([c]));
l.push(m);
let d = Ro({ inputs: { x: n }, backend: t8, attrs: { dtype: "float32" } });
l.push(d);
let f = zl({ inputs: { a: d, b: m }, backend: t8 });
l.push(f);
let h = fi({ inputs: { x: f }, backend: t8, attrs: { axis: s, keepDims: a } });
return l.forEach((g) => t8.disposeIntermediateTensorInfo(g)), h;
}
var tE = { kernelName: Un, backendName: "cpu", kernelFunc: C7 };
function w7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
Q(n, "min");
let i = y.parseAxisParam(s, n.shape), p = i, u = w.getAxesPermutation(p, n.shape.length), c = n;
u != null && (c = It({ inputs: { x: n }, backend: t8, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("min", p, c.shape.length);
let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t8.data.get(c.dataId).values;
for (let x = 0; x < f.length; ++x) {
let b = x * d, C = h[b];
for (let S = 0; S < d; ++S) {
let k = h[b + S];
(Number.isNaN(k) || k < C) && (C = k);
}
f[x] = C;
}
u != null && t8.disposeIntermediateTensorInfo(c);
let g = t8.makeTensorInfo(l, c.dtype, f);
if (a) {
let x = w.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t8, attrs: { shape: x } });
return t8.disposeIntermediateTensorInfo(g), b;
}
return g;
}
var rE = { kernelName: Gn, backendName: "cpu", kernelFunc: w7 };
function S7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { paddings: s, mode: a } = o;
Q(n, "mirrorPad");
let i = s.map((C, S) => C[0] + n.shape[S] + C[1]), p = s.map((C) => C[0]), u = s.map((C, S) => C[0] + n.shape[S]), c = a === "reflect" ? 0 : 1, l = t8.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 C = 0; C < f; C++) {
let S = y.indexToLoc(C, 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[C] = l[k];
}
return { dataId: t8.write(x, i, n.dtype), shape: i, dtype: n.dtype };
}
var oE = { kernelName: Kn, backendName: "cpu", kernelFunc: S7 };
var I7 = ze((r, e) => {
let t8 = r % e;
return r < 0 && e < 0 || r >= 0 && e >= 0 ? t8 : (t8 + e) % e;
});
var v7 = Ye(qn, I7);
var nE = { kernelName: qn, backendName: "cpu", kernelFunc: v7 };
var aE = Up(zw());
function fI(r) {
let { inputs: e, backend: t8, 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 = dI({ inputs: { x: n }, backend: t8, attrs: { reductionIndices: p, keepDims: false } }), c = w.expandShapeToKeepDim(u.shape, p), l = Ve({ inputs: { x: u }, backend: t8, attrs: { shape: c } }), m = Ll({ inputs: { a: n, b: l }, backend: t8 }), d = LS({ inputs: { x: m }, backend: t8 }), f = fi({ inputs: { x: d }, backend: t8, attrs: { axis: p, keepDims: false } }), h = Ve({ inputs: { x: f }, backend: t8, attrs: { shape: c } }), g = zl({ inputs: { a: d, b: h }, backend: t8 });
return t8.disposeIntermediateTensorInfo(u), t8.disposeIntermediateTensorInfo(l), t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), t8.disposeIntermediateTensorInfo(h), g;
}
var sE = { kernelName: Is, backendName: "cpu", kernelFunc: fI };
function k7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o;
Q(n, "multinomial");
let p = i ? n : fI({ inputs: { logits: n }, backend: t8, attrs: { dim: -1 } }), u = p.shape[0], c = p.shape[1], l = t8.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 C = 1; C < g.length; ++C)
g[C] = g[C - 1] + l[h + C];
let x = aE.alea(a.toString()), b = f * s;
for (let C = 0; C < s; ++C) {
let S = x();
d[b + C] = g.length;
for (let k = 0; k < g.length; k++)
if (S < g[k]) {
d[b + C] = k;
break;
}
}
}
return i || t8.disposeIntermediateTensorInfo(p), t8.makeTensorInfo(m, "int32", d);
}
var iE = { kernelName: jn, backendName: "cpu", kernelFunc: k7 };
var N7 = Wt.nonMaxSuppressionV3Impl;
function T7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o;
Q(n, "NonMaxSuppression");
let u = t8.data.get(n.dataId).values, c = t8.data.get(s.dataId).values, { selectedIndices: l } = N7(u, c, a, i, p);
return t8.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var uE = { kernelName: Qn, backendName: "cpu", kernelFunc: T7 };
var _7 = Wt.nonMaxSuppressionV4Impl;
function $7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o;
Q(n, "NonMaxSuppressionPadded");
let c = t8.data.get(n.dataId).values, l = t8.data.get(s.dataId).values, { selectedIndices: m, validOutputs: d } = _7(c, l, a, i, p, u);
return [t8.makeTensorInfo([m.length], "int32", new Int32Array(m)), t8.makeTensorInfo([], "int32", new Int32Array([d]))];
}
var pE = { kernelName: Xa, backendName: "cpu", kernelFunc: $7 };
var E7 = Wt.nonMaxSuppressionV5Impl;
function R7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o;
Q(n, "NonMaxSuppressionWithScore");
let c = t8.data.get(n.dataId).values, l = t8.data.get(s.dataId).values, m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = E7(c, l, m, d, f, h);
return [t8.makeTensorInfo([g.length], "int32", new Int32Array(g)), t8.makeTensorInfo([x.length], "float32", new Float32Array(x))];
}
var cE = { kernelName: Zn, backendName: "cpu", kernelFunc: R7 };
function D7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o;
Q(n, "oneHot");
let u = y.sizeFromShape(n.shape), c = new Float32Array(u * a);
c.fill(p);
let l = t8.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 t8.makeTensorInfo([...n.shape, a], s, c);
}
var lE = { kernelName: Jn, backendName: "cpu", kernelFunc: D7 };
function Ul(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
if (o.dtype === "string")
throw new Error("zerosLike is not supported for string tensors");
if (o.dtype === "complex64") {
let n = Eo({ inputs: { input: o }, backend: t8 }), s = Ul({ inputs: { x: n }, backend: t8 }), a = Fa({ inputs: { input: o }, backend: t8 }), i = Ul({ inputs: { x: a }, backend: t8 }), p = Kt({ inputs: { real: s, imag: i }, backend: t8 });
return t8.disposeIntermediateTensorInfo(n), t8.disposeIntermediateTensorInfo(s), t8.disposeIntermediateTensorInfo(a), t8.disposeIntermediateTensorInfo(i), p;
} else
return Wl({ backend: t8, attrs: { shape: o.shape, value: 0, dtype: o.dtype } });
}
var mE = { kernelName: ba, backendName: "cpu", kernelFunc: Ul };
function dE(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
if (o.dtype === "string")
throw new Error("onesLike is not supported for string tensors");
if (o.dtype === "complex64") {
let n = Eo({ inputs: { input: o }, backend: t8 }), s = dE({ inputs: { x: n }, backend: t8 }), a = Fa({ inputs: { input: o }, backend: t8 }), i = Ul({ inputs: { x: a }, backend: t8 }), p = Kt({ inputs: { real: s, imag: i }, backend: t8 });
return t8.disposeIntermediateTensorInfo(n), t8.disposeIntermediateTensorInfo(s), t8.disposeIntermediateTensorInfo(a), t8.disposeIntermediateTensorInfo(i), p;
} else
return Wl({ backend: t8, attrs: { shape: o.shape, value: 1, dtype: o.dtype } });
}
var fE = { kernelName: pa, backendName: "cpu", kernelFunc: dE };
function hI(r) {
let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o;
if (e.length === 1)
return kc({ inputs: { input: e[0] }, backend: t8, 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 = kc({ inputs: { input: c }, backend: t8, attrs: { dim: n } });
return i.push(l), l;
}), u = gu({ inputs: p, backend: t8, attrs: { axis: n } });
return i.forEach((c) => t8.disposeIntermediateTensorInfo(c)), u;
}
var hE = { kernelName: ca, backendName: "cpu", kernelFunc: hI };
function A7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o;
Q(n, "pad");
let i = s.map((b, C) => b[0] + n.shape[C] + b[1]), p = s.map((b) => b[0]), u = t8.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: t8.write(g, i, n.dtype), shape: i, dtype: n.dtype };
}
var zf = { kernelName: es, backendName: "cpu", kernelFunc: A7 };
var F7 = ze((r, e) => Math.pow(r, e));
var P7 = Ye(ts, F7);
var gE = { kernelName: ts, backendName: "cpu", kernelFunc: P7 };
function O7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t8.data.get(x.dataId).values), u = n.map((x) => x.shape), c = t8.data.get(s.dataId).values, l = t8.data.get(a.dataId).values, [m, d, f] = $f(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t8.makeTensorInfo([x.length], "int32", x)), g = t8.makeTensorInfo(f, s.dtype, d);
return h.concat([g]);
}
var xE = { kernelName: jp, backendName: "cpu", kernelFunc: O7 };
function M7(r) {
let { inputs: e, backend: t8 } = r, { starts: o, limits: n, deltas: s } = e, a = t8.data.get(o.dataId).values, i = t8.data.get(n.dataId).values, p = t8.data.get(s.dataId).values, [u, c] = Ef(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t8.makeTensorInfo([u.length], "int32", u), m = t8.makeTensorInfo([c.length], o.dtype, c);
return [l, m];
}
var yE = { kernelName: Xp, backendName: "cpu", kernelFunc: M7 };
function L7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t8.data.get(n.dataId).values, c = t8.data.get(s.dataId).values, l = t8.data.get(a.dataId).values, m = i.map((g) => t8.data.get(g.dataId).values), d = i.map((g) => g.shape), [f, h] = Rf(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p);
return t8.makeTensorInfo(f, s.dtype, h);
}
var bE = { kernelName: Yp, backendName: "cpu", kernelFunc: L7 };
function B7(r) {
let { backend: e, attrs: t8 } = r, { start: o, stop: n, dtype: s, step: a } = t8, i = cp(o, n, a, s);
return e.makeTensorInfo([i.length], s, i);
}
var CE = { kernelName: la, backendName: "cpu", kernelFunc: B7 };
var z7 = Ie(ns, (r) => 1 / r);
var wE = { kernelName: ns, backendName: "cpu", kernelFunc: z7 };
function V7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o;
Q(n, "resizeBilinear");
let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t8.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], C = 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)), P = 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, q = Math.min(d - 1, Math.ceil(z)), Y = M + U * p[2], J = L + U * p[2], re = M + q * p[2], ne = L + q * p[2];
for (let ee = 0; ee < f; ee++) {
let oe = h[Y + ee], ie = h[J + ee], le = h[re + ee], be = h[ne + ee], _e = oe + (le - oe) * j, ve = ie + (be - ie) * j, Fe = _e + (ve - _e) * P;
g[C++] = Fe;
}
}
}
return t8.makeTensorInfo([l, u, c, f], "float32", g);
}
var SE = { kernelName: is, backendName: "cpu", kernelFunc: V7 };
function W7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o;
Q([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], C = t8.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), P = Math.min(Math.ceil(R), u - 1), O = _ + D * i[1], M = _ + P * i[1], L = R - D, B = 1 - L;
for (let z = 0; z < d; z++) {
let U = z * b, j = Math.floor(U), q = Math.min(Math.ceil(U), c - 1), Y = U - j, J = 1 - Y, re = O + j * i[2], ne = O + q * i[2], ee = M + j * i[2], oe = M + q * i[2], ie = B * J, le = B * Y, be = L * J, _e = L * Y;
for (let ve = 0; ve < l; ve++) {
let Fe = C[S++];
f[re + ve] += Fe * ie, f[ne + ve] += Fe * le, f[ee + ve] += Fe * be, f[oe + ve] += Fe * _e;
}
}
}
}
return t8.makeTensorInfo([p, c, u, l], "float32", f);
}
var IE = { kernelName: Qa, backendName: "cpu", kernelFunc: W7 };
function U7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o;
Q(n, "resizeNearestNeighbor");
let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t8.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], C = 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 ? C * (R + 0.5) : C * R, P = Math.min(m - 1, s ? Math.round(D) : Math.floor(D));
a && (P = Math.max(0, P));
let O = E + P * 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 t8.makeTensorInfo([l, u, c, f], n.dtype, g);
}
var vE = { kernelName: as, backendName: "cpu", kernelFunc: U7 };
function G7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o;
Q([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 = t8.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], C = x[0] / b[0], S = x[1] / b[1], k = 1 / C, _ = 1 / S, E = Math.ceil(k) * 2 + 2, R = Math.ceil(_) * 2 + 2;
for (let D = 0; D < u; D++) {
let P = D * i[0];
for (let O = 0; O < c; O++) {
let M = P + 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 * _), q = Math.floor(j - R / 2);
for (let Y = 0; Y < m; Y++) {
let J = 0;
for (let re = 0; re < E; re++) {
let ne = re + B;
if (ne < 0 || ne >= d)
continue;
let ee = P + ne * p[1], oe = ne * C, ie = Math.min(c - 1, a ? Math.round(oe) : Math.floor(oe));
if (O === ie)
for (let le = 0; le < R; le++) {
let be = le + q;
if (be < 0 || be >= f)
continue;
let _e = ee + be * p[2], ve = be * S, Fe = Math.min(l - 1, a ? Math.round(ve) : Math.floor(ve));
z === Fe && (J += g[_e + Y]);
}
}
h[U + Y] = J;
}
}
}
}
return t8.makeTensorInfo(n.shape, n.dtype, h);
}
var kE = { kernelName: Ya, backendName: "cpu", kernelFunc: G7 };
function H7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dims: s } = o;
Q(n, "reverse");
let a = n.shape.length, i = y.parseAxisParam(s, n.shape);
if (a === 0)
return lr({ inputs: { x: n }, backend: t8 });
let p = new tt(n.shape, n.dtype), u = t8.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 t8.makeTensorInfo(p.shape, p.dtype, p.values);
}
var NE = { kernelName: ps, backendName: "cpu", kernelFunc: H7 };
var TE = { kernelName: Ds, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => {
let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t8, p = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [u, c, l, m] = o.shape, [d, f] = w.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 P = 0; P < m; P++) {
let O = [u, _, R, P], 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" && (P === 3 ? U = h : U = s[P]), B >= 0 && B < l && z >= 0 && z < c) {
let q = z * (l * m), Y = B * m, J = k + q + Y + P;
U = b[J];
}
let j = k + E + D + P;
p[j] = U;
}
}
}
}
return { dataId: i.write(p, o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
var K7 = Ie(cs, (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 _E = { kernelName: cs, backendName: "cpu", kernelFunc: K7 };
function q7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = true, d = t8.bufferSync(n), f = t8.bufferSync(s), h = Ls(d, f, a, l, u, p, i, c, 0, m);
return t8.makeTensorInfo(a, h.dtype, h.values);
}
var $E = { kernelName: ms, backendName: "cpu", kernelFunc: q7 };
function j7(r, e) {
let t8 = 0, o = r.length, n = 0;
for (; t8 < o; )
n = Math.floor((t8 + o) / 2), r[n] < e ? t8 = n + 1 : o = n;
return o;
}
function X7(r, e) {
let t8 = 0, o = r.length, n = 0;
for (; t8 < o; )
n = Math.floor((t8 + o) / 2), r[n] <= e ? t8 = n + 1 : o = n;
return o;
}
function EE(r, e, t8, o, n, s) {
let a = y.getArrayFromDType("int32", t8 * n);
for (let i = 0; i < t8; ++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" ? j7(p, e[c + u]) : X7(p, e[c + u]);
}
return a;
}
function Y7(r) {
let { inputs: e, backend: t8, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = t8.data.get(n.dataId).values, p = t8.data.get(s.dataId).values, u = EE(i, p, n.shape[0], n.shape[1], s.shape[1], a);
return t8.makeTensorInfo(s.shape, "int32", u);
}
var RE = { kernelName: fs, backendName: "cpu", kernelFunc: Y7 };
function Q7(r) {
let { inputs: e, backend: t8 } = r, { condition: o, t: n, e: s } = e;
Q([o, n, s], "select");
let a = o.shape.length, i = t8.data.get(o.dataId).values, p = t8.data.get(n.dataId).values, u = t8.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 t8.makeTensorInfo(n.shape, c, l);
}
var DE = { kernelName: da, backendName: "cpu", kernelFunc: Q7 };
var Z7 = w.SELU_SCALEALPHA;
var J7 = w.SELU_SCALE;
var eQ = Ie(hs, (r) => r >= 0 ? J7 * r : Z7 * (Math.exp(r) - 1));
var AE = { kernelName: hs, backendName: "cpu", kernelFunc: eQ };
var tQ = Ie(ys, (r) => r < 0 ? -1 : r > 0 ? 1 : 0);
var FE = { kernelName: ys, backendName: "cpu", kernelFunc: tQ };
var rQ = Ie(gs, (r) => Math.sin(r));
var PE = { kernelName: gs, backendName: "cpu", kernelFunc: rQ };
var oQ = Ie(xs, (r) => Math.sinh(r));
var OE = { kernelName: xs, backendName: "cpu", kernelFunc: oQ };
var nQ = 11920928955078125e-23;
var ME = Math.log(nQ) + 2;
var sQ = Ie(Cs, (r) => {
let e = r > -ME, t8 = r < ME, o = Math.exp(r), n;
return t8 ? n = o : e ? n = r : n = Math.log(1 + o), n;
});
var LE = { kernelName: Cs, backendName: "cpu", kernelFunc: sQ };
function aQ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o;
Q([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 = zf.kernelFunc({ inputs: { x: n }, backend: t8, attrs: { paddings: p, constantValue: 0 } }), c = w.getReshaped(u.shape, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u.shape, s, i, false), h = Ve({ inputs: { x: u }, backend: t8, attrs: { shape: c } }), b = It({ inputs: { x: h }, backend: t8, attrs: { perm: l } }), k = Ve({ inputs: { x: b }, backend: t8, attrs: { shape: m } });
return t8.disposeIntermediateTensorInfo(u), t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(b), k;
}
var BE = { kernelName: ha, backendName: "cpu", kernelFunc: aQ };
function iQ(r) {
let { inputs: e, backend: t8 } = 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 = t8.data.get(o.dataId).values, p = t8.data.get(n.dataId).values, u = t8.data.get(s.dataId).values, c = t8.data.get(a.dataId).values[0], [l, m, d, f, h] = Df(i, o.shape, o.dtype, p, n.dtype, u, c);
return [t8.makeTensorInfo(m, o.dtype, l), t8.makeTensorInfo([m[0]], n.dtype, d), t8.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t8.makeTensorInfo([h.length], o.dtype, new Int32Array(h))];
}
var zE = { kernelName: Hi, backendName: "cpu", kernelFunc: iQ };
function uQ(r) {
let { inputs: e, backend: t8 } = 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(t8.data.get(n.dataId).values), i = t8.data.get(o.dataId).values, p = Array.from(t8.data.get(s.dataId).values), [u, c, l] = Af(i, o.shape, o.dtype, a, p);
return [t8.makeTensorInfo(c, o.dtype, u), t8.makeTensorInfo([l.length], s.dtype, new Int32Array(l))];
}
var VE = { kernelName: Za, backendName: "cpu", kernelFunc: uQ };
function pQ(r) {
let { inputs: e, backend: t8 } = 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 = t8.data.get(o.dataId).values, i = t8.data.get(n.dataId).values, p = t8.data.get(s.dataId).values, [u, c] = Sc(a, o.shape, o.dtype, i, p, true);
return t8.makeTensorInfo(c, o.dtype, u);
}
var WE = { kernelName: Ki, backendName: "cpu", kernelFunc: pQ };
function cQ(r) {
let { inputs: e, backend: t8 } = 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 = t8.data.get(o.dataId).values, i = t8.data.get(n.dataId).values, p = t8.data.get(s.dataId).values, [u, c] = Sc(a, o.shape, o.dtype, i, p);
return t8.makeTensorInfo(c, o.dtype, u);
}
var UE = { kernelName: qi, backendName: "cpu", kernelFunc: cQ };
function lQ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false, f = t8.bufferSync(n), h;
switch (s.dtype) {
case "bool": {
let g = t8.bufferSync(s), x = !!t8.data.get(a.dataId).values[0];
h = Ls(f, g, i, m, c, u, p, l, x, d);
break;
}
case "float32": {
let g = t8.bufferSync(s), x = t8.data.get(a.dataId).values[0];
h = Ls(f, g, i, m, c, u, p, l, x, d);
break;
}
case "int32": {
let g = t8.bufferSync(s), x = t8.data.get(a.dataId).values[0];
h = Ls(f, g, i, m, c, u, p, l, x, d);
break;
}
case "string": {
let g = t8.bufferSync(s), x = y.decodeString(t8.data.get(a.dataId).values[0]);
h = Ls(f, g, i, m, c, u, p, l, x, d);
break;
}
default:
throw new Error(`Unsupported type ${s.dtype}`);
}
return t8.makeTensorInfo(i, h.dtype, h.values);
}
var GE = { kernelName: vs, backendName: "cpu", kernelFunc: lQ };
function mQ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.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 }, backend: t8, attrs: { begin: u, size: m } });
return u[i] += l, d;
});
}
var HE = { kernelName: ga, backendName: "cpu", kernelFunc: mQ };
var KE = { kernelName: ji, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e }) => {
let { x: t8 } = r, o = e;
Q(t8, "square");
let n = o.data.get(t8.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, t8.shape, t8.dtype), shape: t8.shape, dtype: t8.dtype };
} };
var dQ = Ie(wo, (r, e) => {
let t8 = e;
return isNaN(r) ? NaN : r > 0 ? 1 : t8.alpha;
});
var qE = { kernelName: wo, backendName: "cpu", kernelFunc: dQ };
function fQ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o;
Q(n, "stridedSlice");
let { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
if (h)
k = Ve({ inputs: { x: n }, backend: t8, 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, C, S), E = Ao({ inputs: { x: n }, backend: t8, attrs: { begin: b, size: _ } });
k = Ve({ inputs: { x: E }, backend: t8, attrs: { shape: f } }), t8.disposeIntermediateTensorInfo(E);
} else {
let _ = t8.bufferSync(n), E = Ff(d, _, S, b);
k = t8.makeTensorInfo(f, E.dtype, E.values);
}
return k;
}
var jE = { kernelName: Ns, backendName: "cpu", kernelFunc: fQ };
function hQ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t8.data.get(c.dataId).values, d = t8.data.get(l.dataId).values, [f, h] = mp(m, d, n, s, a, i, p, u);
return [t8.makeTensorInfo([f.length], "string", f), t8.makeTensorInfo(l.shape, "int32", h)];
}
var XE = { kernelName: xa, backendName: "cpu", kernelFunc: hQ };
function gQ(r) {
let { inputs: e, backend: t8, 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 = t8.data.get(s.dataId).values, p = t8.data.get(a.dataId).values[0], [u, c, l] = dp(i, p, n), m = c.length;
return [t8.makeTensorInfo([m, 2], "int32", u), t8.makeTensorInfo([m], "string", c), t8.makeTensorInfo([2], "int32", new Int32Array(l))];
}
var YE = { kernelName: Xi, backendName: "cpu", kernelFunc: gQ };
function xQ(r) {
let { inputs: e, backend: t8, 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 = t8.data.get(s.dataId).values, i = fp(a, n);
return t8.makeTensorInfo(s.shape, "int32", i);
}
var QE = { kernelName: Yi, backendName: "cpu", kernelFunc: xQ };
var yQ = Ie(_s, (r) => Math.tan(r));
var ZE = { kernelName: _s, backendName: "cpu", kernelFunc: yQ };
var bQ = Ie($s, (r) => Math.tanh(r));
var JE = { kernelName: $s, backendName: "cpu", kernelFunc: bQ };
function CQ(r) {
let { inputs: e, backend: t8 } = r, { tensor: o, indices: n, updates: s } = e, { sliceRank: a, numUpdates: i, sliceSize: p, strides: u, outputSize: c } = w.calculateShapes(s, n, o.shape), l = false, m = t8.bufferSync(n), d = t8.bufferSync(s), f = t8.bufferSync(o), h = Ls(m, d, o.shape, c, p, i, a, u, f, l);
return t8.makeTensorInfo(o.shape, h.dtype, h.values);
}
var eR = { kernelName: ds, backendName: "cpu", kernelFunc: CQ };
function wQ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reps: s } = o;
Q(n, "tile");
let a = Pf(t8.bufferSync(n), s);
return t8.makeTensorInfo(a.shape, a.dtype, a.values);
}
var tR = { kernelName: uo, backendName: "cpu", kernelFunc: wQ };
function SQ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o;
Q(n, "topk");
let i = t8.data.get(n.dataId).values, [p, u] = Of(i, n.shape, n.dtype, s, a);
return [t8.makeTensorInfo(p.shape, p.dtype, p.values), t8.makeTensorInfo(u.shape, u.dtype, u.values)];
}
var rR = { kernelName: Es, backendName: "cpu", kernelFunc: SQ };
function IQ(r) {
let { inputs: e, attrs: t8, backend: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = t8, [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], C = 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 P = 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 q, Y = B[6] * U + B[7] * z + 1;
if (Y === 0)
continue;
let J = (B[0] * U + B[1] * z + B[2]) / Y, re = (B[3] * U + B[4] * z + B[5]) / Y, ne = oR(J, m, i), ee = oR(re, l, i);
switch (a) {
case "nearest":
q = _Q(P, l, m, b, C, S, L, ee, ne, j, p);
break;
case "bilinear":
q = $Q(P, l, m, b, C, 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] = q;
}
return o.makeTensorInfo(g, n.dtype, D);
}
return { dataId: o.write(D, g, n.dtype), shape: n.shape, dtype: n.dtype };
}
var nR = { kernelName: Rs, backendName: "cpu", kernelFunc: IQ };
function oR(r, e, t8) {
switch (t8) {
case "reflect":
return vQ(r, e);
case "wrap":
return kQ(r, e);
case "nearest":
return TQ(r, e);
case "constant":
default:
return NQ(r, e);
}
}
function vQ(r, e) {
let t8 = r;
if (t8 < 0)
if (e <= 1)
t8 = 0;
else {
let o = 2 * e;
t8 < o && (t8 = o * Math.trunc(-t8 / o) + t8), t8 = t8 < -e ? t8 + o : -t8 - 1;
}
else if (t8 > e - 1)
if (e <= 1)
t8 = 0;
else {
let o = 2 * e;
t8 -= o * Math.trunc(t8 / o), t8 >= e && (t8 = o - t8 - 1);
}
return y.clamp(0, t8, e - 1);
}
function kQ(r, e) {
let t8 = r;
if (t8 < 0)
if (e <= 1)
t8 = 0;
else {
let o = e - 1;
t8 += e * (Math.trunc(-t8 / o) + 1);
}
else if (t8 > e - 1)
if (e <= 1)
t8 = 0;
else {
let o = e - 1;
t8 -= e * Math.trunc(t8 / o);
}
return y.clamp(0, t8, e - 1);
}
function NQ(r, e) {
return r;
}
function TQ(r, e) {
return y.clamp(0, r, e - 1);
}
function Gl(r, e, t8, 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 < t8 ? r[l] : c;
}
function _Q(r, e, t8, o, n, s, a, i, p, u, c) {
let l = Math.round(i), m = Math.round(p);
return Gl(r, e, t8, o, n, s, a, l, m, u, c);
}
function $Q(r, e, t8, 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, t8, o, n, s, a, l, m, u, c) + (p - m) * Gl(r, e, t8, o, n, s, a, l, f, u, c), g = (f - p) * Gl(r, e, t8, o, n, s, a, d, m, u, c) + (p - m) * Gl(r, e, t8, o, n, s, a, d, f, u, c);
return (d - i) * h + (i - l) * g;
}
function EQ(r) {
let { inputs: e, attrs: t8, backend: o } = r, { axis: n } = t8, { x: s } = e;
Q(s, "unique");
let a = o.data.get(s.dataId).values, { outputValues: i, outputShape: p, indices: u } = hp(a, n, s.shape, s.dtype);
return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)];
}
var sR = { kernelName: Qi, backendName: "cpu", kernelFunc: EQ };
function RQ(r) {
let { inputs: e, backend: t8, 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 = Ao({ inputs: { x: n }, backend: t8, attrs: { begin: c, size: l } });
m[d] = Ve({ inputs: { x: f }, backend: t8, attrs: { shape: p } }), t8.disposeIntermediateTensorInfo(f);
}
return m;
}
var aR = { kernelName: ya, backendName: "cpu", kernelFunc: RQ };
function DQ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o;
Q(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 = kc({ inputs: { input: m }, backend: t8, attrs: { dim: f + 1 } });
m = h, c.push(h);
}
for (let f = 0; f < a; ++f) {
let h = y.createScalarValue(f, "int32"), g = t8.makeTensorInfo([], "int32", h), x = OS({ inputs: { a: g, b: m }, backend: t8 }), b = Ro({ inputs: { x }, backend: t8, attrs: { dtype: "float32" } }), C = pp({ inputs: { a: b, b: n }, backend: t8 }), S = fi({ inputs: { x: C }, backend: t8, attrs: { axis: 0, keepDims: false } });
u.push(S), c.push(g), c.push(x), c.push(b), c.push(C), c.push(S);
}
let d = hI({ inputs: u, backend: t8, attrs: { axis: 0 } });
return c.forEach((f) => t8.disposeIntermediateTensorInfo(f)), d;
}
var iR = { kernelName: Zi, backendName: "cpu", kernelFunc: DQ };
var AQ = [D_, zT, A_, F_, HT, P_, O_, M_, L_, B_, z_, V_, W_, U_, G_, K_, q_, j_, X_, R_, Y_, Q_, Z_, KT, J_, GT, qT, e$, VT, t$, o$, n$, s$, a$, i$, u$, p$, c$, l$, m$, d$, f$, h$, g$, x$, y$, b$, C$, w$, S$, I$, v$, N$, k_, T$, jT, _$, XT, $$, YT, E$, R$, D$, QT, ZT, A$, F$, P$, O$, JT, e_, WT, M$, r$, L$, B$, z$, N_, t_, r_, V$, o_, W$, U$, G$, H$, K$, q$, j$, n_, X$, Y$, Q$, Z$, eE, tE, rE, s_, oE, nE, iE, a_, i_, uE, pE, cE, u_, lE, fE, hE, zf, gE, T_, c_, xE, yE, bE, CE, UT, Vl, wE, __, $_, E_, SE, IE, vE, kE, NE, TE, _E, h_, $E, RE, DE, AE, x_, FE, PE, OE, y_, sE, LE, BE, zE, VE, WE, UE, GE, HE, C_, KE, w_, S_, qE, jE, XE, YE, QE, I_, k$, ZE, JE, eR, tR, rR, nR, p_, sR, aR, iR, mE];
for (let r of AQ)
Ja(r);
var $c = {};
Ke($c, { assertNotComplex: () => Bs, bindCanvasToFramebuffer: () => WQ, bindColorTextureToFramebuffer: () => jl, bindTextureToProgramUniformSampler: () => RI, bindTextureUnit: () => lR, bindVertexBufferToProgramAttribute: () => Kf, callAndCheck: () => ce, canBeRepresented: () => bI, createFragmentShader: () => wI, createFramebuffer: () => _I, createProgram: () => SI, createStaticIndexBuffer: () => kI, createStaticVertexBuffer: () => vI, createTexture: () => NI, createVertexShader: () => CI, getBatchDim: () => gi, getExtensionOrThrow: () => Nc, getFramebufferErrorMessage: () => mR, getMaxTexturesInShader: () => FI, getNumChannels: () => zQ, getProgramUniformLocation: () => EI, getProgramUniformLocationOrThrow: () => $I, getRowsCols: () => xi, getShapeAs3D: () => _c, getTextureShapeFromLogicalShape: () => DI, getWebGLDisjointQueryTimerVersion: () => PI, getWebGLErrorMessage: () => cR, getWebGLMaxTextureSize: () => AI, hasExtension: () => qr, isCapableOfRenderingToFloatTexture: () => OI, isDownloadFloatTextureEnabled: () => MI, isReshapeFree: () => yu, isWebGLFenceEnabled: () => LI, isWebGLVersionEnabled: () => jf, linkProgram: () => II, logShaderSourceAndInfoLog: () => Hf, resetMaxTextureSize: () => UQ, resetMaxTexturesInShader: () => GQ, unbindColorTextureFromFramebuffer: () => qf, unbindTextureUnit: () => VQ, validateFramebuffer: () => Tc, validateProgram: () => ql, validateTextureSize: () => TI });
var xp = {};
var Vf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
function gI(r, e) {
xp[r] = e;
}
function Kr(r, e) {
if (!(r in xp) || e != null) {
let o = PQ(r, e);
if (o !== null)
xp[r] = o;
else
return console.log("Could not get context for WebGL version", r), null;
}
let t8 = xp[r];
return t8 == null || t8.isContextLost() ? (delete xp[r], Kr(r)) : (t8.disable(t8.DEPTH_TEST), t8.disable(t8.STENCIL_TEST), t8.disable(t8.BLEND), t8.disable(t8.DITHER), t8.disable(t8.POLYGON_OFFSET_FILL), t8.disable(t8.SAMPLE_COVERAGE), t8.enable(t8.SCISSOR_TEST), t8.enable(t8.CULL_FACE), t8.cullFace(t8.BACK), xp[r]);
}
function FQ(r) {
if (!A().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 PQ(r, e) {
if (r !== 1 && r !== 2)
throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
let t8 = e == null ? FQ(r) : e;
return t8.addEventListener("webglcontextlost", (o) => {
o.preventDefault(), delete xp[r];
}, false), A().getBool("SOFTWARE_WEBGL_ENABLED") && (Vf.failIfMajorPerformanceCaveat = false), r === 1 ? t8.getContext("webgl", Vf) || t8.getContext("experimental-webgl", Vf) : t8.getContext("webgl2", Vf);
}
var xu;
(function(r) {
r[r.DENSE = 0] = "DENSE", r[r.SHARED_BATCH = 1] = "SHARED_BATCH";
})(xu || (xu = {}));
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 tr;
(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";
})(tr || (tr = {}));
function yp(r, e) {
return [e, r];
}
function uR(r, e) {
return r * e;
}
function Hl(r) {
let e = y.sizeFromShape(r), t8 = Math.ceil(e / 4);
return y.sizeToSquarishShape(t8);
}
function Pa(r, e) {
return [Math.max(1, Math.ceil(e / 2)), Math.max(1, Math.ceil(r / 2))];
}
function pR(r, e) {
let [t8, o] = Pa(r, e);
return t8 * o * 4;
}
function Kl(r, e) {
let t8 = r, o, n, s, a, i, p, u, c, l, m;
return A().getNumber("WEBGL_VERSION") === 2 ? (o = t8.R32F, n = t8.R16F, s = t8.RGBA16F, a = t8.RGBA32F, i = t8.RED, u = 4, c = 1, l = t8.HALF_FLOAT, m = t8.FLOAT, p = t8.RGBA8) : (o = r.RGBA, n = r.RGBA, s = r.RGBA, a = t8.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 t8 = e();
return A().getBool("DEBUG") && OQ(r), t8;
}
function OQ(r) {
let e = r.getError();
if (e !== r.NO_ERROR)
throw new Error("WebGL Error: " + cR(r, e));
}
var MQ = 596e-10;
var LQ = 65504;
function bI(r) {
return !!(A().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || r === 0 || MQ < Math.abs(r) && Math.abs(r) < LQ);
}
function cR(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 Nc(r, e) {
return hi(r, () => r.getExtension(e), 'Extension "' + e + '" not supported on this browser.');
}
function CI(r, e) {
let t8 = hi(r, () => r.createShader(r.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
if (ce(r, () => r.shaderSource(t8, e)), ce(r, () => r.compileShader(t8)), r.getShaderParameter(t8, r.COMPILE_STATUS) === false)
throw console.log(r.getShaderInfoLog(t8)), new Error("Failed to compile vertex shader.");
return t8;
}
function wI(r, e) {
let t8 = hi(r, () => r.createShader(r.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
if (ce(r, () => r.shaderSource(t8, e)), ce(r, () => r.compileShader(t8)), A().get("ENGINE_COMPILE_ONLY"))
return t8;
if (r.getShaderParameter(t8, r.COMPILE_STATUS) === false)
throw Hf(e, r.getShaderInfoLog(t8)), new Error("Failed to compile fragment shader.");
return t8;
}
var BQ = /ERROR: [0-9]+:([0-9]+):/g;
function Hf(r, e) {
let t8 = BQ.exec(e);
if (t8 == null) {
console.log(`Couldn't parse line number in error: ${e}`), console.log(r);
return;
}
let o = +t8[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 ${y.rightPad(u[0], i)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(`
`));
}
function SI(r) {
return hi(r, () => r.createProgram(), "Unable to create WebGLProgram.");
}
function II(r, e) {
if (ce(r, () => r.linkProgram(e)), !A().get("ENGINE_COMPILE_ONLY") && r.getProgramParameter(e, r.LINK_STATUS) === false)
throw console.log(r.getProgramInfoLog(e)), new Error("Failed to link vertex and fragment shaders.");
}
function ql(r, e) {
if (ce(r, () => r.validateProgram(e)), r.getProgramParameter(e, r.VALIDATE_STATUS) === false)
throw console.log(r.getProgramInfoLog(e)), new Error("Shader program validation failed.");
}
function vI(r, e) {
let t8 = hi(r, () => r.createBuffer(), "Unable to create WebGLBuffer");
return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, t8)), ce(r, () => r.bufferData(r.ARRAY_BUFFER, e, r.STATIC_DRAW)), t8;
}
function kI(r, e) {
let t8 = hi(r, () => r.createBuffer(), "Unable to create WebGLBuffer");
return ce(r, () => r.bindBuffer(r.ELEMENT_ARRAY_BUFFER, t8)), ce(r, () => r.bufferData(r.ELEMENT_ARRAY_BUFFER, e, r.STATIC_DRAW)), t8;
}
function zQ() {
return A().getNumber("WEBGL_VERSION") === 2 ? 1 : 4;
}
function NI(r) {
return hi(r, () => r.createTexture(), "Unable to create WebGLTexture.");
}
function TI(r, e) {
let t8 = A().getNumber("WEBGL_MAX_TEXTURE_SIZE");
if (r <= 0 || e <= 0) {
let o = `[${r}x${e}]`;
throw new Error("Requested texture size " + o + " is invalid.");
}
if (r > t8 || e > t8) {
let o = `[${r}x${e}]`, n = `[${t8}x${t8}]`;
throw new Error("Requested texture size " + o + " greater than WebGL maximum on this browser / GPU " + n + ".");
}
}
function _I(r) {
return hi(r, () => r.createFramebuffer(), "Unable to create WebGLFramebuffer.");
}
function Kf(r, e, t8, o, n, s, a) {
let i = r.getAttribLocation(e, t8);
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 lR(r, e, t8) {
dR(r, t8), ce(r, () => r.activeTexture(r.TEXTURE0 + t8)), ce(r, () => r.bindTexture(r.TEXTURE_2D, e));
}
function VQ(r, e) {
dR(r, e), ce(r, () => r.activeTexture(r.TEXTURE0 + e)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function $I(r, e, t8) {
return hi(r, () => r.getUniformLocation(e, t8), 'uniform "' + t8 + '" not present in program.');
}
function EI(r, e, t8) {
return r.getUniformLocation(e, t8);
}
function RI(r, e, t8, o) {
ce(r, () => lR(r, e, o)), ce(r, () => r.uniform1i(t8, o));
}
function WQ(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, t8) {
ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, t8)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, e, 0));
}
function qf(r, e) {
ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, e)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, null, 0));
}
function Tc(r) {
let e = r.checkFramebufferStatus(r.FRAMEBUFFER);
if (e !== r.FRAMEBUFFER_COMPLETE)
throw new Error("Error binding framebuffer: " + mR(r, e));
}
function mR(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 ${e}`;
}
}
function hi(r, e, t8) {
let o = ce(r, () => e());
if (o == null)
throw new Error(t8);
return o;
}
function dR(r, e) {
let t8 = r.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, o = e + r.TEXTURE0;
if (o < r.TEXTURE0 || o > t8) {
let n = `[gl.TEXTURE0, gl.TEXTURE${t8}]`;
throw new Error(`textureUnit must be in ${n}.`);
}
}
function gi(r, e = 2) {
return y.sizeFromShape(r.slice(0, r.length - e));
}
function xi(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 _c(r) {
let e = [1, 1, 1];
return r.length === 0 || r.length === 1 && r[0] === 1 || (e = [gi(r), ...xi(r)]), e;
}
function DI(r, e = false) {
let t8 = A().getNumber("WEBGL_MAX_TEXTURE_SIZE"), o = A().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");
o === 1 / 0 && A().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE") && (o = t8 / 2), e && (t8 = t8 * 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 <= t8 ? s = [1, n] : r.length === 2 && r[0] <= t8 && r[1] <= t8 ? s = r : r.length === 3 && r[0] * r[1] <= t8 && r[2] <= t8 ? s = [r[0] * r[1], r[2]] : r.length === 3 && r[0] <= t8 && r[1] * r[2] <= t8 ? s = [r[0], r[1] * r[2]] : r.length === 4 && r[0] * r[1] * r[2] <= t8 && r[3] <= t8 ? s = [r[0] * r[1] * r[2], r[3]] : r.length === 4 && r[0] <= t8 && r[1] * r[2] * r[3] <= t8 && (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 = gi(r), p = 2, u = 2;
r.length && ([p, u] = xi(r)), n = i * (p / 2) * (u / 2), s = y.sizeToSquarishShape(n).map((c) => c * 2);
} else
s = y.sizeToSquarishShape(n);
return s;
}
function Wf(r) {
return r % 2 === 0;
}
function yu(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 t8 = r[r.length - 1], o = e[e.length - 1];
if (t8 === o || Wf(t8) && Wf(o) && (r[0] === 1 || e[0] === 1))
return true;
}
return r[1] === e[1] && Wf(r[0]) && Wf(e[0]);
}
var Uf;
var Gf;
function AI(r) {
if (Uf == null) {
let e = Kr(r);
Uf = e.getParameter(e.MAX_TEXTURE_SIZE);
}
return Uf;
}
function UQ() {
Uf = null;
}
function GQ() {
Gf = null;
}
function FI(r) {
if (Gf == null) {
let e = Kr(r);
Gf = e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS);
}
return Math.min(16, Gf);
}
function PI(r) {
if (r === 0)
return 0;
let e, t8 = Kr(r);
return qr(t8, "EXT_disjoint_timer_query_webgl2") && r === 2 ? e = 2 : qr(t8, "EXT_disjoint_timer_query") ? e = 1 : e = 0, e;
}
function qr(r, e) {
return r.getExtension(e) != null;
}
function jf(r) {
try {
if (Kr(r) != null)
return true;
} catch (e) {
return console.log("Error when getting WebGL context: ", e), false;
}
return false;
}
function OI(r) {
if (r === 0)
return false;
let e = Kr(r);
if (r === 1) {
if (!qr(e, "OES_texture_float"))
return false;
} else if (!qr(e, "EXT_color_buffer_float"))
return false;
return yI(e);
}
function MI(r) {
if (r === 0)
return false;
let e = Kr(r);
if (r === 1) {
if (!qr(e, "OES_texture_float") || !qr(e, "WEBGL_color_buffer_float"))
return false;
} else {
if (qr(e, "EXT_color_buffer_float"))
return yI(e);
let o = "EXT_color_buffer_half_float";
if (qr(e, o)) {
let n = e.getExtension(o);
return HQ(e, n);
}
return false;
}
return yI(e);
}
function yI(r) {
let e = Kl(r), t8 = r.createTexture();
r.bindTexture(r.TEXTURE_2D, t8);
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, t8, 0);
let a = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE;
return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(t8), r.deleteFramebuffer(s), a;
}
function HQ(r, e) {
let t8 = Kl(r, e), o = r.createTexture();
r.bindTexture(r.TEXTURE_2D, o);
let n = 1, s = 1;
r.texImage2D(r.TEXTURE_2D, 0, t8.internalFormatHalfFloat, n, s, 0, t8.textureFormatFloat, t8.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 LI(r) {
return r !== 2 ? false : Kr(r).fenceSync != null;
}
function Bs(r, e) {
Array.isArray(r) || (r = [r]), r.forEach((t8) => {
t8 != null && y.assert(t8.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGL backend.`);
});
}
var Se = A();
Se.registerFlag("HAS_WEBGL", () => Se.getNumber("WEBGL_VERSION") > 0);
Se.registerFlag("WEBGL_VERSION", () => jf(2) ? 2 : jf(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", () => AI(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => FI(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
let r = Se.getNumber("WEBGL_VERSION");
return r === 0 ? 0 : PI(r);
});
Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !ru.isMobile());
Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => OI(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", () => MI(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_FENCE_API_ENABLED", () => LI(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", () => ru.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 vt() {
let r, e, t8, o, n, s, a, i, p, u;
return A().getNumber("WEBGL_VERSION") === 2 ? (r = "#version 300 es", e = "in", t8 = "out", o = "in", n = "texture", s = "outputColor", a = "out vec4 outputColor;", i = A().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", t8 = "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: t8, varyingFs: o, texture2D: n, output: s, defineOutput: a, defineSpecialNaN: i, defineSpecialInf: p, defineRound: u };
}
function zs(r, e, t8 = "index") {
let o = y.computeStrides(e);
return o.map((n, s) => {
let a = `int ${r[s]} = ${t8} / ${n}`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t8} - ${r[s]} * ${n}` : `index -= ${r[s]} * ${n}`;
return `${a}; ${i};`;
}).join("");
}
function bp(r, e, t8 = "index") {
let o = y.computeStrides(e);
return o.map((n, s) => {
let a = `int ${r[s]} = ${t8} / outShapeStrides[${s}]`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t8} - ${r[s]} * outShapeStrides[${s}]` : `index -= ${r[s]} * outShapeStrides[${s}]`;
return `${a}; ${i};`;
}).join("");
}
function KQ(r, e) {
let t8 = r.length, o = r.map((s) => `${e}[${s}]`), n = new Array(t8 - 1);
n[t8 - 2] = o[t8 - 1];
for (let s = t8 - 3; s >= 0; --s)
n[s] = `(${n[s + 1]} * ${o[s + 1]})`;
return n;
}
function fR(r, e, t8 = "index") {
let o = r.map((s, a) => a), n = KQ(o, e);
return n.map((s, a) => {
let i = `int ${r[a]} = ${t8} / ${n[a]}`, p = a === n.length - 1 ? `int ${r[a + 1]} = ${t8} - ${r[a]} * ${n[a]}` : `index -= ${r[a]} * ${n[a]}`;
return `${i}; ${p};`;
}).join("");
}
function Ec(r) {
let e = y.computeStrides(r).map((t8) => t8.toString());
return `
int getFlatIndex(ivec3 coords) {
return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
}
`;
}
function Rc() {
return `
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`;
}
var Xf = `
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: hR } = w;
function gR(r, e, t8) {
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};`)), t8.enableShapeUniforms) {
let { uniformShape: h } = Yf(t8.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;`);
}
}), t8.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;");
}
t8.customUniforms && t8.customUniforms.forEach((d) => {
o.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`);
});
let n = o.join(`
`), s = r.map((d) => qQ(d, e, t8.packedInputs, t8.enableShapeUniforms)).join(`
`), a = e.texShape, i = vt(), p = YQ(i), u, c, l = JQ(i);
return e.isPacked ? (u = jQ(e.logicalShape, a, t8.enableShapeUniforms), c = ZQ(i)) : (u = XQ(e.logicalShape, a, t8.enableShapeUniforms), c = QQ(i)), t8.packedInputs && (l += oZ), [l, p, c, n, u, s, t8.userCode].join(`
`);
}
function Ac(r, e = false) {
let t8 = r.shapeInfo.logicalShape;
switch (t8.length) {
case 0:
return hZ(r, e);
case 1:
return xZ(r, e);
case 2:
return bZ(r, e);
case 3:
return wZ(r, e);
case 4:
return IZ(r, e);
case 5:
return vZ(r);
case 6:
return kZ(r);
default:
throw new Error(`${t8.length}-D input sampling is not yet supported`);
}
}
function xR(r, e) {
switch (r.shapeInfo.logicalShape.length) {
case 0:
return fZ(r);
case 1:
return gZ(r, e);
case 2:
return yZ(r, e);
case 3:
return CZ(r, e);
default:
return SZ(r, e);
}
}
function qQ(r, e, t8 = false, o) {
let n = "";
t8 ? n += xR(r, o) : n += Ac(r, o);
let s = r.shapeInfo.logicalShape, a = e.logicalShape;
return s.length <= a.length && (t8 ? n += NZ(r, e) : n += TZ(r, e)), n;
}
function jQ(r, e, t8) {
switch (r.length) {
case 0:
return yR();
case 1:
return nZ(r, e, t8);
case 2:
return mZ(r, e, t8);
case 3:
return aZ(r, e, t8);
default:
return uZ(r, e, t8);
}
}
function XQ(r, e, t8) {
switch (r.length) {
case 0:
return yR();
case 1:
return sZ(r, e, t8);
case 2:
return dZ(r, e, t8);
case 3:
return iZ(r, e, t8);
case 4:
return pZ(r, e, t8);
case 5:
return cZ(r, e);
case 6:
return lZ(r, e);
default:
throw new Error(`${r.length}-D output sampling is not yet supported`);
}
}
function YQ(r) {
return `
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${r.texture2D}(textureSampler, uv).r;
}
`;
}
function QQ(r) {
return `
void setOutput(float val) {
${r.output} = vec4(val, 0, 0, 0);
}
`;
}
function ZQ(r) {
return `
void setOutput(vec4 val) {
${r.output} = val;
}
`;
}
function JQ(r) {
return `${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);
}
${eZ}
${tZ}
${rZ}
`;
}
var eZ = `
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 tZ = `
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 rZ = `
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 oZ = `
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 yR() {
return `
int getOutputCoords() {
return 0;
}
`;
}
function nZ(r, e, t8) {
let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)];
return o[0] === 1 ? t8 ? `
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 ? t8 ? `
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
` : `
int getOutputCoords() {
return 2 * int(resultUV.y * ${o[0]}.0);
}
` : t8 ? `
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
` : `
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${o[0]}, ${o[1]}));
return 2 * (resTexRC.x * ${o[1]} + resTexRC.y);
}
`;
}
function sZ(r, e, t8) {
return e[0] === 1 ? t8 ? `
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
` : `
int getOutputCoords() {
return int(resultUV.x * ${e[1]}.0);
}
` : e[1] === 1 ? t8 ? `
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
` : `
int getOutputCoords() {
return int(resultUV.y * ${e[0]}.0);
}
` : t8 ? `
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
` : `
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
return resTexRC.x * ${e[1]} + resTexRC.y;
}
`;
}
function aZ(r, e, t8) {
if (t8)
return `
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;
let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)], n = Math.ceil(r[2] / 2), s = n * Math.ceil(r[1] / 2);
return `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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 iZ(r, e, t8) {
if (t8)
return `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${bp(["r", "c", "d"], r)}
return ivec3(r, c, d);
}
`;
let o = zs(["r", "c", "d"], r);
return `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${o}
return ivec3(r, c, d);
}
`;
}
function uZ(r, e, t8) {
if (t8)
return `
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;
let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)], n = Math.ceil(r[r.length - 1] / 2), s = n * Math.ceil(r[r.length - 2] / 2), a = s, i = "", p = "b, r, c";
for (let u = 2; u < r.length - 1; u++)
a *= r[r.length - u - 1], i = `
int b${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 pZ(r, e, t8) {
if (t8)
return `
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${bp(["r", "c", "d", "d2"], r)}
return ivec4(r, c, d, d2);
}
`;
let o = zs(["r", "c", "d", "d2"], r);
return `
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${o}
return ivec4(r, c, d, d2);
}
`;
}
function cZ(r, e) {
let t8 = zs(["r", "c", "d", "d2", "d3"], r);
return `
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t8}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`;
}
function lZ(r, e) {
let t8 = zs(["r", "c", "d", "d2", "d3", "d4"], r);
return `
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t8}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`;
}
function mZ(r, e, t8) {
let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)];
if (y.arraysEqual(r, e))
return t8 ? `
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
` : `
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${o[0]}, ${o[1]}));
}
`;
let n = Math.ceil(r[1] / 2);
return t8 ? `
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
` : `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${o[0]}, ${o[1]}));
int index = resTexRC.x * ${o[1]} + resTexRC.y;
int r = 2 * (index / ${n});
int c = imod(index, ${n}) * 2;
return ivec2(r, c);
}
`;
}
function dZ(r, e, t8) {
return y.arraysEqual(r, e) ? t8 ? `
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
` : `
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
}
` : r[1] === 1 ? t8 ? `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
` : `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(index, 0);
}
` : r[0] === 1 ? t8 ? `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
` : `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(0, index);
}
` : t8 ? `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
` : `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
int r = index / ${r[1]};
int c = index - r * ${r[1]};
return ivec2(r, c);
}
`;
}
function Cp(r) {
return `offset${r}`;
}
function fZ(r) {
let e = r.name, t8 = "get" + e.charAt(0).toUpperCase() + e.slice(1), o = vt();
return `
vec4 ${t8}() {
return ${o.texture2D}(${e}, halfCR);
}
`;
}
function hZ(r, e) {
let t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1);
if (r.shapeInfo.isUniform)
return `float ${o}() {return ${t8};}`;
let [n, s] = r.shapeInfo.texShape;
if (n === 1 && s === 1)
return `
float ${o}() {
return sampleTexture(${t8}, halfCR);
}
`;
let a = Cp(t8);
if (e)
return `
float ${o}() {
vec2 uv = uvFromFlat(${t8}TexShape[0], ${t8}TexShape[1], ${a});
return sampleTexture(${t8}, uv);
}
`;
let [i, p] = r.shapeInfo.texShape;
return `
float ${o}() {
vec2 uv = uvFromFlat(${i}, ${p}, ${a});
return sampleTexture(${t8}, uv);
}
`;
}
function gZ(r, e) {
let t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1), n = r.shapeInfo.texShape, s = vt();
if (e)
return `
vec4 ${o}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${t8}TexShape[0]) / 2.0), ceil(float(${t8}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${s.texture2D}(${t8}, 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 ${s.texture2D}(${t8}, uv);
}
`;
}
function xZ(r, e) {
let t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1);
if (r.shapeInfo.isUniform)
return `
float ${o}(int index) {
${Fc(r)}
}
`;
let n = r.shapeInfo.texShape, s = n[0], a = n[1];
if (a === 1 && s === 1)
return `
float ${o}(int index) {
return sampleTexture(${t8}, halfCR);
}
`;
let i = Cp(t8);
return a === 1 ? e ? `
float ${o}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t8}TexShape[0]));
return sampleTexture(${t8}, uv);
}
` : `
float ${o}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${s}.0);
return sampleTexture(${t8}, uv);
}
` : s === 1 ? e ? `
float ${o}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t8}TexShape[1]), 0.5);
return sampleTexture(${t8}, uv);
}
` : `
float ${o}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${a}.0, 0.5);
return sampleTexture(${t8}, uv);
}
` : e ? `
float ${o}(int index) {
vec2 uv = uvFromFlat(${t8}TexShape[0], ${t8}TexShape[1], index + ${i});
return sampleTexture(${t8}, uv);
}
` : `
float ${o}(int index) {
vec2 uv = uvFromFlat(${s}, ${a}, index + ${i});
return sampleTexture(${t8}, uv);
}
`;
}
function yZ(r, e) {
let t8 = 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 = vt();
if (s != null && y.arraysEqual(t8, 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(t8[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 bZ(r, e) {
let t8 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape;
if (s != null && y.arraysEqual(t8, 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(t8), p = a;
if (p.length < t8.length) {
let m = Pc(r, p), d = ["row", "col"];
return `
${Ac(m, e)}
float ${n}(int row, int col) {
return ${n}(${Oc(d, i)});
}
`;
}
if (r.shapeInfo.isUniform)
return `
float ${n}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t8[1]}, 1)));
${Fc(r)}
}
`;
let u = s[0], c = s[1], l = Cp(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, ${l}), vec3(${t8[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, ${l}), vec3(${t8[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 * ${t8[1]} + col + ${l};
vec2 uv = uvFromFlat(${u}, ${c}, index);
return sampleTexture(${o}, uv);
}
`;
}
function CZ(r, e) {
let t8 = 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 (t8[0] === 1) {
let m = t8.slice(1), d = [1, 2], f = Pc(r, m), h = ["b", "row", "col"];
return `
${xR(f, e)}
vec4 ${n}(int b, int row, int col) {
return ${n}(${Oc(h, d)});
}
`;
}
let i = vt();
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(t8[2] / 2), l = c * Math.ceil(t8[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 wZ(r, e) {
let t8 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t8[1] * t8[2], a = t8[2], { newShape: i, keptDims: p } = y.squeezeShape(t8), u = i;
if (u.length < t8.length) {
let h = Pc(r, u), g = ["row", "col", "depth"];
return `
${Ac(h, e)}
float ${n}(int row, int col, int depth) {
return ${n}(${Oc(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)));
${Fc(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 ${n}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t8[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${l}.0);
return sampleTexture(${o}, uv);
}
`;
let f = Cp(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 SZ(r, e) {
let t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1), n = vt();
if (e)
return `
vec4 ${o}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${t8}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${t8}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${t8}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${t8}TexShape[0]) / 2.0), ceil(float(${t8}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 ${n.texture2D}(${t8}, 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 ${n.texture2D}(${t8}, uv);
}
`;
}
function IZ(r, e) {
let t8 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t8[3], a = t8[2] * s, i = t8[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(t8);
if (p.length < t8.length) {
let b = Pc(r, p), C = ["row", "col", "depth", "depth2"];
return `
${Ac(b, e)}
float ${n}(int row, int col, int depth, int depth2) {
return ${n}(${Oc(C, 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)));
${Fc(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 ${n}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t8[1] * t8[2]}, ${t8[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${m}.0);
return sampleTexture(${o}, uv);
}
`;
let x = Cp(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 vZ(r) {
let e = r.shapeInfo.logicalShape, t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.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 = Pc(r, p), g = ["row", "col", "depth", "depth2", "depth3"];
return `
${Ac(h)}
float ${o}(int row, int col, int depth, int depth2, int depth3) {
return ${o}(${Oc(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;
${Fc(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, ${m}.0);
return sampleTexture(${t8}, 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, ${m}.0);
return sampleTexture(${t8}, uv);
}
`;
let f = Cp(t8);
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}, ${d}, index);
return sampleTexture(${t8}, uv);
}
`;
}
function kZ(r) {
let e = r.shapeInfo.logicalShape, t8 = r.name, o = "get" + t8.charAt(0).toUpperCase() + t8.slice(1), { newShape: n, keptDims: s } = y.squeezeShape(e);
if (n.length < e.length) {
let g = Pc(r, n), x = ["row", "col", "depth", "depth2", "depth3", "depth4"];
return `
${Ac(g)}
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${o}(${Oc(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)));
${Fc(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, ${d}.0);
return sampleTexture(${t8}, 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, ${d}.0);
return sampleTexture(${t8}, uv);
}
`;
let h = Cp(t8);
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}, ${f}, index);
return sampleTexture(${t8}, uv);
}
`;
}
function Fc(r) {
let e = r.name, t8 = y.sizeFromShape(r.shapeInfo.logicalShape);
return t8 < 2 ? `return ${e};` : `
for (int i = 0; i < ${t8}; i++) {
if (i == index) {
return ${e}[i];
}
}
`;
}
function NZ(r, e) {
let t8 = r.name, o = t8.charAt(0).toUpperCase() + t8.slice(1), n = "get" + o + "AtOutCoords", s = r.shapeInfo.logicalShape.length, a = e.logicalShape.length, i = hR(r.shapeInfo.logicalShape, e.logicalShape), p = Re(a), u = a - s, c, l = ["x", "y", "z", "w", "u", "v"];
s === 0 ? c = "" : a < 2 && i.length >= 1 ? c = "coords = 0;" : c = i.map((b) => `coords.${l[b + u]} = 0;`).join(`
`);
let m = "";
a < 2 && s > 0 ? m = "coords" : m = r.shapeInfo.logicalShape.map((b, C) => `coords.${l[C + 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, C = s - 1;
i.indexOf(b) > -1 && i.indexOf(C) > -1 ? d = "return vec4(outputValue.x);" : i.indexOf(b) > -1 ? d = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : i.indexOf(C) > -1 && (d = "return vec4(outputValue.xx, outputValue.zz);");
}
return `
vec4 ${n}() {
${p} coords = getOutputCoords();
${c}
vec4 outputValue = get${o}(${m});
${d}
}
`;
}
function TZ(r, e) {
let t8 = r.name, o = t8.charAt(0).toUpperCase() + t8.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 ${n}() {
return sampleTexture(${t8}, resultUV);
}
`;
let u = Re(p), c = hR(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 ${r} is not yet supported`);
}
function Yf(r, e, t8) {
let { newShape: o, keptDims: n } = y.squeezeShape(e), s = e.length, a = r && s === 3 && e[0] === 1, i = a ? e.slice(1) : o, p = !r && s > 1 && !y.arraysEqual(e, t8) && o.length < s || a;
return { useSqueezeShape: p, uniformShape: p ? i : e, keptDims: n };
}
function Pc(r, e) {
let t8 = JSON.parse(JSON.stringify(r));
return t8.shapeInfo.logicalShape = e, t8;
}
function Oc(r, e) {
return e.map((t8) => r[t8]).join(", ");
}
function CR(r, e, t8, o) {
let n = t8.map((c, l) => {
let m = { logicalShape: c.shape, texShape: c.isUniform ? null : c.texData.texShape, isUniform: c.isUniform, isPacked: c.isUniform ? false : c.texData.isPacked, flatOffset: null };
return c.texData != null && c.texData.slice != null && c.texData.slice.flatOffset > 0 && (m.flatOffset = c.texData.slice.flatOffset), { name: e.variableNames[l], shapeInfo: m };
}), s = n.map((c) => c.shapeInfo), a = { logicalShape: o.shape, texShape: o.texData.texShape, isUniform: false, isPacked: o.texData.isPacked, flatOffset: null }, i = gR(n, a, e), p = wI(r.gl, i), u = r.createProgram(p);
return A().get("ENGINE_COMPILE_ONLY") ? { program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a, variablesLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : (r.buildVao(u), Object.assign({ program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a }, BI(r, e, u)));
}
function BI(r, e, t8) {
let o = [], n = [], s, a, i, p = null, u = null;
u = r.getUniformLocation(t8, "NAN", false), A().getNumber("WEBGL_VERSION") === 1 && (p = r.getUniformLocation(t8, "INFINITY", false));
let c = false;
for (let l of e.variableNames) {
let m = { name: l, uniform: r.getUniformLocation(t8, l, c), offset: r.getUniformLocation(t8, `offset${l}`, c) };
e.enableShapeUniforms && (m.shape = r.getUniformLocation(t8, `${l}Shape`, c), m.texShape = r.getUniformLocation(t8, `${l}TexShape`, c)), o.push(m);
}
if (e.enableShapeUniforms && (s = r.getUniformLocation(t8, "outShape", c), i = r.getUniformLocation(t8, "outShapeStrides", c), a = r.getUniformLocation(t8, "outTexShape", c)), e.customUniforms)
for (let l of e.customUniforms)
n.push(r.getUniformLocation(t8, l.name, c));
return { variablesLocations: o, customUniformLocations: n, infLoc: p, nanLoc: u, outShapeLocation: s, outShapeStridesLocation: i, outTexShapeLocation: a };
}
function bR(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((t8, o) => {
let n = t8.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 (t8.isUniform && s.isUniform)
return;
let i = t8.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 wR(r, e, t8, o, n) {
e.program.enableShapeUniforms || (bR(e.inShapeInfos, t8), bR([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), A().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 < t8.length; ++p) {
let u = t8[p], { uniform: c, offset: l, shape: m, texShape: d } = e.variablesLocations[p];
if (m) {
let { uniformShape: f } = Yf(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 ${u.type} is not supported yet.`);
}
r.executeProgram();
}
function SR(r, e, t8) {
let o = "";
e.concat(t8).forEach((a) => {
let i = a.texData != null && a.texData.slice != null && a.texData.slice.flatOffset > 0;
if (r.enableShapeUniforms && !a.isUniform) {
let p = a.texData.texShape, { useSqueezeShape: u, uniformShape: c, keptDims: l } = Yf(r.packedInputs, a.shape, p), m = "", d = "", f = "";
if (c.length === 1 && r.packedInputs) {
let k = [Math.ceil(p[0] / 2), Math.ceil(p[1] / 2)];
m = `${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]}_${k[k.length - 1] === p[1]}`;
}
let h = a.shape.length, g = c.length === 2 && y.arraysEqual(a.shape, p), x = y.sizeFromShape(a.shape) === 1, b = w.getBroadcastDims(a.shape, t8.shape), C = !r.packedInputs && h === t8.shape.length && y.arraysEqual(p, t8.texData.texShape), S = r.packedInputs || c.length > 2 ? "" : `${p[0] > 1}_${p[1] > 1}`;
o += `${h}_${C}_${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 + `${A().getNumber("WEBGL_VERSION")}`, s;
}
function pt(r) {
return A().getBool("WEBGL_USE_SHAPES_UNIFORMS") && r <= 4;
}
var Qf = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = xu.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
let t8 = vt();
this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = `
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms ? bp(["r", "c", "d"], e) : zs(["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);
}
${t8.output} = result;
}
`;
}
};
var Zf = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = xu.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
let t8 = vt();
this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = `
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms ? bp(["r", "c", "d"], e) : zs(["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));
}
${t8.output} = result;
}
`;
}
};
var Jf = class {
constructor(e) {
this.variableNames = ["A"], this.outTexUsage = mr.DOWNLOAD;
let t8 = vt();
this.outputShape = e, this.userCode = `
${Xf}
void main() {
float x = getAAtOutCoords();
${t8.output} = encode_float(x);
}
`;
}
};
var eh = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = mr.DOWNLOAD;
let t8 = vt();
this.outputShape = e, this.userCode = `
${Xf}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t8.output} = encode_float(x);
}
`;
}
};
var EZ = { R: 0, G: 1, B: 2, A: 3 };
var Xl = class {
constructor(e, t8 = false, o = "RGBA") {
this.variableNames = ["A"], this.customUniforms = [{ name: "texShape", type: "ivec2" }];
let n = vt();
this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length);
let s = "result";
t8 && (s = "floor(result * 255. + 0.5)");
let a = "";
for (let i = 0; i < o.length; i++) {
let p = o[i];
a += `
if(offset == ${i}) {
result = values[${EZ[p]}];
}`;
}
this.userCode = `
${this.enableShapeUniforms ? Rc() : Ec(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 th = class {
constructor(e, t8 = false) {
this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
let o = vt();
this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length);
let n = "", s = "result";
t8 && (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 ? Rc() : Ec(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${n}
${o.output} = ${s};
}
`;
}
};
var rv = {};
Ke(rv, { bindVertexProgramAttributeStreams: () => jI, createBufferFromOutputTexture: () => QI, createFloat16MatrixTexture: () => GI, createFloat16PackedMatrixTexture: () => qI, createFloat32MatrixTexture: () => UI, createIndexBuffer: () => WI, createPackedMatrixTexture: () => KI, createUnsignedBytesMatrixTexture: () => HI, createVertexBuffer: () => VI, createVertexShader: () => zI, downloadByteEncodedFloatMatrixFromOutputTexture: () => JI, downloadFloat32MatrixFromBuffer: () => ZI, downloadMatrixFromPackedOutputTexture: () => tv, downloadPackedMatrixFromBuffer: () => ev, getInternalFormatForFloat16MatrixTexture: () => oh, getInternalFormatForFloat16PackedMatrixTexture: () => ah, getInternalFormatForFloat32MatrixTexture: () => rh, getInternalFormatForPackedMatrixTexture: () => sh, getInternalFormatForUnsignedBytesMatrixTexture: () => nh, uploadDenseMatrixToTexture: () => XI, uploadPixelDataToTexture: () => YI });
function zI(r) {
let e = vt(), t8 = `${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 CI(r, t8);
}
function VI(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 vI(r, e);
}
function WI(r) {
let e = new Uint16Array([0, 1, 2, 2, 1, 3]);
return kI(r, e);
}
function Yl(r, e, t8, o, n, s) {
TI(e, t8);
let a = NI(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)), A().getNumber("WEBGL_VERSION") === 1 ? ce(r, () => r.texImage2D(i, 0, o, e, t8, 0, n, s, null)) : ce(r, () => r.texStorage2D(i, 1, o, e, t8)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)), { texture: a, texShape: [t8, e] };
}
function rh(r) {
return r.internalFormatFloat;
}
function UI(r, e, t8, o) {
let [n, s] = yp(e, t8);
return Yl(r, n, s, rh(o), o.textureFormatFloat, r.FLOAT);
}
function oh(r) {
return r.internalFormatHalfFloat;
}
function GI(r, e, t8, o) {
let [n, s] = yp(e, t8);
return Yl(r, n, s, oh(o), o.textureFormatFloat, o.textureTypeHalfFloat);
}
function nh(r) {
return r.downloadTextureFormat;
}
function HI(r, e, t8, o) {
let [n, s] = yp(e, t8);
return Yl(r, n, s, nh(o), r.RGBA, r.UNSIGNED_BYTE);
}
function sh(r) {
return r.internalFormatPackedFloat;
}
function KI(r, e, t8, o) {
let [n, s] = Pa(e, t8);
return Yl(r, n, s, sh(o), r.RGBA, r.FLOAT);
}
function ah(r) {
return r.internalFormatPackedHalfFloat;
}
function qI(r, e, t8, o) {
let [n, s] = Pa(e, t8);
return Yl(r, n, s, ah(o), r.RGBA, o.textureTypeHalfFloat);
}
function jI(r, e, t8) {
return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, t8)), Kf(r, e, "clipSpacePos", t8, 3, 20, 0) && Kf(r, e, "uv", t8, 2, 20, 12);
}
function XI(r, e, t8, o, n, s) {
ce(r, () => r.bindTexture(r.TEXTURE_2D, e));
let a, i, p;
n instanceof Uint8Array ? (a = new Uint8Array(t8 * o * 4), i = r.UNSIGNED_BYTE, p = r.RGBA) : (a = new Float32Array(t8 * o * 4), i = r.FLOAT, p = s.internalFormatPackedFloat), a.set(n), A().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t8, o, r.RGBA, i, a)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, p, t8, o, 0, r.RGBA, i, a)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function YI(r, e, t8) {
ce(r, () => r.bindTexture(r.TEXTURE_2D, e)), t8.data instanceof Uint8Array ? A().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t8.width, t8.height, r.RGBA, r.UNSIGNED_BYTE, t8.data)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, t8.width, t8.height, 0, r.RGBA, r.UNSIGNED_BYTE, t8.data)) : A().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, r.RGBA, r.UNSIGNED_BYTE, t8)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, r.RGBA, r.UNSIGNED_BYTE, t8)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function QI(r, e, t8, o) {
let n = r.createBuffer();
ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, n));
let i = 4 * 4 * e * t8;
return ce(r, () => r.bufferData(r.PIXEL_PACK_BUFFER, i, r.STREAM_READ)), ce(r, () => r.readPixels(0, 0, t8, e, r.RGBA, r.FLOAT, 0)), ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, null)), n;
}
function ZI(r, e, t8) {
let o = r, n = new Float32Array(t8);
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 JI(r, e, t8, o) {
let [n, s] = yp(e, t8), a = 4, i = new Uint8Array(uR(e * t8, a));
return ce(r, () => r.readPixels(0, 0, n, s, o.downloadTextureFormat, r.UNSIGNED_BYTE, i)), new Float32Array(i.buffer);
}
function ev(r, e, t8, o, n, s, a, i) {
let p = r, u = new Float32Array(pR(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 tv(r, e, t8) {
let o = new Float32Array(e * t8 * 4);
return ce(r, () => r.readPixels(0, 0, t8, e, r.RGBA, r.FLOAT, o)), o;
}
var wp = class {
constructor(e) {
this.outputTexture = null, this.program = null, this.disposed = false, this.itemsToPoll = [];
let t8 = A().getNumber("WEBGL_VERSION");
if (e != null ? (this.gl = e, gI(t8, e)) : this.gl = Kr(t8), e = this.gl, A().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"), A().getNumber("WEBGL_VERSION") === 1) {
let s = "OES_texture_float", a = "OES_texture_half_float";
if (this.textureFloatExtension = Nc(this.gl, s), qr(this.gl, a))
this.textureHalfFloatExtension = Nc(this.gl, a);
else if (A().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), qr(this.gl, n))
this.colorBufferHalfFloatExtension = Nc(this.gl, n);
else if (A().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", qr(this.gl, o))
this.colorBufferFloatExtension = this.gl.getExtension(o);
else if (qr(this.gl, n))
this.colorBufferHalfFloatExtension = this.gl.getExtension(n);
else
throw new Error("GL context does not support color renderable floats");
this.vertexBuffer = VI(this.gl), this.indexBuffer = WI(this.gl), this.framebuffer = _I(this.gl), this.textureConfig = Kl(this.gl, this.textureHalfFloatExtension);
}
get debug() {
return A().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, t8) {
return this.throwIfDisposed(), UI(this.gl, e, t8, this.textureConfig);
}
createFloat16MatrixTexture(e, t8) {
return this.throwIfDisposed(), GI(this.gl, e, t8, this.textureConfig);
}
createUnsignedBytesMatrixTexture(e, t8) {
return this.throwIfDisposed(), HI(this.gl, e, t8, this.textureConfig);
}
uploadPixelDataToTexture(e, t8) {
this.throwIfDisposed(), YI(this.gl, e, t8);
}
uploadDenseMatrixToTexture(e, t8, o, n) {
this.throwIfDisposed(), XI(this.gl, e, t8, o, n, this.textureConfig);
}
createFloat16PackedMatrixTexture(e, t8) {
return this.throwIfDisposed(), qI(this.gl, e, t8, this.textureConfig);
}
createPackedMatrixTexture(e, t8) {
return this.throwIfDisposed(), KI(this.gl, e, t8, this.textureConfig);
}
deleteMatrixTexture(e) {
this.throwIfDisposed(), this.outputTexture === e && (qf(this.gl, this.framebuffer), this.outputTexture = null), ce(this.gl, () => this.gl.deleteTexture(e));
}
downloadByteEncodedFloatMatrixFromOutputTexture(e, t8, o) {
return this.downloadMatrixDriver(e, () => JI(this.gl, t8, o, this.textureConfig));
}
downloadPackedMatrixFromBuffer(e, t8, o, n, s, a) {
return ev(this.gl, e, t8, o, n, s, a, this.textureConfig);
}
downloadFloat32MatrixFromBuffer(e, t8) {
return ZI(this.gl, e, t8);
}
createBufferFromTexture(e, t8, o) {
this.bindTextureToFrameBuffer(e);
let n = QI(this.gl, t8, o, this.textureConfig);
return this.unbindTextureToFrameBuffer(), n;
}
createAndWaitForFence() {
let e = this.createFence(this.gl);
return this.pollFence(e);
}
createFence(e) {
let t8, o;
if (A().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;
}, t8 = s;
} else
A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? (t8 = this.beginQuery(), this.endQuery(), o = () => this.isQueryAvailable(t8, A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))) : o = () => true;
return { query: t8, isFencePassed: o };
}
downloadMatrixFromPackedTexture(e, t8, o) {
return this.downloadMatrixDriver(e, () => tv(this.gl, t8, o));
}
createProgram(e) {
this.throwIfDisposed();
let t8 = this.gl;
this.vertexShader == null && (this.vertexShader = zI(t8));
let o = SI(t8);
ce(t8, () => t8.attachShader(o, this.vertexShader)), ce(t8, () => t8.attachShader(o, e)), II(t8, o);
let n = Object.assign(o, { vao: this.createVertexArray() });
return this.debug && ql(t8, n), n;
}
buildVao(e) {
this.setProgram(e), this.bindVertexArray(e.vao);
let t8 = this.gl;
ce(t8, () => t8.bindBuffer(t8.ELEMENT_ARRAY_BUFFER, this.indexBuffer)), jI(t8, 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, t8, o = true) {
return this.throwIfDisposed(), o ? $I(this.gl, e, t8) : EI(this.gl, e, t8);
}
getAttributeLocation(e, t8) {
return this.throwIfDisposed(), ce(this.gl, () => this.gl.getAttribLocation(e, t8));
}
getUniformLocationNoThrow(e, t8) {
return this.throwIfDisposed(), this.gl.getUniformLocation(e, t8);
}
setInputMatrixTexture(e, t8, o) {
this.throwIfDisposed(), this.throwIfNoProgram(), RI(this.gl, e, t8, o);
}
setOutputMatrixTexture(e, t8, o) {
this.setOutputMatrixTextureDriver(e, o, t8);
}
setOutputPackedMatrixTexture(e, t8, o) {
this.throwIfDisposed();
let [n, s] = Pa(t8, o);
this.setOutputMatrixTextureDriver(e, n, s);
}
setOutputMatrixWriteRegion(e, t8, o, n) {
this.setOutputMatrixWriteRegionDriver(o, e, n, t8);
}
setOutputPackedMatrixWriteRegion(e, t8, o, n) {
throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
}
debugValidate() {
this.program != null && ql(this.gl, this.program), Tc(this.gl);
}
executeProgram() {
this.throwIfDisposed(), this.throwIfNoProgram();
let e = this.gl;
if (this.debug) {
let t8 = this.getVertexArray();
console.assert(t8 === 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 = Nc(this.gl, A().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 (A().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(), t8 = e.createQueryEXT();
return e.beginQueryEXT(e.TIME_ELAPSED_EXT, t8), t8;
}
endQuery() {
if (A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
let t8 = this.gl, o = this.getQueryTimerExtensionWebGL2();
t8.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, A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))), this.getQueryTime(e, A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
}
getQueryTime(e, t8) {
if (t8 === 0)
return null;
if (t8 === 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, t8) {
if (t8 === 0)
return true;
if (t8 === 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((t8) => {
this.addItemToPoll(() => e.isFencePassed(), () => t8());
});
}
pollItems() {
let e = RZ(this.itemsToPoll.map((t8) => t8.isDoneFn));
for (let t8 = 0; t8 <= e; ++t8) {
let { resolveFn: o } = this.itemsToPoll[t8];
o();
}
this.itemsToPoll = this.itemsToPoll.slice(e + 1);
}
addItemToPoll(e, t8) {
if (this.itemsToPoll.push({ isDoneFn: e, resolveFn: t8 }), this.itemsToPoll.length > 1)
return;
let o;
"setTimeoutCustom" in A().platform && (o = A().platform.setTimeoutCustom.bind(A().platform)), y.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0), () => 0, null, o);
}
bindTextureToFrameBuffer(e) {
this.throwIfDisposed(), jl(this.gl, e, this.framebuffer), this.debug && Tc(this.gl);
}
unbindTextureToFrameBuffer() {
this.outputTexture != null ? (jl(this.gl, this.outputTexture, this.framebuffer), this.debug && Tc(this.gl)) : qf(this.gl, this.framebuffer);
}
downloadMatrixDriver(e, t8) {
this.bindTextureToFrameBuffer(e);
let o = t8();
return this.unbindTextureToFrameBuffer(), o;
}
setOutputMatrixTextureDriver(e, t8, o) {
this.throwIfDisposed();
let n = this.gl;
jl(n, e, this.framebuffer), this.debug && Tc(n), this.outputTexture = e, ce(n, () => n.viewport(0, 0, t8, o)), ce(n, () => n.scissor(0, 0, t8, o));
}
setOutputMatrixWriteRegionDriver(e, t8, o, n) {
this.throwIfDisposed(), ce(this.gl, () => this.gl.scissor(e, t8, 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 RZ(r) {
let e = 0;
for (; e < r.length && r[e](); ++e)
;
return e - 1;
}
var { addImpl: IR, bincountImpl: ih, bincountReduceImpl: vR, bitwiseAndImpl: kR, castImpl: NR, ceilImpl: TR, concatImpl: _R, equalImpl: $R, expImpl: ER, expm1Impl: RR, floorImpl: DR, gatherNdImpl: AR, gatherV2Impl: FR, greaterImpl: PR, greaterEqualImpl: OR, lessImpl: MR, lessEqualImpl: LR, linSpaceImpl: BR, logImpl: zR, maxImpl: VR, maximumImpl: WR, minimumImpl: UR, multiplyImpl: GR, negImpl: HR, notEqualImpl: KR, prodImpl: qR, raggedGatherImpl: jR, raggedRangeImpl: XR, raggedTensorToTensorImpl: YR, rangeImpl: QR, rsqrtImpl: ZR, scatterImpl: JR, sigmoidImpl: eD, simpleAbsImpl: uh, sliceImpl: tD, sparseFillEmptyRowsImpl: rD, sparseReshapeImpl: oD, sparseSegmentReductionImpl: ph, sqrtImpl: nD, staticRegexReplaceImpl: sD, stridedSliceImpl: aD, stringNGramsImpl: iD, stringSplitImpl: uD, stringToHashBucketFastImpl: pD, subImpl: cD, tileImpl: lD, topKImpl: mD, transposeImpl: Sp, uniqueImpl: dD } = Ic;
function ov(r, e) {
return ["x", "y", "z", "w", "u", "v"].slice(0, e).map((t8) => `${r}.${t8}`);
}
function Rt(r, e) {
return e === 1 ? [r] : ov(r, e);
}
function fD(r, e) {
if (r === 1)
return "rc";
let t8 = "";
for (let o = 0; o < r; o++)
t8 += e[o], o < r - 1 && (t8 += ",");
return t8;
}
var ch = class {
constructor(e) {
if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = pt(this.outputShape.length), this.rank === 0)
this.userCode = `
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;
else {
let t8 = Rt("rc", this.rank), o = Re(this.rank), n = this.getOutOfBoundsCondition(t8), s = this.getSetup(t8), a = this.getOutput(t8);
this.userCode = `
void main() {
${o} rc = getOutputCoords();
if(${n}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${a}));
}
}
`;
}
}
getSourceCoordsArr(e) {
let t8 = [];
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;
t8.push(s);
}
return t8;
}
getOutOfBoundsCondition(e) {
if (this.rank === 1)
return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`;
let t8 = "";
for (let o = this.rank - 2; o < this.rank; o++)
t8 += `${e[o]} >= ${this.enableShapeUniforms ? `outShape[${o}]` : this.outputShape[o]}`, o < this.rank - 1 && (t8 += "||");
return t8;
}
getSetup(e) {
if (this.rank === 1)
return "";
let t8 = 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 = ${t8[0]};
int c = ${t8[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${o};
bool rEdge = rp1 >= ${n};
`;
}
getOutput(e) {
let t8 = this.getSourceCoordsArr(e);
return this.rank === 1 ? `getA(rc), (rc + 1 >= ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0` : `getA(${t8[0]}),
cEdge ? 0. : getA(${t8[1]}),
rEdge ? 0. : getA(${t8[2]}),
rEdge || cEdge ? 0. : getA(${t8[3]})`;
}
};
var Mc = class {
constructor(e, t8) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = pt(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 = `
${DZ(t8, this.enableShapeUniforms)}
${this.enableShapeUniforms ? Rc() : Ec(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 DZ(r, e) {
return `
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${e ? fR(["r", "c", "d"], "inputShape") : zs(["r", "c", "d"], r)}
return ivec3(r, c, d);
}
`;
}
var lh = 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, t8, o) {
let n = gD(t8, o), s = xD(e, n, o);
s in this.freeTextures || (this.freeTextures[s] = []), s in this.usedTextures || (this.usedTextures[s] = []);
let a = hD(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 === tr.PACKED_2X2_FLOAT32 ? i = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : n === tr.PACKED_2X2_FLOAT16 ? i = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : n === tr.UNPACKED_FLOAT32 ? i = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : n === tr.UNPACKED_FLOAT16 ? i = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : n === tr.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, t8, o, n) {
if (this.freeTextures == null)
return;
let s = gD(o, n), a = xD(t8, s, n);
a in this.freeTextures || (this.freeTextures[a] = []);
let i = hD(t8, s, this.gpgpu.gl, this.gpgpu.textureConfig, n), p = A().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 t8 = this._numBytesFree / this._numBytesAllocated;
console.log(`Bytes allocated: ${this._numBytesAllocated}`), console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * t8)}%)`);
}
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((t8) => {
this.gpgpu.deleteMatrixTexture(t8.texture);
});
for (let e in this.usedTextures)
this.usedTextures[e].forEach((t8) => {
this.gpgpu.deleteMatrixTexture(t8.texture);
});
this.freeTextures = null, this.usedTextures = null, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0;
}
}
};
function AZ(r, e) {
let t8 = r;
if (e === t8.R32F)
return 4;
if (e === t8.R16F)
return 2;
if (e === t8.RGBA32F)
return 16;
if (e === r.RGBA)
return 16;
if (e === t8.RGBA16F)
return 8;
if (e === t8.RGBA8)
return 4;
throw new Error(`Unknown internal format ${e}`);
}
function hD(r, e, t8, o, n) {
let s = FZ(e, o), a;
if (n) {
let [p, u] = Pa(r[0], r[1]);
a = p * u;
} else {
let [p, u] = yp(r[0], r[1]);
a = p * u;
}
let i = AZ(t8, s);
return a * i;
}
function FZ(r, e) {
switch (r) {
case tr.PACKED_2X2_FLOAT32:
return sh(e);
case tr.PACKED_2X2_FLOAT16:
return ah(e);
case tr.UNPACKED_FLOAT32:
return rh(e);
case tr.UNPACKED_FLOAT16:
return oh(e);
case tr.PACKED_4X1_UNSIGNED_BYTE:
return nh(e);
default:
throw new Error(`Unknown physical texture type ${r}`);
}
}
function PZ(r) {
return A().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? r ? tr.PACKED_2X2_FLOAT32 : tr.UNPACKED_FLOAT32 : r ? tr.PACKED_2X2_FLOAT16 : tr.UNPACKED_FLOAT16;
}
function gD(r, e) {
if (r === mr.UPLOAD)
return tr.PACKED_2X2_FLOAT32;
if (r === mr.RENDER || r == null)
return PZ(e);
if (r === mr.DOWNLOAD || r === mr.PIXELS)
return tr.PACKED_4X1_UNSIGNED_BYTE;
throw new Error(`Unknown logical texture type ${r}`);
}
function xD(r, e, t8) {
return `${r[0]}_${r[1]}_${e}_${t8}`;
}
var rr = class {
constructor(e, t8) {
this.variableNames = ["A"], this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = `
float unaryOperation(float x) {
${t8}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`;
}
};
var Ut = "if (isnan(x)) return x;";
var yD = "return x;";
var nv = "return abs(x);";
var bD = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var CD = Ut + `
return (x < 0.0) ? 0.0 : x;
`;
var wD = Ut + `
return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var Oa = "return x;";
var SD = "return 1.0 / (1.0 + exp(-1.0 * x));";
var vD = "return x;";
var kD = `
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 ND = `
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 TD = `
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 _D = "return 1.0 / (1.0 + exp(-1.0 * x));";
var Fr = class {
constructor(e, t8) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = `
vec4 unaryOperation(vec4 x) {
${t8}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`;
}
};
var mh = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = pt(this.outputShape.length);
let t8 = e.length, o = Rt("rc", t8), n = Re(t8), s = fD(t8, o), a = o.slice(-2), i = t8 <= 1 ? "rc" : `vec2(${a.join(",")})`;
this.userCode = `
void main() {
${n} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${i}));
}
`;
}
};
var MZ = Wt.whereImpl;
var LZ = 1e-7;
var BZ = 1e-4;
var dh = {};
function zZ(r) {
return r in dh || (dh[r] = {}), dh[r];
}
var VZ = A().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
var WZ = 600;
function UZ() {
return A().global.screen == null ? 1024 : A().global.screen.height * A().global.screen.width * window.devicePixelRatio * WZ / 1024 / 1024;
}
var bu = class extends so {
nextDataId() {
return bu.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, !A().getBool("HAS_WEBGL"))
throw new Error("WebGL is not supported on this device");
let t8;
if (e != null) {
if (e instanceof wp)
t8 = e;
else {
let o = Kr(A().getNumber("WEBGL_VERSION"), e);
t8 = new wp(o);
}
this.binaryCache = {}, this.gpgpuCreatedLocally = false;
} else {
let o = Kr(A().getNumber("WEBGL_VERSION"));
t8 = new wp(o), this.binaryCache = zZ(A().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
}
this.gpgpu = t8, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new lh(this.gpgpu), this.numMBBeforeWarning = UZ(), this.texData = new Bo(this, ur());
}
numDataIds() {
return this.texData.numDataIds() - this.pendingDeletes;
}
writeTexture(e, t8, o, n, s, a) {
let i = this.makeTensorInfo(t8, o), p = this.texData.get(i.dataId);
p.isPacked = false, p.texture = { texture: e, texShape: [n, s] }, p.texShape = [n, s];
let u = _c(t8), c = new Xl(u, false, a), l = this.runWebGLProgram(c, [i], o, [[n, s]]);
return l.shape = t8, p.texture = null, this.disposeIntermediateTensorInfo(i), l.dataId;
}
write(e, t8, o) {
if ((A().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || A().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: t8, 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 t8 = this.texData.get(e);
t8.refCount++;
}
decRef(e) {
if (this.texData.has(e)) {
let t8 = this.texData.get(e);
t8.refCount--;
}
}
move(e, t8, o, n, s) {
if (A().getBool("DEBUG") && this.checkNumericalProblems(t8), 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: t8, usage: mr.UPLOAD, refCount: s });
}
disposeIntermediateTensorInfo(e) {
this.disposeData(e.dataId);
}
readSync(e) {
let t8 = this.texData.get(e), { values: o, dtype: n, complexTensorInfos: s, slice: a, shape: i, isPacked: p } = t8;
if (a != null) {
let m;
p ? m = new Fr(i, Oa) : m = new rr(i, Oa);
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 = w.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 t8 = this.texData.get(e), { values: o, shape: n, slice: s, dtype: a, complexTensorInfos: i, isPacked: p } = t8;
if (s != null) {
let f;
p ? f = new Fr(n, Oa) : f = new rr(n, Oa);
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 (A().getBool("DEBUG") && !A().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && A().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" && A().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 = w.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, t8 = {}) {
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 Fr(s, Oa) : d = new rr(s, Oa);
let f = this.runWebGLProgram(d, [{ dataId: e, shape: s, dtype: i }], i), h = this.readToGPU(f, t8);
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, t8.customTexShape), l = ur().makeTensorFromTensorInfo(c), m = this.texData.get(c.dataId);
return Object.assign({ tensorRef: l }, m.texture);
}
bufferSync(e) {
let t8 = this.readSync(e.dataId);
if (e.dtype === "string")
try {
let o = t8.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, t8);
}
checkNumericalProblems(e) {
if (e != null)
for (let t8 = 0; t8 < e.length; t8++) {
let o = e[t8];
if (!bI(o))
throw A().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 ${o} cannot be represented on this device.`);
}
}
getValuesFromTexture(e) {
let { shape: t8, dtype: o, isPacked: n } = this.texData.get(e), s = y.sizeFromShape(t8);
if (A().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
let m = this.decode(e), d = this.texData.get(m.dataId), f = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Hl(t8)).subarray(0, s);
return this.disposeIntermediateTensorInfo(m), f;
}
let a = A().getBool("WEBGL_PACK") && n === true, i = a ? _c(t8) : t8, p = a ? new eh(i) : new Jf(i), u = this.runWebGLProgram(p, [{ shape: i, dtype: o, dataId: e }], "float32"), c = this.texData.get(u.dataId), l = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture, c.texShape[0], c.texShape[1]).subarray(0, s);
return this.disposeIntermediateTensorInfo(u), l;
}
timerAvailable() {
return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
}
time(e) {
let t8 = this.activeTimers, o = [], n = false;
this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, e();
let s = y.flatten(this.activeTimers.map((p) => p.query)).filter((p) => p != null), a = y.flatten(this.activeTimers.map((p) => p.name)).filter((p) => p != null);
this.activeTimers = t8, n && (this.programTimersStack = null);
let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null };
return (async () => {
if (A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
let p = await Promise.all(s);
i.kernelMs = y.sum(p), i.getExtraProfileInfo = () => p.map((u, c) => ({ name: a[c], ms: u })).map((u) => `${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 A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? this.gpgpu.beginQuery() : { startMs: y.now(), endMs: null };
}
endTimer(e) {
return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? (this.gpgpu.endQuery(), e) : (e.endMs = y.now(), e);
}
async getQueryTime(e) {
if (A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0)
return this.gpgpu.waitForQueryAndGetTime(e);
let t8 = e;
return t8.endMs - t8.startMs;
}
disposeData(e, t8 = false) {
if (this.pendingDisposal.has(e))
return false;
if (!this.texData.has(e))
return true;
if (t8 ? this.texData.get(e).refCount = 0 : this.texData.get(e).refCount--, !t8 && 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, t8), this.disposeData(o.imag.dataId, t8)), this.texData.delete(e), true;
}
releaseGPUData(e) {
let { texture: t8, 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), t8 != null && (this.numBytesInGPU -= this.computeBytes(n, o), this.textureManager.releaseTexture(t8, 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, t8 = VZ) {
return A().getBool("WEBGL_CPU_FORWARD") && e.every((o) => this.texData.get(o.dataId).texture == null && y.sizeFromShape(o.shape) < t8);
}
getGPGPUContext() {
return this.gpgpu;
}
where(e) {
w.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
let t8 = e.dataSync();
return MZ(e.shape, t8);
}
packedUnaryOp(e, t8, o) {
let n = new Fr(e.shape, t8), s = this.compileAndRun(n, [e], o);
return ur().makeTensorFromTensorInfo(s);
}
abs(e) {
if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") {
let n = uh(this.texData.get(e.dataId).values);
return this.makeOutput(e.shape, e.dtype, n);
}
if (A().getBool("WEBGL_PACK_UNARY_OPERATIONS"))
return this.packedUnaryOp(e, nv, e.dtype);
let t8 = new rr(e.shape, nv), o = this.compileAndRun(t8, [e]);
return ur().makeTensorFromTensorInfo(o);
}
makeTensorInfo(e, t8, o) {
let n;
if (t8 === "string" && o != null && o.length > 0 && y.isString(o[0])) {
let s = o.map((a) => y.encodeString(a));
n = this.write(s, e, t8);
} else
n = this.write(o, e, t8);
return this.texData.get(n).usage = null, { dataId: n, shape: e, dtype: t8 };
}
makeOutput(e, t8, o) {
return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e, t8, o), this);
}
unpackTensor(e) {
let t8 = new mh(e.shape);
return this.runWebGLProgram(t8, [e], e.dtype);
}
packTensor(e) {
let t8 = new ch(e.shape), o = true;
return this.runWebGLProgram(t8, [e], e.dtype, null, o);
}
packedReshape(e, t8) {
let o = [gi(e.shape), ...xi(e.shape)], n = { dtype: e.dtype, shape: o, dataId: e.dataId }, s = [gi(t8), ...xi(t8)], a = new Mc(s, o), i = true, p = [o], u = this.runWebGLProgram(a, [n], e.dtype, p, i);
return { dataId: u.dataId, shape: t8, dtype: u.dtype };
}
decode(e, t8) {
let o = this.texData.get(e), { isPacked: n, shape: s, dtype: a } = o;
if (t8 != null) {
let m = y.sizeFromShape(s), d = t8[0] * t8[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 = _c(s), p;
n ? p = new Zf(i) : p = new Qf(i);
let u = true, c = [t8 != null ? t8 : Hl(i)], l = this.runWebGLProgram(p, [{ shape: i, dtype: a, dataId: e }], a, c, u, t8);
return { dtype: a, shape: s, dataId: l.dataId };
}
runWebGLProgram(e, t8, 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 === xu.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 = t8.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) <= A().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 && !yu(b.shape, x.shape)) {
let C = x, S = x.shape;
x.shape = b.shape, x = this.packedReshape(x, S), u.push(x), b = this.texData.get(x.dataId), C.shape = S;
}
return { shape: x.shape, texData: b, isUniform: false };
});
this.uploadToGPU(i.dataId);
let l = { shape: i.shape, texData: p, isUniform: false }, m = SR(e, c, l), d = this.getAndSaveBinary(m, () => CR(this.gpgpu, e, c, l)), f = this.activeTimers != null, h;
f && (h = this.startTimer()), A().get("ENGINE_COMPILE_ONLY") || wR(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 = A().get("WEBGL_FLUSH_THRESHOLD");
if (g > 0) {
let x = y.now();
x - this.lastGlFlushTime > g && (this.gpgpu.gl.flush(), this.lastGlFlushTime = x);
}
if (!A().getBool("WEBGL_LAZILY_UNPACK") && p.isPacked && s === false) {
let x = this.unpackTensor(i);
return this.disposeIntermediateTensorInfo(i), x;
}
return i;
}
compileAndRun(e, t8, o, n, s = false) {
return o = o || t8[0].dtype, this.runWebGLProgram(e, t8, o, n, s);
}
getAndSaveBinary(e, t8) {
return e in this.binaryCache || (this.binaryCache[e] = t8()), this.binaryCache[e];
}
getTextureManager() {
return this.textureManager;
}
dispose() {
this.disposed || (A().getBool("IS_TEST") || Object.keys(this.binaryCache).forEach((t8) => {
this.gpgpu.deleteProgram(this.binaryCache[t8].webGLProgram), delete this.binaryCache[t8];
}), 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 (!A().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
let e = A().getBool("DEBUG");
A().set("DEBUG", false);
let t8 = this.abs(ke(1e-8)).dataSync()[0];
if (A().set("DEBUG", e), t8 > 0)
return 32;
}
return 16;
})), this.floatPrecisionValue;
}
epsilon() {
return this.floatPrecision() === 32 ? LZ : BZ;
}
uploadToGPU(e) {
let t8 = this.texData.get(e), { shape: o, dtype: n, values: s, texture: a, usage: i, isPacked: p } = t8;
if (a != null)
return;
let u = this.activeTimers != null, c;
u && (c = y.now());
let l = t8.texShape;
if (l == null && (l = DI(o, p), t8.texShape = l), s != null) {
let m = _c(o), d, f = l[1], h = l[0], g = s instanceof Uint8Array || s instanceof Uint8ClampedArray;
(p || !g) && ([f, h] = Pa(l[0], l[1])), p ? d = new th(m, g) : d = new Xl(m, g);
let x = g ? [h, f] : l, b = this.makeTensorInfo(x, n), C = this.texData.get(b.dataId);
g ? C.usage = mr.PIXELS : C.usage = mr.UPLOAD, C.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);
t8.texShape = E.texShape, t8.isPacked = E.isPacked, t8.usage = E.usage, A().get("ENGINE_COMPILE_ONLY") ? this.disposeData(_.dataId) : (t8.texture = E.texture, t8.values = null, this.texData.delete(_.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += y.now() - c);
} else {
let m = this.acquireTexture(l, i, n, p);
t8.texture = m;
}
}
convertAndCacheOnCPU(e, t8) {
let o = this.texData.get(e), { dtype: n } = o;
return t8 != null && (o.values = GZ(t8, n)), o.values;
}
acquireTexture(e, t8, 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, t8, n);
}
computeBytes(e, t8) {
return e[0] * e[1] * y.bytesPerElement(t8);
}
checkCompileCompletion() {
for (let [, e] of Object.entries(this.binaryCache))
this.checkCompletion_(e);
}
async checkCompileCompletionAsync() {
let e = [];
if (this.gpgpu.parallelCompilationExtension) {
for (let [, t8] of Object.entries(this.binaryCache))
e.push(this.checkCompletionAsync_(t8));
return Promise.all(e);
} else {
for (let [, t8] of Object.entries(this.binaryCache)) {
let o = new Promise((n) => {
try {
this.checkCompletion_(t8), 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 tS(), 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 ? (Hf(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: t8, customUniformLocations: o, infLoc: n, nanLoc: s, outShapeLocation: a, outShapeStridesLocation: i, outTexShapeLocation: p } = BI(this.gpgpu, e.program, e.webGLProgram);
e.variablesLocations = t8, e.customUniformLocations = o, e.infLoc = n, e.nanLoc = s, e.outShapeLocation = a, e.outShapeStridesLocation = i, e.outTexShapeLocation = p;
}
}
createTensorFromGPUData(e, t8, 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, t8, o, s, a, i);
return ur().makeTensorFromDataId(u, t8, o, p);
}
};
bu.nextDataId = 0;
function GZ(r, e) {
if (e === "float32" || e === "complex64")
return r;
if (e === "int32" || e === "bool") {
let t8 = e === "int32" ? new Int32Array(r.length) : new Uint8Array(r.length);
for (let o = 0; o < t8.length; ++o)
t8[o] = Math.round(r[o]);
return t8;
} else
throw new Error(`Unknown dtype ${e}`);
}
var HZ = "4.7.0";
function $D() {
A().set("WEBGL_FORCE_F16_TEXTURES", true);
}
ru.isBrowser() && nu("webgl", () => new bu(), 2);
var tat = { forceHalfFloat: $D };
var Lc = `
if (isnan(a)) return a;
if (isnan(b)) return b;
`;
var Pr = class {
constructor(e, t8, o) {
this.variableNames = ["A", "B"], this.outputShape = w.assertAndGetBroadcastShape(t8, o), this.enableShapeUniforms = pt(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 Xr = `
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 jr = class {
constructor(e, t8, o, n = false) {
this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = w.assertAndGetBroadcastShape(t8, o);
let s = this.outputShape.length;
this.enableShapeUniforms = pt(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: t8 } = r, { x: o } = e;
return t8.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
var ED = { kernelName: Co, backendName: "webgl", kernelFunc: Dt };
function Or(r) {
let { inputs: e, backend: t8 } = r, { real: o, imag: n } = e, s = t8.makeTensorInfo(o.shape, "complex64"), a = t8.texData.get(s.dataId), i = Dt({ inputs: { x: o }, backend: t8 }), p = Dt({ inputs: { x: n }, backend: t8 });
return a.complexTensorInfos = { real: i, imag: p }, s;
}
var RD = { kernelName: Ri, backendName: "webgl", kernelFunc: Or };
var sv = "return (a < 0.) ? b * a : a;";
var av = `
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function KZ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = t8.makeTensorInfo([], "float32", y.createScalarValue(s, "float32")), i = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new jr(av, n.shape, a.shape) : new Pr(sv, n.shape, a.shape), p = t8.runWebGLProgram(i, [n, a], "float32");
return t8.disposeIntermediateTensorInfo(a), p;
}
var DD = { kernelName: En, backendName: "webgl", kernelFunc: KZ };
var iv = "return (a < 0.) ? b * a : a;";
var uv = `
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function qZ(r) {
let { inputs: e, backend: t8 } = r, { x: o, alpha: n } = e, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new jr(uv, o.shape, n.shape) : new Pr(iv, o.shape, n.shape);
return t8.runWebGLProgram(s, [o, n], "float32");
}
var AD = { kernelName: rs, backendName: "webgl", kernelFunc: qZ };
var Fo = "if (isnan(x)) return x;";
function xe({ opSnippet: r, packedOpSnippet: e, cpuKernelImpl: t8, dtype: o }) {
return ({ inputs: n, backend: s }) => {
let { x: a } = n, i = s, p = o || a.dtype;
if (i.shouldExecuteOnCPU([a]) && t8 != null) {
let l = i.texData.get(a.dataId), m = t8(l.values, p);
return i.makeTensorInfo(a.shape, p, m);
}
let u = A().getBool("WEBGL_PACK_UNARY_OPERATIONS") && e != null, c;
return u ? c = new Fr(a.shape, e) : c = new rr(a.shape, r), i.runWebGLProgram(c, [a], p);
};
}
function nt({ opSnippet: r, packedOpSnippet: e, checkOutOfBounds: t8 = 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((C) => {
let [S, k] = C, _ = { dataId: S.dataId, dtype: S.dtype, shape: p.shape }, E = { dataId: k.dataId, dtype: k.dtype, shape: u.shape }, R = new Pr(r, p.shape, u.shape);
return c.runWebGLProgram(R, [_, E], dt(S.dtype, k.dtype));
}), b = Or({ 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" ? w.fromUint8ToStringArray(f) : f, x = p.dtype === "string" ? w.fromUint8ToStringArray(h) : h, [b, C] = n(p.shape, u.shape, g, x, l), S = c.makeTensorInfo(C, l), k = c.texData.get(S.dataId);
return k.values = b, S;
}
let m = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") && e != null, d;
return m ? d = new jr(e, p.shape, u.shape, t8) : d = new Pr(r, p.shape, u.shape), c.runWebGLProgram(d, [p, u], l);
};
}
function yi(r, e = false) {
if (r === "linear")
return e ? vD : yD;
if (r === "relu")
return e ? ND : CD;
if (r === "elu")
return e ? kD : bD;
if (r === "relu6")
return e ? TD : wD;
if (r === "prelu")
return e ? uv : iv;
if (r === "leakyrelu")
return e ? av : sv;
if (r === "sigmoid")
return e ? _D : SD;
throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`);
}
var Bc = class {
constructor(e, t8, 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 = pt(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 C = "rc.x", S = "rc.x";
e[0] < t8[0] ? C = `imod(rc.x, ${e[0]})` : t8[0] < e[0] && (S = `imod(rc.x, ${t8[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 = ${C};
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 pv = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
var Ql = class {
constructor(e, t8, o) {
this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = w.assertAndGetBroadcastShape(t8, 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 FD = "return a * b;";
function Zl(r) {
let { inputs: e, backend: t8 } = r, { a: o, b: n } = e, s = w.upcastType(o.dtype, n.dtype);
if (o.dtype === "complex64") {
let i = t8.texData.get(o.dataId), p = t8.texData.get(n.dataId), u = new Ql(pv.REAL, o.shape, n.shape), c = new Ql(pv.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 = t8.runWebGLProgram(u, l, "float32"), d = t8.runWebGLProgram(c, l, "float32"), f = Or({ inputs: { real: m, imag: d }, backend: t8 });
return t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d), f;
}
if (t8.shouldExecuteOnCPU([o, n])) {
let i = t8.texData.get(o.dataId), p = t8.texData.get(n.dataId), [u, c] = GR(o.shape, n.shape, i.values, p.values, s), l = t8.makeTensorInfo(c, s), m = t8.texData.get(l.dataId);
return m.values = u, l;
}
let a;
return A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? a = new jr(FD, o.shape, n.shape) : a = new Pr(FD, o.shape, n.shape), t8.runWebGLProgram(a, [o, n], s);
}
var PD = { kernelName: Xn, backendName: "webgl", kernelFunc: Zl };
function OD(r, e, t8) {
let o = [gi(r.shape), ...xi(r.shape)], n = { dtype: r.dtype, shape: o, dataId: r.dataId }, s = [gi(e), ...xi(e)], a = new Mc(s, o), i = true, p = [o], u = t8.runWebGLProgram(a, [n], r.dtype, p, i);
return { dataId: u.dataId, shape: e, dtype: u.dtype };
}
function te(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { shape: s } = o, a = t8, 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 && !yu(n.shape, p) && !(c.texture !== null && yu(c.shape, p)) ? OD(n, p, a) : (a.incRef(n.dataId), { dataId: n.dataId, shape: p, dtype: n.dtype });
}
var MD = { kernelName: ma, backendName: "webgl", kernelFunc: te };
var Jl = class {
constructor(e, t8) {
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 (t8 != null) {
let l = 1 / t8;
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);
${u}
}
setOutput(sumValue);
}
`;
}
};
var fh = class {
constructor(e, t8) {
this.variableNames = ["x"];
let { windowSize: o, batchSize: n, inSize: s, outSize: a } = e;
this.outputShape = [n, a];
let i = "0.0", p = "";
t8 === "prod" ? i = "1.0" : t8 === "min" ? (i = "1.0 / 1e-20", p = "min") : t8 === "max" && (i = "-1.0 / 1e-20", p = "max");
let u = `${t8}(${t8}(${t8}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
t8 === "sum" ? u = "sumValue" : t8 === "prod" ? u = "prodValue" : t8 === "all" ? u = "allValue" : t8 === "any" && (u = "anyValue");
let c = Math.floor(o / 4) * 4, l = o % 4, m = `
if (${t8 === "sum"}) {
sumValue += dot(values, ones);
} else if (${t8 === "prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${p}(values, minMaxValue);
if (${t8 === "min"} || ${t8 === "max"}) {
minMaxValue = ${p}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`, d = "vec4";
t8 === "all" ? (i = "1.0", m = `
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`, d = "bvec4") : t8 === "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 XZ(r) {
let e = [];
for (; e.length === 0 || e[e.length - 1].outSize !== 1; ) {
let t8 = e.length ? e[e.length - 1].outSize : r[1], o = w.computeOptimalWindowSize(t8);
e.push({ inSize: t8, windowSize: o, outSize: Math.ceil(t8 / o) });
}
return e;
}
function Yr(r, e, t8, o) {
let n = XZ(r.shape), s = r;
for (let a = 0; a < n.length; a++) {
let { inSize: i, windowSize: p, outSize: u } = n[a], c, l;
t8 === "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 fh({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, t8), l = s, s = o.runWebGLProgram(c, [s], e), l.dataId !== r.dataId && o.disposeIntermediateTensorInfo(l);
}
return s;
}
var hh = class {
constructor(e, t8) {
this.variableNames = ["A"];
let o = new Array(e.length);
for (let a = 0; a < o.length; a++)
o[a] = e[t8[a]];
this.outputShape = o, this.rank = o.length;
let n = Re(this.rank), s = YZ(t8);
this.userCode = `
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`;
}
};
function YZ(r) {
let e = r.length;
if (e > 6)
throw Error(`Transpose for rank ${e} is not yet supported`);
let t8 = ["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]] = t8[n];
return o.join();
}
var gh = class {
constructor(e, t8) {
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[t8[c]];
if (this.outputShape = o, this.rank = o.length, this.rank > 6)
throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
let n = Re(this.rank), s = ov("rc", this.rank), a = new Array(this.rank);
for (let c = 0; c < t8.length; c++)
a[t8[c]] = s[c];
let i = `vec2(${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 Cu(r, e, t8) {
let o = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new gh(r.shape, e) : new hh(r.shape, e);
return t8.runWebGLProgram(o, [r], r.dtype);
}
function LD(r, e, t8, o) {
let n = e, s = r.shape.length, a = y.parseAxisParam(n, r.shape), i = a, p = w.getAxesPermutation(i, s), u = p != null, c = r;
u && (c = Cu(r, p, o), i = w.getInnerMostAxes(i.length, s)), w.assertAxesAreInnerMostDims("sum", i, s);
let [l, m] = w.computeOutAndReduceShapes(c.shape, i), d = l;
t8 && (d = w.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 = ti(r.dtype), C = Yr(x, b, "sum", o), S = te({ inputs: { x: C }, attrs: { shape: d }, backend: o });
return o.disposeIntermediateTensorInfo(x), o.disposeIntermediateTensorInfo(C), u && o.disposeIntermediateTensorInfo(c), S;
}
function Ip(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
return LD(n, s, a, t8);
}
var BD = { kernelName: Ss, backendName: "webgl", kernelFunc: Ip };
function Ct(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t8, 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 = Sp(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 = Cu(n, s, a);
return u;
}
var zD = { kernelName: po, backendName: "webgl", kernelFunc: Ct };
var cv = 1e3;
function vp({ a: r, b: e, transposeA: t8, 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 = t8 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t8 ? 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 = Ir.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 ${e.shape} and transposeA=${t8} and transposeB=${o} must match.`);
let k = t8 ? [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], P = Math.max(x, b), O = t8 ? E.shape[1] : E.shape[2], M = s != null, L = a != null, B = p === "leakyrelu", z = p != null ? yi(p, true) : null, U = M || L || B || z != null, j;
if ((d === 1 || f === 1) && O > cv && U === false) {
let Y = E, J = R;
t8 && (Y = Ct({ inputs: { x: E }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(Y)), o && (J = Ct({ inputs: { x: R }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(J));
let re = f !== 1, ne = f === 1, ee = Y;
re && (ee = te({ inputs: { x: Y }, backend: n, attrs: { shape: [P, O, 1] } }), D.push(ee));
let oe = f === 1 ? 2 : 1, ie = J;
ne && (ie = te({ inputs: { x: J }, backend: n, attrs: { shape: [P, 1, O] } }), D.push(ie));
let le = Zl({ inputs: { a: ee, b: ie }, backend: n });
j = Ip({ inputs: { x: le }, backend: n, attrs: { axis: oe, keepDims: true } }), D.push(le);
} else {
let Y = dt(r.dtype, e.dtype), J = new Bc(k, _, [P, d, f], t8, 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, Y);
}
let q = te({ inputs: { x: j }, backend: n, attrs: { shape: S } });
D.push(j);
for (let Y of D)
n.disposeIntermediateTensorInfo(Y);
return q;
}
function QZ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o;
return vp({ a: n, b: s, transposeA: p, transposeB: u, backend: t8, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c });
}
var VD = { kernelName: So, backendName: "webgl", kernelFunc: QZ };
var WD = "return abs(x);";
function ZZ(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
if (t8.shouldExecuteOnCPU([o]) && o.dtype !== "complex64") {
let s = t8.texData.get(o.dataId), a = uh(s.values);
return t8.makeTensorInfo(o.shape, o.dtype, a);
}
let n;
return A().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Fr(o.shape, WD) : n = new rr(o.shape, WD), t8.runWebGLProgram(n, [o], o.dtype);
}
var UD = { kernelName: js, backendName: "webgl", kernelFunc: ZZ };
var JZ = Ut + `
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`;
var e9 = xe({ opSnippet: JZ });
var GD = { kernelName: Vo, backendName: "webgl", kernelFunc: e9 };
var t9 = Ut + `
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`;
var r9 = xe({ opSnippet: t9 });
var HD = { kernelName: Wo, backendName: "webgl", kernelFunc: r9 };
var KD = "return a + b;";
var o9 = nt({ opSnippet: KD, packedOpSnippet: KD, supportsComplex: true, cpuKernelImpl: IR });
var qD = { kernelName: io, backendName: "webgl", kernelFunc: o9 };
var xh = class {
constructor(e, t8) {
this.outputShape = [], this.outputShape = e, this.variableNames = t8.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 = ${n};
setOutput(result);
}
`;
}
};
var yh = class {
constructor(e, t8) {
this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t8.map((s, a) => `T${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 bh(r) {
let { inputs: e, backend: t8 } = r, o = e;
if (o.length === 1)
return Dt({ inputs: { x: o[0] }, backend: t8 });
if (o.length > A().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
let p = Math.floor(o.length / 2), u = bh({ inputs: o.slice(0, p), backend: t8 }), c = bh({ inputs: o.slice(p), backend: t8 });
return bh({ inputs: [u, c], backend: t8 });
}
let n = o.map((p) => p.dtype).reduce((p, u) => dt(p, u)), s = o.map((p) => p.shape), i = A().getBool("WEBGL_PACK") ? new yh(o[0].shape, s) : new xh(o[0].shape, s);
return t8.runWebGLProgram(i, o, n);
}
var jD = { kernelName: Uo, backendName: "webgl", kernelFunc: bh };
function n9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n;
c != null && (l = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, i)), w.assertAxesAreInnerMostDims("all", u, i);
let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t8, attrs: { shape: [-1, f] } }), g = Yr(h, h.dtype, "all", t8), x;
if (a) {
let b = w.expandShapeToKeepDim(m, p);
x = te({ inputs: { x: g }, backend: t8, attrs: { shape: b } });
} else
x = te({ inputs: { x: g }, backend: t8, attrs: { shape: m } });
return t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(g), c != null && t8.disposeIntermediateTensorInfo(l), x;
}
var XD = { kernelName: Go, backendName: "webgl", kernelFunc: n9 };
function s9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n;
c != null && (l = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, i)), w.assertAxesAreInnerMostDims("any", u, i);
let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t8, attrs: { shape: [-1, f] } }), g = Yr(h, h.dtype, "any", t8), x;
if (a) {
let b = w.expandShapeToKeepDim(m, p);
x = te({ inputs: { x: g }, backend: t8, attrs: { shape: b } });
} else
x = te({ inputs: { x: g }, backend: t8, attrs: { shape: m } });
return t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(g), c != null && t8.disposeIntermediateTensorInfo(l), x;
}
var YD = { kernelName: Ho, backendName: "webgl", kernelFunc: s9 };
var Ch = class {
constructor(e, t8, o) {
this.variableNames = ["A"];
let { windowSize: n, batchSize: s, outSize: a } = e;
o || this.variableNames.push("bestIndicesA"), this.outputShape = [s, a];
let i = t8 === "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 ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`;
}
};
var wh = class {
constructor(e, t8, o, n) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, y.assert(e.length > 2, () => `Packed arg${o.charAt(0).toUpperCase() + o.slice(1)} supports only inputs with rank above 2.`);
let s = e[e.length - 1], a = Math.ceil(s / t8);
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"), C = Rt("sourceLocA", m - 1).concat("inIdx.a"), S = o === "max" ? "greaterThan" : "lessThan", k = n ? "" : `
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${x.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${C.join()})));`, _ = `vec4(
getAChannel(${g.join()}),
hasNextCol ? getAChannel(${x.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${C.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}) * ${t8};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${_};
for (int i = 0; i < ${t8}; 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 QD(r, e, t8, o = null) {
let n = e.shape[0], s = e.shape[1];
o != null && (n = o.shape[0], s = o.shape[1]);
let a = w.computeOptimalWindowSize(s), i = { windowSize: a, inSize: s, batchSize: n, outSize: Math.ceil(s / a) }, p = new Ch(i, t8, 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 = QD(r, e, t8, c);
return r.disposeIntermediateTensorInfo(c), l;
}
function ZD(r, e, t8, o = null) {
let n = o != null ? o.shape : e.shape, s = n[n.length - 1], a = w.computeOptimalWindowSize(s), i = new wh(n, a, t8, o == null), p = o == null ? [e] : [e, o], u = r.runWebGLProgram(i, p, "int32");
if (u.shape.length === e.shape.length) {
let c = ZD(r, e, t8, u);
return r.disposeIntermediateTensorInfo(u), c;
}
return u;
}
function Sh(r, e, t8, o) {
let n = [t8];
if (w.assertAxesAreInnerMostDims("arg" + o.charAt(0).toUpperCase() + o.slice(1), n, e.shape.length), !A().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] = w.computeOutAndReduceShapes(p.shape, n), l = y.sizeFromShape(c), m = te({ inputs: { x: p }, backend: r, attrs: { shape: [-1, l] } });
s.push(m);
let d = QD(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 ZD(r, e, o);
}
function a9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length);
let c = Sh(t8, p, a[0], "max");
return u.forEach((l) => t8.disposeIntermediateTensorInfo(l)), c;
}
var JD = { kernelName: Xs, backendName: "webgl", kernelFunc: a9 };
function i9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length);
let c = Sh(t8, p, a[0], "min");
return u.forEach((l) => t8.disposeIntermediateTensorInfo(l)), c;
}
var eA = { kernelName: Ys, backendName: "webgl", kernelFunc: i9 };
var u9 = Ut + `
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`;
var p9 = xe({ opSnippet: u9 });
var tA = { kernelName: Ko, backendName: "webgl", kernelFunc: p9 };
var c9 = Ut + "return log(x + sqrt(x * x + 1.0));";
var l9 = xe({ opSnippet: c9 });
var rA = { kernelName: qo, backendName: "webgl", kernelFunc: l9 };
var m9 = Ut + `
return atan(x);
`;
var d9 = xe({ opSnippet: m9 });
var oA = { kernelName: jo, backendName: "webgl", kernelFunc: d9 };
var f9 = Lc + `
return atan(a, b);
`;
var h9 = `
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);
` + Xr + `
return result;
`;
var g9 = nt({ opSnippet: f9, packedOpSnippet: h9 });
var nA = { kernelName: Yo, backendName: "webgl", kernelFunc: g9 };
var x9 = Ut + `
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
var y9 = xe({ opSnippet: x9 });
var sA = { kernelName: Xo, backendName: "webgl", kernelFunc: y9 };
var Vs = class {
constructor(e, t8, o, n = false, s = false) {
if (this.variableNames = ["x"], t8 === "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 = t8 === "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 * ${m} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;
return;
}
let C = "max", S = `${t8}(${t8}(${t8}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
t8 === "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 = ${C}(values, minMaxValue);
}
`;
this.userCode = `
const ivec2 strides = ivec2(${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 wu = class {
constructor(e, t8, o, n = false, s = false) {
if (this.variableNames = ["x"], t8 === "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 C = t8 === "avg", S = "0.0";
if (C || (S = "-1.0 / 1e-20"), o) {
let P = ">=";
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 ${P} 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 * ${h} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;
return;
}
let k = "max", _ = `${t8}(${t8}(${t8}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
t8 === "avg" && (_ = "avgValue / max(count, 1.0)");
let E = Math.floor(a / 4) * 4, R = a % 4, D = `
if (${C}) {
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 b9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e;
Bs(n, "avgPool");
let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
let c = w.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: t8 });
let l = new Vs(c, "avg", false);
return t8.runWebGLProgram(l, [n], "float32");
}
var aA = { kernelName: Qo, backendName: "webgl", kernelFunc: b9 };
function C9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, p, u), m = new wu(l, "avg", false);
return t8.runWebGLProgram(m, [n], "float32");
}
var iA = { kernelName: Qs, backendName: "webgl", kernelFunc: C9 };
var Ih = class {
constructor(e) {
this.variableNames = ["dy"], this.outputShape = e.inShape;
let t8 = 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 / (t8 * 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 >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`;
}
};
var vh = class {
constructor(e) {
this.variableNames = ["dy"], this.outputShape = e.inShape;
let t8 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterDepth, m = e.effectiveFilterHeight, d = e.effectiveFilterWidth, f = l - 1 - e.padInfo.front, h = m - 1 - e.padInfo.top, g = d - 1 - e.padInfo.left, x = 1 / (t8 * o * n);
this.userCode = `
const ivec3 pads = ivec3(${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 w9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new vh(m);
return t8.runWebGLProgram(d, [n], a.dtype);
}
var uA = { kernelName: Ei, backendName: "webgl", kernelFunc: w9 };
function S9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s;
Bs([n, s], "avgPoolGrad");
let { filterSize: i, strides: p, pad: u } = o, c = w.computePool2DInfo(a.shape, i, p, 1, u), l = new Ih(c);
return t8.runWebGLProgram(l, [n], a.dtype);
}
var pA = { kernelName: $i, backendName: "webgl", kernelFunc: S9 };
function I9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
return vp({ a: n, b: s, transposeA: a, transposeB: i, backend: t8 });
}
var cA = { kernelName: Zo, backendName: "webgl", kernelFunc: I9 };
var kh = class {
constructor(e, t8, o, n, s, a) {
this.outputShape = [], this.variableNames = ["x", "mean", "variance"], w.assertAndGetBroadcastShape(e, t8), w.assertAndGetBroadcastShape(e, o);
let i = "0.0";
n != null && (w.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
let p = "1.0";
s != null && (w.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 Nh = class {
constructor(e, t8, o, n, s, a) {
this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], w.assertAndGetBroadcastShape(e, t8), w.assertAndGetBroadcastShape(e, o);
let i = "vec4(0.0)";
n != null && (w.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
let p = "vec4(1.0)";
s != null && (w.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 v9 = ({ inputs: r, backend: e, attrs: t8 }) => {
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 } = t8;
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 = A().getBool("WEBGL_PACK_NORMALIZATION") ? new Nh(o.shape, n.shape, s.shape, c, l, p) : new kh(o.shape, n.shape, s.shape, c, l, p);
return e.runWebGLProgram(m, u, u[0].dtype);
};
var lA = { kernelName: In, backendName: "webgl", kernelFunc: v9 };
var Th = class {
constructor(e) {
this.variableNames = ["source"], this.outputShape = e, this.rank = e.length;
let t8 = Re(this.rank);
this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
let o = k9(this.rank), n, s = e.map((a, i) => `sourceLoc.${lv[i]} = start[${i}] + coords.${lv[i]};`);
n = `
${t8} sourceLoc;
${t8} coords = getOutputCoords();
${s.join(`
`)}
`, this.userCode = `
void main() {
${n}
setOutput(getSource(${o}));
}
`;
}
};
var lv = ["x", "y", "z", "w", "u", "v"];
function k9(r) {
if (r === 1)
return "sourceLoc";
if (r <= 6)
return lv.slice(0, r).map((e) => "sourceLoc." + e).join(",");
throw Error(`Slicing for rank ${r} is not yet supported`);
}
var _h = class {
constructor(e) {
this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
let t8 = Re(this.rank), o = Rt("coords", this.rank), n = Rt("sourceLoc", this.rank), s = this.rank === 1 ? "sourceLoc" : `vec2(${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 +
${t8}(${e.map((c, l) => `start[${l}]`).join()});` : e.map((c, l) => `${n[l]} = ${o[l]} + start[${l}];`).join(`
`);
this.userCode = `
void main() {
${t8} coords = getOutputCoords();
${t8} sourceLoc;
${u}
vec4 result = vec4(0.);
${i}
${p}
setOutput(result);
}
`;
}
};
function N9(r, e, t8, o) {
let n = o.texData.get(r.dataId), s = o.makeTensorInfo(t8, r.dtype), a = o.texData.get(s.dataId);
Object.assign(a, n), a.refCount = 1, a.shape = t8, 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 Ws(r) {
let { inputs: e, backend: t8, 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 t8.makeTensorInfo(p, n.dtype, []);
if (t8.shouldExecuteOnCPU([n]) || n.dtype === "string") {
let l = t8.texData.get(n.dataId), m = tD(l.values, i, p, n.shape, n.dtype);
return t8.makeTensorInfo(p, n.dtype, m);
}
let { isPacked: u } = t8.texData.get(n.dataId), c = ct.isSliceContinous(n.shape, i, p);
if (u || !c) {
let l = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new _h(p) : new Th(p), m = [i];
return t8.runWebGLProgram(l, [n], n.dtype, m);
}
return t8.uploadToGPU(n.dataId), N9(n, i, p, t8);
}
var mA = { kernelName: fa, backendName: "webgl", kernelFunc: Ws };
var T9 = (r) => {
let { inputs: e, backend: t8, 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, C) => b * C), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = [], f = te({ inputs: { x: n }, backend: t8, attrs: { shape: p } }), h = Ct({ inputs: { x: f }, backend: t8, attrs: { perm: u } }), g = te({ inputs: { x: h }, backend: t8, attrs: { shape: c } }), x = Ws({ inputs: { x: g }, backend: t8, attrs: { begin: l, size: m } });
return d.push(f), d.push(h), d.push(g), d.forEach((b) => t8.disposeIntermediateTensorInfo(b)), x;
};
var dA = { kernelName: Zs, backendName: "webgl", kernelFunc: T9 };
function _9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t8.readSync(n.dataId), p = t8.readSync(s.dataId), u = ih(i, p, s.dtype, s.shape, a);
return t8.makeTensorInfo([a], s.dtype, u);
}
var fA = { kernelName: Jo, backendName: "webgl", kernelFunc: _9 };
var $9 = `
int r = int(a.r) & int(b.r);
int g = int(a.g) & int(b.g);
int rb = int(a.b) & int(b.b);
int ra = int(a.a) & int(b.a);
return vec4(r, g, rb, ra);
`;
var E9 = `
return float(int(a.r) & int(b.r));
`;
function R9(r) {
let { inputs: e, backend: t8 } = r, { a: o, b: n } = e, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS"), a = A().getNumber("WEBGL_VERSION");
if (t8.shouldExecuteOnCPU([o, n]) || a === 1) {
let p = t8.texData.get(o.dataId).values, u = t8.texData.get(n.dataId).values, [c, l] = kR(o.shape, n.shape, p, u, o.dtype), m = t8.makeTensorInfo(l, o.dtype), d = t8.texData.get(m.dataId);
return d.values = c, m;
}
let i;
return s ? i = new jr($9, o.shape, n.shape, false) : i = new Pr(E9, o.shape, n.shape), t8.runWebGLProgram(i, [o, n], o.dtype);
}
var hA = { kernelName: Ha, backendName: "webgl", kernelFunc: R9 };
function D9(r) {
let { inputs: e, backend: t8 } = r, { s0: o, s1: n } = e, s = t8.readSync(o.dataId), a = t8.readSync(n.dataId), i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a));
return t8.makeTensorInfo([i.length], "int32", Int32Array.from(i));
}
var gA = { kernelName: Js, backendName: "webgl", kernelFunc: D9 };
var A9 = "return float(a != b);";
var mv = nt({ opSnippet: A9, cpuKernelImpl: KR, dtype: "bool" });
var xA = { kernelName: Yn, backendName: "webgl", kernelFunc: mv };
function bi(r) {
let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.texData.get(o.dataId);
return Dt({ inputs: { x: n.complexTensorInfos.real }, backend: t8 });
}
var yA = { kernelName: Gi, backendName: "webgl", kernelFunc: bi };
var F9 = "return float(int(x));";
function bA(r, e) {
let t8 = new rr(r.shape, F9), o = e.runWebGLProgram(t8, [r], "int32");
return { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
function dv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dtype: s } = o;
if (s === "complex64") {
if (n.dtype === "complex64")
return Dt({ inputs: { x: n }, backend: t8 });
let a = Gr(n.shape), i = dv({ inputs: { x: n }, backend: t8, attrs: { dtype: "float32" } }), p = Or({ inputs: { real: i, imag: a }, backend: t8 });
return a.dispose(), t8.disposeIntermediateTensorInfo(i), p;
}
if (n.dtype === "complex64") {
let a = bi({ inputs: { input: n }, backend: t8 }), i = dv({ inputs: { x: a }, backend: t8, attrs: { dtype: s } });
return t8.disposeIntermediateTensorInfo(a), i;
}
if (!y.hasEncodingLoss(n.dtype, s)) {
let a = Dt({ inputs: { x: n }, backend: t8 });
return { dataId: a.dataId, shape: a.shape, dtype: s };
}
if (t8.shouldExecuteOnCPU([n])) {
let a = t8.texData.get(n.dataId).values, [i, p, u] = NR(a, n.shape, n.dtype, s);
return t8.makeTensorInfo(i, p, u);
}
if (s === "int32")
return bA(n, t8);
if (s === "bool") {
let a = t8.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = mv({ inputs: { a: n, b: a }, backend: t8 });
return t8.disposeIntermediateTensorInfo(a), p;
}
throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`);
}
var CA = { kernelName: yo, backendName: "webgl", kernelFunc: dv };
var wA = "return ceil(x);";
var P9 = xe({ opSnippet: wA, packedOpSnippet: wA, cpuKernelImpl: TR });
var SA = { kernelName: en, backendName: "webgl", kernelFunc: P9 };
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 Eh = 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 O9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i;
A().getBool("WEBGL_PACK_CLIP") ? i = new Eh(n.shape) : i = new $h(n.shape);
let p = [[s], [a]];
return t8.runWebGLProgram(i, [n], n.dtype, p);
}
var IA = { kernelName: bo, backendName: "webgl", kernelFunc: O9 };
var Rh = class {
constructor(e) {
this.variableNames = ["real", "imag"], this.outputShape = e, this.userCode = `
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`;
}
};
function vA(r, e) {
return { dataId: e.dataId, dtype: e.dtype, shape: r.shape };
}
function M9(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e, n = t8.texData.get(o.dataId), s = new Rh(o.shape), a = [vA(o, n.complexTensorInfos.real), vA(o, n.complexTensorInfos.imag)];
return t8.runWebGLProgram(s, a, a[0].dtype);
}
var kA = { kernelName: Di, backendName: "webgl", kernelFunc: M9 };
var Dh = class {
constructor(e) {
this.outputShape = [], this.outputShape = w.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${i}`);
let t8 = new Array(e.length - 1);
t8[0] = e[0][1];
for (let a = 1; a < t8.length; a++)
t8[a] = t8[a - 1] + e[a][1];
let o = [`if (yC < ${t8[0]}) setOutput(getT0(yR, yC));`];
for (let a = 1; a < t8.length; a++) {
let i = t8[a - 1];
o.push(`else if (yC < ${t8[a]}) setOutput(getT${a}(yR, yC-${i}));`);
}
let n = t8.length, s = t8[t8.length - 1];
o.push(`else setOutput(getT${n}(yR, yC-${s}));`), this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${o.join(`
`)}
}
`;
}
};
var Fh = class {
constructor(e, t8) {
this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = w.computeOutShape(e, t8);
let o = this.outputShape, n = o.length, s = Re(n), a = Rt("coords", n), i = ["x", "y", "z", "w", "u", "v"].slice(0, n);
this.variableNames = e.map((h, g) => `T${g}`);
let p = new Array(e.length - 1);
p[0] = e[0][t8];
for (let h = 1; h < p.length; h++)
p[h] = p[h - 1] + e[h][t8];
let u = i[t8], 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}(${Ah(i, u, g)}),
vec2(${Ah(c, u, g)}));
}`;
}
let d = p.length, f = p[p.length - 1];
m += `
return getChannel(
getT${d}(${Ah(i, u, f)}),
vec2(${Ah(c, u, f)}));`, this.userCode = `
float getValue(${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 Ah(r, e, t8) {
let o = r.indexOf(e);
return r.map((s, a) => a === o ? `${s} - ${t8}` : s).join();
}
function kp(r) {
let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.texData.get(o.dataId);
return Dt({ inputs: { x: n.complexTensorInfos.imag }, backend: t8 });
}
var NA = { kernelName: Vi, backendName: "webgl", kernelFunc: kp };
function zc(r, e, t8) {
let o = r[0].dtype;
if (o === "complex64") {
let d = r.map((b) => bi({ inputs: { input: b }, backend: t8 })), f = r.map((b) => kp({ inputs: { input: b }, backend: t8 })), h = zc(d, e, t8), g = zc(f, e, t8), x = Or({ inputs: { real: h, imag: g }, backend: t8 });
return d.forEach((b) => t8.disposeIntermediateTensorInfo(b)), f.forEach((b) => t8.disposeIntermediateTensorInfo(b)), t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(g), x;
}
let n = t8.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: t8, attrs: { shape: _ } });
}), f = d.map((S) => ({ vals: t8.readSync(S.dataId), shape: S.shape })), h = w.computeOutShape(d.map((S) => S.shape), 1), g = d[0].shape[0] === 1, x = _R(f, h, o, g), b = w.computeOutShape(r.map((S) => S.shape), e), C = t8.makeTensorInfo(b, o, x);
return d.forEach((S) => t8.disposeIntermediateTensorInfo(S)), C;
}
let s = r.filter((d) => y.sizeFromShape(d.shape) > 0), a = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && s[0].shape.length > 1;
if (s.length === 1) {
let d = a ? new rr(r[0].shape, Oa) : new Fr(r[0].shape, Oa);
return t8.runWebGLProgram(d, r, o);
}
let i = A().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(zc(g, e, t8));
}
let f = zc(d, e, t8);
for (let h of d)
t8.disposeIntermediateTensorInfo(h);
return f;
}
if (a) {
let d = new Fh(s.map((f) => f.shape), e);
return t8.runWebGLProgram(d, s, o);
}
let { tensors2D: p, outShape: u } = L9(s, e, t8), c = new Dh(p.map((d) => d.shape)), l = t8.runWebGLProgram(c, p, o);
p.forEach((d) => t8.disposeIntermediateTensorInfo(d));
let m = te({ inputs: { x: l }, attrs: { shape: u }, backend: t8 });
return t8.disposeIntermediateTensorInfo(l), m;
}
function L9(r, e, t8) {
let o = w.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: t8 })), outShape: o };
}
function fv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape);
w.assertParamsConsistent(a, s);
let i = w.computeOutShape(e.map((u) => u.shape), s);
if (y.sizeFromShape(i) === 0)
return t8.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: t8 }) : zc(p, s, t8);
}
var TA = { kernelName: ea, backendName: "webgl", kernelFunc: fv };
var Vc = class {
constructor(e, t8 = 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, C = 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 _ = t8 ? "result += getBiasAtOutCoords();" : "";
t8 && 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}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${C}];
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;
${_}
${k}
setOutput(result);
}
`;
}
};
var Ph = class {
constructor(e) {
this.variableNames = ["x", "W"], this.outputShape = e.outShape;
let t8 = e.padInfo.front, o = e.padInfo.top, n = e.padInfo.left, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.filterDepth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4;
this.userCode = `
const ivec3 strides = ivec3(${s}, ${a}, ${i});
const ivec3 pads = ivec3(${t8}, ${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, ${f} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`;
}
};
var Wc = class {
constructor(e, t8 = false, o = null, n = false, s = false) {
this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = pt(this.outputShape.length);
let a = e.padInfo.left, i = e.strideWidth, p = e.dilationWidth, u = e.filterHeight, c = e.filterWidth, l = c, m = `
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;
for (let g = 0; g < c; g++)
m += `
vec4 xTexelC${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 = t8 ? "result += getBiasAtOutCoords();" : "";
t8 && 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 Oh = class {
constructor(e, t8) {
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 = pt(this.outputShape.length);
let { dataFormat: o } = t8, n = vt(), 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 Mh(r, e) {
let t8 = r.length;
return t8 >= 3 ? e ? [...r.slice(0, -3), r[t8 - 3] * r[t8 - 2], r[t8 - 1]] : [...r.slice(0, -3), r[t8 - 3], r[t8 - 2] * r[t8 - 1]] : !e && t8 === 1 && r[0] > 1 ? [r[0], 1] : null;
}
function Lh({ x: r, filter: e, convInfo: t8, 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 = t8.inChannels, l = p[0] * p[1] * p[2], m = t8.outChannels, d = t8.dataFormat === "channelsLast", f = false, h = false, g, x = [];
if (s != null) {
let S = Mh(s.shape, d);
S != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: S } }), x.push(s));
}
if (n != null) {
let S = Mh(n.shape, d);
S != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: S } }), x.push(n));
}
if (!((l === 1 || m === 1) && c > cv) && 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, t8.inChannels], dtype: r.dtype }, _ = u.shape;
u.shape = u.shape.slice(), u.shape[u.shape.length - 2]++, y.assert(yu(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, t8.inChannels, t8.outChannels] } });
x.push(E);
let R = vp({ 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 = t8.outShape, g = Dt({ inputs: { x: R }, backend: o }), g.shape = t8.outShape, x.push(R);
} else {
let S = t8.outHeight * t8.outWidth, k = te({ inputs: { x: r }, backend: o, attrs: { shape: d ? [t8.batchSize, S, t8.inChannels] : [t8.batchSize, t8.inChannels, S] } }), _ = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, t8.inChannels, t8.outChannels] } }), E = vp({ 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: t8.outShape } }), x.push(k), x.push(_), x.push(E);
}
for (let S of x)
o.disposeIntermediateTensorInfo(S);
return g;
}
function Bh({ x: r, filter: e, convInfo: t8, 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 } = t8, f = d === "channelsLast", h = p * u * c, g = m * l, x = [t8.batchSize, h, g], b = true, C = false, S = [];
if (s != null) {
let q = Mh(s.shape, f);
q != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: q } }), S.push(s));
}
if (n != null) {
let q = Mh(n.shape, f);
q != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: q } }), 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 Oh(x, t8), E = [r.shape, [t8.padInfo.top, t8.padInfo.left], [t8.strideHeight, t8.strideWidth], [t8.dilationHeight, t8.dilationWidth], [t8.inChannels], [t8.filterWidth * t8.inChannels], [t8.outWidth]], R = o.runWebGLProgram(_, [r], "float32", E), D = te({ inputs: { x: R }, backend: o, attrs: { shape: x } });
S.push(R), S.push(D);
let P = n != null, O = s != null, M = i === "leakyrelu", L = i ? yi(i, true) : null, B = new Bc(f ? D.shape : k.shape, f ? k.shape : D.shape, f ? [t8.batchSize, g, t8.outChannels] : [t8.batchSize, t8.outChannels, g], b, C, P, L, O, M), z = f ? [D, k] : [k, D];
if (n && z.push(n), O && z.push(s), M) {
let q = o.makeTensorInfo([], "float32", y.createScalarValue(a, "float32"));
z.push(q), S.push(q);
}
let U = o.runWebGLProgram(B, z, "float32"), j = te({ inputs: { x: U }, backend: o, attrs: { shape: t8.outShape } });
S.push(U);
for (let q of S)
o.disposeIntermediateTensorInfo(q);
return j;
}
function B9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(p), m = w.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 = Lh({ x: n, filter: s, convInfo: m, backend: t8 });
else if (m.strideWidth <= 2 && l === "channelsLast" && A().getBool("WEBGL_EXP_CONV")) {
let h = new Wc(m), g = [[m.padInfo.top, m.padInfo.left], [m.strideHeight, m.strideWidth], [m.dilationHeight, m.dilationWidth], [m.inHeight, m.inWidth]];
d = t8.runWebGLProgram(h, [n, s], "float32", g);
} else if (A().getBool("WEBGL_CONV_IM2COL"))
d = Bh({ x: n, filter: s, convInfo: m, backend: t8 });
else {
let h = new Vc(m);
d = t8.runWebGLProgram(h, [n, s], "float32");
}
let f = te({ inputs: { x: d }, backend: t8, attrs: { shape: m.outShape } });
return t8.disposeIntermediateTensorInfo(d), f;
}
var _A = { kernelName: tn, backendName: "webgl", kernelFunc: B9 };
var zh = class {
constructor(e) {
this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
let t8 = 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 * ${t8} - ${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;
}
${a ? `float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);` : `float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);`}
}
}
}
setOutput(dotProd);
}
`;
}
};
var Vh = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
let t8 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dataFormat === "channelsLast", i = t8 - 1 - e.padInfo.top, p = o - 1 - e.padInfo.left, u = a ? 1 : 2, c = a ? 2 : 3, l = a ? 3 : 1;
this.userCode = `
const ivec2 pads = ivec2(${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 < ${t8}; 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 = ${t8} - 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 Wh = class {
constructor(e) {
this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
let t8 = 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 * ${t8} - ${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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`;
}
};
var Uh = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
let t8 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = t8 - 1 - e.padInfo.front, u = o - 1 - e.padInfo.top, c = n - 1 - e.padInfo.left;
this.userCode = `
const ivec3 pads = ivec3(${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 < ${t8}; 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 = ${t8} - 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 z9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new zh(m);
return t8.runWebGLProgram(d, [n, s], "float32");
}
var $A = { kernelName: Ai, backendName: "webgl", kernelFunc: z9 };
var Gh = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "strides", type: "vec2" }], this.outputShape = e.inShape, this.enableShapeUniforms = pt(this.outputShape.length);
let t8 = e.filterHeight, o = e.filterWidth, n = t8 - 1 - e.padInfo.top, s = o - 1 - e.padInfo.left;
this.userCode = `
const ivec2 pads = ivec2(${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 < ${t8}; 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 = ${t8} - 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 V9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(u), m = w.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l);
if (A().getBool("WEBGL_PACK") && l === "channelsLast") {
let d = [[m.strideHeight, m.strideWidth]], f = new Gh(m);
return t8.runWebGLProgram(f, [n, s], "float32", d);
} else {
let d = new Vh(m);
return t8.runWebGLProgram(d, [n, s], "float32");
}
}
var EA = { kernelName: rn, backendName: "webgl", kernelFunc: V9 };
function W9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), c = new Ph(u);
return t8.runWebGLProgram(c, [n, s], "float32");
}
var RA = { kernelName: on, backendName: "webgl", kernelFunc: W9 };
function U9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = w.computeConv3DInfo(n.shape, p, a, 1, i), c = new Wh(u);
return t8.runWebGLProgram(c, [n, s], "float32");
}
var DA = { kernelName: Ka, backendName: "webgl", kernelFunc: U9 };
function G9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o, u = w.computeConv3DInfo(p, s.shape, i, 1, a), c = new Uh(u);
return t8.runWebGLProgram(c, [n, s], "float32");
}
var AA = { kernelName: nn, backendName: "webgl", kernelFunc: G9 };
var H9 = Fo + `
return cos(x);
`;
var K9 = `
vec4 result = cos(x);
bvec4 isNaN = isnan(x);
${Xr}
return result;
`;
var q9 = xe({ opSnippet: H9, packedOpSnippet: K9 });
var FA = { kernelName: sn, backendName: "webgl", kernelFunc: q9 };
var j9 = `
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`;
var X9 = xe({ opSnippet: j9 });
var PA = { kernelName: an, backendName: "webgl", kernelFunc: X9 };
var Hh = class {
constructor(e, t8, o, n, s) {
this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = [];
let [a, i, p, u] = e, [c] = t8, [l, m] = o;
this.outputShape = [c, l, m, u];
let d = n === "bilinear" ? 1 : 0, [f, h] = [`${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) * ${f}`], [C, S, k] = m > 1 ? [`${(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(${C});
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(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`;
}
};
var Y9 = (r) => {
let { inputs: e, backend: t8, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new Hh(n.shape, s.shape, i, p, u);
return t8.runWebGLProgram(c, [n, s, a], "float32");
};
var OA = { kernelName: cn, backendName: "webgl", kernelFunc: Y9 };
var Np;
(function(r) {
r.Prod = "*", r.Sum = "+";
})(Np || (Np = {}));
var em = class {
constructor(e, t8, o, n) {
this.op = e, this.outputShape = t8, this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }];
let s = this.outputShape.length, a = this.op === Np.Prod ? "1.0" : "0.0", i = o ? a : `getX(${MA(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 = ${LA(s, "coords", this.op)};
float val = ${i};
int pow2 = int(pow(2.0, index));
if (${u}) {
int idx = ${c};
${LA(s, "coords", this.op)} = idx;
val ${this.op}= getX(${MA(s, "coords", this.op)});
}
setOutput(val);
}
`;
}
};
function MA(r, e, t8) {
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 ${t8} for rank ${r} is not yet supported`);
}
function LA(r, e, t8) {
if (r === 1)
return `${e}`;
if (r === 2)
return `${e}.y`;
if (r === 3)
return `${e}.z`;
if (r === 4)
return `${e}.w`;
throw new Error(`Cumulative ${t8} for rank ${r} is not yet supported`);
}
function Kh(r, e, t8, o, n, s) {
let a = e.shape.length, i = w.getAxesPermutation([o], a), p = e;
i != null && (p = Ct({ inputs: { x: e }, backend: t8, attrs: { perm: i } }));
let u = w.getInnerMostAxes(1, a)[0];
if (u !== a - 1)
throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`);
let c = p.shape[u], l = Dt({ inputs: { x: p }, backend: t8 });
for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) {
let d = new em(r, p.shape, false, s), f = [[m]], h = l;
l = t8.runWebGLProgram(d, [l], l.dtype, f), t8.disposeIntermediateTensorInfo(h);
}
if (n) {
let m = new em(r, p.shape, n, s), d = l;
l = t8.runWebGLProgram(m, [l], l.dtype), t8.disposeIntermediateTensorInfo(d);
}
if (i != null) {
let m = w.getUndoAxesPermutation(i), d = Ct({ inputs: { x: l }, backend: t8, attrs: { perm: m } });
return t8.disposeIntermediateTensorInfo(l), t8.disposeIntermediateTensorInfo(p), d;
}
return l;
}
function Q9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
return Kh(Np.Prod, n, t8, s, a, i);
}
var BA = { kernelName: un, backendName: "webgl", kernelFunc: Q9 };
function Z9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
return Kh(Np.Sum, n, t8, s, a, i);
}
var zA = { kernelName: pn, backendName: "webgl", kernelFunc: Z9 };
function J9(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o;
if (n.shape.length === 1) {
let p = t8.readSync(n.dataId), u = t8.readSync(s.dataId), c = ih(p, u, s.dtype, s.shape, a);
return t8.makeTensorInfo([a], s.dtype, c);
} else if (n.shape.length === 2) {
let p = t8.bufferSync(n), u = t8.bufferSync(s), c = vR(p, u, a, i);
return t8.makeTensorInfo(c.shape, s.dtype, c.values);
}
throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`);
}
var VA = { kernelName: ta, backendName: "webgl", kernelFunc: J9 };
var qh = class {
constructor(e, t8, o) {
this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t8, 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 / ${t8};
int offset_h = imod(h, ${t8});
int in_w = w / ${t8};
int offset_w = imod(w, ${t8});
int offset_d = (offset_h * ${t8} + 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 eJ(r) {
let { inputs: e, backend: t8, 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 qh(f, s, a);
return t8.runWebGLProgram(h, [n], n.dtype);
}
var WA = { kernelName: ln, backendName: "webgl", kernelFunc: eJ };
var Uc = class {
constructor(e, t8 = 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 = pt(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 = t8 ? "result += getBiasAtOutCoords();" : "";
t8 && 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}
${c}
setOutput(result);
}
`;
}
};
var Gc = class {
constructor(e, t8 = false, o = null, n = false, s = false) {
this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = pt(this.outputShape.length);
let a = e.outChannels / e.inChannels, i = e.padInfo.left, p = e.strideWidth, u = e.dilationWidth, c = e.filterHeight, l = e.filterWidth, m = l, d = `
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;
for (let x = 0; x < l; x++)
d += `
vec4 xTexelC${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 C = 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) + ${C};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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);
`) : C === 1 ? d += `
xC${b + 1} = xTexelC${b};
` : d += `
xCOffset = xC + ${C};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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 = t8 ? "result += getBiasAtOutCoords();" : "";
t8 && 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 tJ(r) {
let { inputs: e, backend: t8, 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(w.eitherStridesOrDilationsAreOne(a, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);
let l = w.computeConv2DInfo(n.shape, s.shape, a, c, i, u, true), m;
A().getBool("WEBGL_PACK_DEPTHWISECONV") && l.strideWidth <= 2 && l.outChannels / l.inChannels === 1 ? m = new Gc(l) : m = new Uc(l);
let d = [[l.padInfo.top, l.padInfo.left], [l.strideHeight, l.strideWidth], [l.dilationHeight, l.dilationWidth], [l.inHeight, l.inWidth]];
return t8.runWebGLProgram(m, [n, s], "float32", d);
}
var UA = { kernelName: mn, backendName: "webgl", kernelFunc: tJ };
var jh = class {
constructor(e) {
this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
let t8 = 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 * ${t8} - ${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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`;
}
};
var Xh = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
let t8 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = t8 - 1 - e.padInfo.top, i = o - 1 - e.padInfo.left, p = e.outChannels / e.inChannels;
this.userCode = `
const ivec2 pads = ivec2(${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 < ${t8}; 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 = ${t8} - 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 * ${p} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`;
}
};
function rJ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = w.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new jh(l);
return t8.runWebGLProgram(m, [n, s], "float32");
}
var GA = { kernelName: Fi, backendName: "webgl", kernelFunc: rJ };
function oJ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = w.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new Xh(l);
return t8.runWebGLProgram(m, [n, s], "float32");
}
var HA = { kernelName: Pi, backendName: "webgl", kernelFunc: oJ };
var Yh = class {
constructor(e) {
this.variableNames = ["X"], this.outputShape = [e, e], this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`;
}
};
function nJ(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = te({ inputs: { x: o }, backend: t8, attrs: { shape: [s] } }), i = new Yh(s), p = t8.runWebGLProgram(i, [a], a.dtype), u = te({ inputs: { x: p }, backend: t8, attrs: { shape: n } });
return t8.disposeIntermediateTensorInfo(a), t8.disposeIntermediateTensorInfo(p), u;
}
var KA = { kernelName: ra, backendName: "webgl", kernelFunc: nJ };
var Qh = class {
constructor(e) {
this.variableNames = ["x", "W"], this.outputShape = e.outShape;
let { inHeight: t8, inWidth: o, padInfo: n, strideHeight: s, strideWidth: a, filterHeight: i, filterWidth: p, dilationHeight: u, dilationWidth: c } = e, { top: l, left: m } = n;
this.userCode = `
const ivec2 strides = ivec2(${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 < ${t8}) {
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 sJ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = w.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c, l = new Qh(u);
c = t8.runWebGLProgram(l, [n, s], "float32");
let m = te({ inputs: { x: c }, backend: t8, attrs: { shape: u.outShape } });
return t8.disposeIntermediateTensorInfo(c), m;
}
var qA = { kernelName: dn, backendName: "webgl", kernelFunc: sJ };
function aJ(r) {
let { inputs: e, backend: t8, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length);
w.checkEinsumDimSizes(a.length, p, s);
let { path: u, steps: c } = w.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 } = w.getEinsumPermutation(d, p[g]), C;
w.isIdentityPermutation(x) ? C = s[g] : (C = Ct({ inputs: { x: s[g] }, backend: t8, attrs: { perm: x } }), f.push(C));
let S = C.shape.slice();
for (let k = 0; k < b.length; ++k)
S.splice(b[k], 0, 1);
y.arraysEqual(C.shape, S) || (C = te({ inputs: { x: C }, backend: t8, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = Zl({ inputs: { a: C, b: m }, backend: t8 }), f.push(m));
}
h < l - 1 && (u[h] >= 0 && (m = Ip({ inputs: { x: m }, backend: t8, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--);
}
for (let h of f)
h !== m && t8.disposeIntermediateTensorInfo(h);
return m;
}
var jA = { kernelName: Li, backendName: "webgl", kernelFunc: aJ };
var iJ = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var uJ = `
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 pJ = xe({ opSnippet: iJ, packedOpSnippet: uJ });
var XA = { kernelName: hn, backendName: "webgl", kernelFunc: pJ };
var cJ = "return (b >= 0.0) ? a : a * (b + 1.0);";
var lJ = `
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
var mJ = (r) => {
let { inputs: e, backend: t8 } = r, { dy: o, y: n } = e, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new jr(lJ, o.shape, n.shape) : new Pr(cJ, o.shape, n.shape);
return t8.runWebGLProgram(s, [o, n], o.dtype);
};
var YA = { kernelName: qa, backendName: "webgl", kernelFunc: mJ };
var dJ = `
return vec4(equal(a, b));
`;
var fJ = "return float(a == b);";
var hJ = nt({ opSnippet: fJ, packedOpSnippet: dJ, dtype: "bool", cpuKernelImpl: $R });
var QA = { kernelName: xn, backendName: "webgl", kernelFunc: hJ };
var gJ = `
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${w.ERF_P};
float a1 = ${w.ERF_A1};
float a2 = ${w.ERF_A2};
float a3 = ${w.ERF_A3};
float a4 = ${w.ERF_A4};
float a5 = ${w.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`;
var xJ = xe({ opSnippet: gJ });
var ZA = { kernelName: gn, backendName: "webgl", kernelFunc: xJ };
var yJ = Fo + `
return exp(x);
`;
var bJ = `
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
var hv = xe({ opSnippet: yJ, packedOpSnippet: bJ, cpuKernelImpl: ER, dtype: "float32" });
var JA = { kernelName: yn, backendName: "webgl", kernelFunc: hv };
function Zh(r) {
let { inputs: e, attrs: t8, backend: o } = r, { dim: n } = t8, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n;
return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), te({ inputs: { x: s }, backend: o, attrs: { shape: i } });
}
var eF = { kernelName: oa, backendName: "webgl", kernelFunc: Zh };
var tF = "return exp(x) - 1.0;";
var CJ = xe({ opSnippet: tF, packedOpSnippet: tF, cpuKernelImpl: RR });
var rF = { kernelName: bn, backendName: "webgl", kernelFunc: CJ };
var tm = class {
constructor(e, t8, o) {
this.variableNames = ["real", "imag"];
let n = t8[1];
this.outputShape = t8;
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 Jh(r, e, t8) {
let o = t8.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: t8, 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 = t8.runWebGLProgram(u, l, "float32"), d = t8.runWebGLProgram(c, l, "float32"), f = Or({ inputs: { real: m, imag: d }, backend: t8 });
t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d);
let h = te({ inputs: { x: f }, backend: t8, attrs: { shape: r.shape } });
return t8.disposeIntermediateTensorInfo(i), t8.disposeIntermediateTensorInfo(f), h;
}
function wJ(r) {
let { inputs: e, backend: t8 } = r, { input: o } = e;
return Jh(o, false, t8);
}
var oF = { kernelName: Bi, backendName: "webgl", kernelFunc: wJ };
var eg = class {
constructor(e, t8) {
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 Ci(r) {
let { backend: e, attrs: t8 } = r, { shape: o, value: n } = t8, { dtype: s } = t8;
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 eg(o, n), i = [[n]];
return e.runWebGLProgram(a, [], s, i);
}
}
var nF = { kernelName: na, backendName: "webgl", kernelFunc: Ci };
var tg = class {
constructor(e) {
this.variableNames = ["Image"], this.outputShape = [];
let t8 = e[2];
this.outputShape = e, this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t8} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t8}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`;
}
};
var sF = { kernelName: Cn, backendName: "webgl", kernelFunc: ({ inputs: r, backend: e }) => {
let { image: t8 } = r, o = e, n = new tg(t8.shape);
return o.runWebGLProgram(n, [t8], t8.dtype);
} };
var aF = "return floor(x);";
var SJ = xe({ opSnippet: aF, packedOpSnippet: aF, cpuKernelImpl: DR });
var iF = { kernelName: wn, backendName: "webgl", kernelFunc: SJ };
var IJ = `
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`;
var vJ = `
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`;
var kJ = nt({ opSnippet: IJ, packedOpSnippet: vJ, dtype: "int32" });
var uF = { kernelName: Sn, backendName: "webgl", kernelFunc: kJ };
var rg = class {
constructor(e) {
this.variableNames = ["A"];
let t8 = vt(), [o, n] = e;
this.outputShape = e, this.userCode = `
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);
vec4 values = ${t8.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`;
}
};
var og = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true;
let t8 = vt(), [o, n] = e;
this.outputShape = e, this.userCode = `
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}.0, ${o}.0);
vec4 values = ${t8.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);
}
}
${t8.output} = result;
}
`;
}
};
var pF = { kernelName: Au, backendName: "webgl", kernelFunc: NJ };
var Hc;
var gv = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
function NJ(r) {
let { inputs: e, backend: t8, 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 = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
(Hc == null || h !== gv) && (gv = h, Hc = document.createElement("canvas").getContext("2d", { willReadFrequently: gv })), Hc.canvas.width = p, Hc.canvas.height = u, Hc.drawImage(n, 0, 0, p, u), n = Hc.canvas;
}
let m = t8.makeTensorInfo(c, "int32");
t8.texData.get(m.dataId).usage = mr.PIXELS, t8.gpgpu.uploadPixelDataToTexture(t8.getTexture(m.dataId), n);
let d = A().getBool("WEBGL_PACK") ? new og(l) : new rg(l), f = t8.runWebGLProgram(d, [m], "int32");
return t8.disposeData(m.dataId), f;
}
function TJ(r) {
let { inputs: e, backend: t8, 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 = w.convertConv2DDataFormat(c), g = w.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h), x, b = [], C = a != null, S = i != null, k = d === "leakyrelu", _ = () => {
let R = [n, s], D = (P, O) => {
if (O === "NCHW" && P.shape.length === 1 && P.shape[0] !== 1) {
let M = te({ inputs: { x: P }, backend: t8, attrs: { shape: [P.shape[0], 1, 1] } });
return b.push(M), M;
}
return P;
};
if (C && R.push(D(a, c)), S && R.push(D(i, c)), k) {
let P = t8.makeTensorInfo([], "float32", y.createScalarValue(f, "float32"));
R.push(P), b.push(P);
}
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 = Lh({ x: n, filter: s, convInfo: g, backend: t8, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f });
else if (g.strideWidth <= 2 && h === "channelsLast" && A().getBool("WEBGL_EXP_CONV")) {
let R = d ? yi(d, true) : null, D = new Wc(g, C, R, S, k), P = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], O = _();
x = t8.runWebGLProgram(D, O, "float32", P);
} else if (A().getBool("WEBGL_CONV_IM2COL"))
x = Bh({ x: n, filter: s, convInfo: g, backend: t8, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f });
else {
let R = d ? yi(d, false) : null, D = new Vc(g, C, R, S, k), P = _();
x = t8.runWebGLProgram(D, P, "float32");
}
let E = te({ inputs: { x }, backend: t8, attrs: { shape: g.outShape } });
return b.push(x), b.forEach((R) => t8.disposeIntermediateTensorInfo(R)), E;
}
var cF = { kernelName: Io, backendName: "webgl", kernelFunc: TJ };
function _J(r) {
let { inputs: e, backend: t8, 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(w.eitherStridesOrDilationsAreOne(p, h), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`);
let g = w.computeConv2DInfo(n.shape, s.shape, p, h, u, l, true), x = A().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, b = m ? yi(m, x) : null, C = [n, s], S = a != null, k = i != null, _ = m === "leakyrelu";
if (S && C.push(a), k && C.push(i), _) {
let P = t8.makeTensorInfo([], "float32", y.createScalarValue(d, "float32"));
C.push(P), f.push(P);
}
let E;
x ? E = new Gc(g, S, b, k, _) : E = new Uc(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 = t8.runWebGLProgram(E, C, "float32", R);
return f.forEach((P) => t8.disposeIntermediateTensorInfo(P)), D;
}
var lF = { kernelName: vo, backendName: "webgl", kernelFunc: _J };
var ng = class {
constructor(e, t8, o, n) {
this.sliceDim = e, this.strides = t8, 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 $J(r) {
let { inputs: e, backend: t8 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = w.prepareAndValidate(o, n), m = te({ inputs: { x: n }, backend: t8, attrs: { shape: [u, a] } }), d = te({ inputs: { x: o }, backend: t8, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } });
if (t8.shouldExecuteOnCPU([o, n]) || o.dtype === "string") {
let x = t8.readSync(n.dataId), b = t8.bufferSync(o), C = AR(x, b, o.dtype, u, a, c, l, o.shape, i);
return t8.makeTensorInfo(p, o.dtype, C.values);
}
let f = new ng(a, l, [u, c], o.shape), h = t8.runWebGLProgram(f, [d, m], d.dtype), g = te({ inputs: { x: h }, backend: t8, attrs: { shape: p } });
return t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(h), g;
}
var mF = { kernelName: vn, backendName: "webgl", kernelFunc: $J };
var sg = class {
constructor(e, t8) {
this.variableNames = ["A", "indices"], this.outputShape = t8, this.rank = t8.length;
let o = Re(this.rank), n = EJ(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 EJ(r, e) {
let t8 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = [];
for (let n = 0; n < r.length; n++)
n === 2 ? o.push("index") : o.push(`${t8[n]}`);
return o.join();
}
function xv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0];
if (A().get("DEBUG")) {
let b = t8.readSync(s.dataId), C = n.shape[p];
for (let S = 0; S < b.length; ++S) {
let k = b[S];
y.assert(k <= C - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${C - 1}]`);
}
}
let u = w.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = te({ inputs: { x: n }, backend: t8, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = te({ inputs: { x: s }, backend: t8, 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 (t8.shouldExecuteOnCPU([n, s]) || n.dtype === "string") {
let b = t8.bufferSync(d), C = t8.bufferSync(m), S = FR(C, b, f);
return l.forEach((k) => t8.disposeIntermediateTensorInfo(k)), t8.makeTensorInfo(u.outputShape, S.dtype, S.values);
}
let h = new sg(m.shape, f), g = t8.runWebGLProgram(h, [m, d], m.dtype);
l.push(g);
let x = te({ inputs: { x: g }, backend: t8, attrs: { shape: u.outputShape } });
return l.forEach((b) => t8.disposeIntermediateTensorInfo(b)), x;
}
var dF = { kernelName: sa, backendName: "webgl", kernelFunc: xv };
var RJ = "return float(a > b);";
var DJ = `
return vec4(greaterThan(a, b));
`;
var AJ = nt({ opSnippet: RJ, packedOpSnippet: DJ, cpuKernelImpl: PR, dtype: "bool" });
var fF = { kernelName: kn, backendName: "webgl", kernelFunc: AJ };
var FJ = "return float(a >= b);";
var PJ = `
return vec4(greaterThanEqual(a, b));
`;
var OJ = nt({ opSnippet: FJ, packedOpSnippet: PJ, dtype: "bool", cpuKernelImpl: OR });
var hF = { kernelName: Nn, backendName: "webgl", kernelFunc: OJ };
function MJ(r) {
let { inputs: e, backend: t8 } = r, { input: o } = e;
return Jh(o, true, t8);
}
var gF = { kernelName: zi, backendName: "webgl", kernelFunc: MJ };
var LJ = "return float(!isnan(x) && !isinf(x));";
var BJ = xe({ opSnippet: LJ, dtype: "bool" });
var xF = { kernelName: Tn, backendName: "webgl", kernelFunc: BJ };
var zJ = "return float(isinf(x));";
var VJ = xe({ opSnippet: zJ, dtype: "bool" });
var yF = { kernelName: _n, backendName: "webgl", kernelFunc: VJ };
var WJ = "return float(isnan(x));";
var UJ = xe({ opSnippet: WJ, dtype: "bool" });
var bF = { kernelName: $n, backendName: "webgl", kernelFunc: UJ };
var GJ = "return float(a < b);";
var HJ = `
return vec4(lessThan(a, b));
`;
var KJ = nt({ opSnippet: GJ, packedOpSnippet: HJ, cpuKernelImpl: MR, dtype: "bool" });
var CF = { kernelName: Rn, backendName: "webgl", kernelFunc: KJ };
var qJ = "return float(a <= b);";
var jJ = `
return vec4(lessThanEqual(a, b));
`;
var XJ = nt({ opSnippet: qJ, packedOpSnippet: jJ, cpuKernelImpl: LR, dtype: "bool" });
var wF = { kernelName: Dn, backendName: "webgl", kernelFunc: XJ };
function YJ(r) {
let { backend: e, attrs: t8 } = r, { start: o, stop: n, num: s } = t8, a = BR(o, n, s);
return e.makeTensorInfo([a.length], "float32", a);
}
var SF = { kernelName: An, backendName: "webgl", kernelFunc: YJ };
var QJ = Fo + `
return x < 0.0 ? 0./0. : log(x);
`;
var ZJ = `
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 JJ = xe({ opSnippet: QJ, packedOpSnippet: ZJ, cpuKernelImpl: zR });
var IF = { kernelName: Fn, backendName: "webgl", kernelFunc: JJ };
var eee = Fo + `
return log(1.0 + x);
`;
var tee = xe({ opSnippet: eee });
var vF = { kernelName: Pn, backendName: "webgl", kernelFunc: tee };
var ree = "return float(a >= 1.0 && b >= 1.0);";
var oee = `
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`;
var nee = nt({ opSnippet: ree, packedOpSnippet: oee, dtype: "bool" });
var kF = { kernelName: On, backendName: "webgl", kernelFunc: nee };
var see = "return float(!(x >= 1.0));";
var aee = xe({ opSnippet: see });
var NF = { kernelName: Mn, backendName: "webgl", kernelFunc: aee };
var iee = "return float(a >= 1.0 || b >= 1.0);";
var uee = `
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`;
var pee = nt({ opSnippet: iee, packedOpSnippet: uee, dtype: "bool" });
var TF = { kernelName: Ln, backendName: "webgl", kernelFunc: pee };
var ag = class {
constructor(e, t8, o, n, s) {
this.variableNames = ["x"], this.outputShape = [];
let a = t8, 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 * ${p};
setOutput(val);
}
`;
}
};
var ig = class {
constructor(e, t8, o, n, s) {
this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true;
let a = t8, i = e[3] - 1;
this.outputShape = e;
let p, u = `float(${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 cee = (r) => {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = A().getBool("WEBGL_PACK_NORMALIZATION") ? new ig(n.shape, s, a, i, p) : new ag(n.shape, s, a, i, p);
return t8.runWebGLProgram(u, [n], n.dtype);
};
var _F = { kernelName: Bn, backendName: "webgl", kernelFunc: cee };
var ug = class {
constructor(e, t8, o, n, s) {
this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t8, 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 - ${t8})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t8} + 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 lee = (r) => {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new ug(n.shape, i, p, u, c);
return t8.runWebGLProgram(l, [n, s, a], n.dtype);
};
var $F = { kernelName: ja, backendName: "webgl", kernelFunc: lee };
function EF(r, e, t8, o) {
let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Yr(i, r.dtype, "max", o), u = te({ inputs: { x: p }, attrs: { shape: t8 }, backend: o });
return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u;
}
function yv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = c != null, m = t8.shouldExecuteOnCPU([n]), d = n;
if (l) {
if (m) {
let C = t8.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 = Sp(C, n.shape, n.dtype, c, S);
d = t8.makeTensorInfo(S, n.dtype);
let _ = t8.texData.get(d.dataId);
_.values = k;
} else
d = Cu(n, c, t8);
u = w.getInnerMostAxes(u.length, i);
}
w.assertAxesAreInnerMostDims("max", u, i);
let [f, h] = w.computeOutAndReduceShapes(d.shape, u), g = f;
a && (g = w.expandShapeToKeepDim(f, p));
let x;
if (m) {
let C = t8.texData.get(d.dataId).values, S = VR(C, y.sizeFromShape(h), g, n.dtype);
x = t8.makeTensorInfo(g, n.dtype);
let k = t8.texData.get(x.dataId);
k.values = S;
} else
x = EF(d, h, g, t8);
return l && t8.disposeIntermediateTensorInfo(d), x;
}
var RF = { kernelName: zn, backendName: "webgl", kernelFunc: yv };
var mee = Lc + `
return max(a, b);
`;
var dee = `
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);
` + Xr + `
return result;
`;
var fee = nt({ opSnippet: mee, packedOpSnippet: dee, cpuKernelImpl: WR });
var DF = { kernelName: Vn, backendName: "webgl", kernelFunc: fee };
function hee(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e;
Bs(n, "maxPool");
let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
let c = w.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: t8 });
let l = new Vs(c, "max", false);
return t8.runWebGLProgram(l, [n], n.dtype);
}
var AF = { kernelName: Wn, backendName: "webgl", kernelFunc: hee };
function gee(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new wu(l, "max", false);
return t8.runWebGLProgram(m, [n], n.dtype);
}
var FF = { kernelName: aa, backendName: "webgl", kernelFunc: gee };
var pg = class {
constructor(e) {
this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
let t8 = 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) / ${t8}.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 * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`;
}
};
var cg = class {
constructor(e) {
this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
let t8 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterDepth, u = e.effectiveFilterHeight, c = e.effectiveFilterWidth, l = p - 1 - e.padInfo.front, m = u - 1 - e.padInfo.top, d = c - 1 - e.padInfo.left, f = p * u * c - 1;
this.userCode = `
const ivec3 pads = ivec3(${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) / ${t8}.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 xee(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new wu(m, "max", true), f = t8.runWebGLProgram(d, [a], a.dtype), h = new cg(m), g = t8.runWebGLProgram(h, [n, f], a.dtype);
return t8.disposeIntermediateTensorInfo(f), g;
}
var PF = { kernelName: Ui, backendName: "webgl", kernelFunc: xee };
function yee(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s;
Bs([s, a], "maxPoolGrad");
let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = true, f = new Vs(m, "max", d), h = t8.runWebGLProgram(f, [i], i.dtype), g = new pg(m), x = t8.runWebGLProgram(g, [n, h], i.dtype);
return t8.disposeIntermediateTensorInfo(h), x;
}
var OF = { kernelName: Wi, backendName: "webgl", kernelFunc: yee };
function MF(r, e, t8, o) {
let n = new Vs(t8, "max", false), s = o.runWebGLProgram(n, [r], "float32");
n = new Vs(t8, "max", true, true, e);
let a = o.runWebGLProgram(n, [r], "float32");
return [s, a];
}
var LF = { kernelName: ia, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => {
let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t8;
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(w.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);
let c = w.computePool2DInfo(o.shape, n, s, u, a), [l, m] = MF(o, i, c, p);
return [l, m];
} };
function BF(r, e, t8, o) {
let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Yr(i, "float32", "mean", o), u = te({ inputs: { x: p }, attrs: { shape: t8 }, backend: o });
return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u;
}
var zF = { kernelName: Un, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => {
let { x: o } = r, { keepDims: n, axis: s } = e, a = t8, i = o.shape.length, p = y.parseAxisParam(s, o.shape), u = p, c = w.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 _ = Sp(S, o.shape, o.dtype, c, k);
f = a.makeTensorInfo(k, o.dtype);
let E = a.texData.get(f.dataId);
E.values = _;
} else
f = Cu(o, c, a);
d.push(f), u = w.getInnerMostAxes(u.length, i);
}
w.assertAxesAreInnerMostDims("sum", u, i);
let [h, g] = w.computeOutAndReduceShapes(f.shape, u), x = h;
n && (x = w.expandShapeToKeepDim(h, p));
let b = BF(f, g, x, a);
for (let C of d)
a.disposeIntermediateTensorInfo(C);
return b;
} };
function bee(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n;
c != null && (l = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, n.shape.length)), w.assertAxesAreInnerMostDims("min", u, i);
let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t8, attrs: { shape: [-1, f] } }), g = Yr(h, h.dtype, "min", t8), x;
if (a) {
let b = w.expandShapeToKeepDim(m, p);
x = te({ inputs: { x: g }, backend: t8, attrs: { shape: b } });
} else
x = te({ inputs: { x: g }, backend: t8, attrs: { shape: m } });
return t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(g), c != null && t8.disposeIntermediateTensorInfo(l), x;
}
var VF = { kernelName: Gn, backendName: "webgl", kernelFunc: bee };
var Cee = Lc + `
return min(a, b);
`;
var wee = `
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);
` + Xr + `
return result;
`;
var See = nt({ opSnippet: Cee, packedOpSnippet: wee, cpuKernelImpl: UR });
var WF = { kernelName: Hn, backendName: "webgl", kernelFunc: See };
var lg = class {
constructor(e, t8, o) {
this.variableNames = ["x"], this.outputShape = t8.map((c, l) => c[0] + e[l] + c[1]);
let n = e.length, s = Re(n), a = t8.map((c) => c[0]).join(","), i = t8.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 mg = class {
constructor(e, t8, o) {
this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t8.map((f, h) => f[0] + e[h] + f[1]);
let n = e.length, s = Re(n), a = t8.map((f) => f[0]).join(","), i = t8.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 Iee = ({ inputs: r, backend: e, attrs: t8 }) => {
let { x: o } = r, { paddings: n, mode: s } = t8, a = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new mg(o.shape, n, s) : new lg(o.shape, n, s);
return e.runWebGLProgram(a, [o], o.dtype);
};
var UF = { kernelName: Kn, backendName: "webgl", kernelFunc: Iee };
var vee = `if (b == 0.0) return NAN;
return mod(a, b);`;
var kee = `
vec4 result = mod(a, b);
bvec4 isNaN = equal(b, vec4(0.0));
` + Xr + `
return result;
`;
var Nee = nt({ opSnippet: vee, packedOpSnippet: kee });
var GF = { kernelName: qn, backendName: "webgl", kernelFunc: Nee };
var dg = class {
constructor(e, t8, 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 < ${t8 - 1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t8 - 1}));
}
`;
}
};
var Tee = `
if (a == b) {
return 1.0;
};
return a / b;`;
var _ee = `
// 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 bv = nt({ opSnippet: Tee, packedOpSnippet: _ee, checkOutOfBounds: true });
var HF = { kernelName: fn, backendName: "webgl", kernelFunc: bv };
var KF = "return a - b;";
var Cv = nt({ opSnippet: KF, packedOpSnippet: KF, supportsComplex: true, cpuKernelImpl: cD });
var qF = { kernelName: Ts, backendName: "webgl", kernelFunc: Cv };
function wv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = yv({ inputs: { x: n }, backend: t8, attrs: { reductionIndices: a, keepDims: false } }), p = w.expandShapeToKeepDim(i.shape, a), u = te({ inputs: { x: i }, backend: t8, attrs: { shape: p } }), c = Cv({ inputs: { a: n, b: u }, backend: t8 }), l = hv({ inputs: { x: c }, backend: t8 }), m = Ip({ inputs: { x: l }, backend: t8, attrs: { axis: a, keepDims: false } }), d = te({ inputs: { x: m }, backend: t8, attrs: { shape: p } }), f = bv({ inputs: { a: l, b: d }, backend: t8 });
return t8.disposeIntermediateTensorInfo(i), t8.disposeIntermediateTensorInfo(u), t8.disposeIntermediateTensorInfo(c), t8.disposeIntermediateTensorInfo(l), t8.disposeIntermediateTensorInfo(m), t8.disposeIntermediateTensorInfo(d), f;
}
var jF = { kernelName: Is, backendName: "webgl", kernelFunc: wv };
function $ee(r) {
let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : wv({ inputs: { logits: n }, backend: t8, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new dg(u, c, s), m = [[a]], d = t8.runWebGLProgram(l, [p], "int32", m);
return i || t8.disposeIntermediateTensorInfo(p), d;
}
var XF = { kernelName: jn, backendName: "webgl", kernelFunc: $ee };
var Eee = Ut + `
return -x;
`;
var Ree = `
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 Dee(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
if (t8.shouldExecuteOnCPU([o])) {
let s = t8.texData.get(o.dataId), [a, i] = HR(s.values, o.shape, o.dtype);
return t8.makeTensorInfo(i, o.dtype, a);
}
let n;
return A().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Fr(o.shape, Ree) : n = new rr(o.shape, Eee), t8.runWebGLProgram(n, [o], o.dtype);
}
var YF = { kernelName: ua, backendName: "webgl", kernelFunc: Dee };
var Aee = Wt.nonMaxSuppressionV3Impl;
function Fee(r) {
w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t8.readSync(n.dataId), c = t8.readSync(s.dataId), { selectedIndices: l } = Aee(u, c, a, i, p);
return t8.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var QF = { kernelName: Qn, backendName: "webgl", kernelFunc: Fee };
var Pee = Wt.nonMaxSuppressionV4Impl;
function Oee(r) {
w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o, c = t8.readSync(n.dataId), l = t8.readSync(s.dataId), { selectedIndices: m, validOutputs: d } = Pee(c, l, a, i, p, u);
return [t8.makeTensorInfo([m.length], "int32", new Int32Array(m)), t8.makeTensorInfo([], "int32", new Int32Array([d]))];
}
var ZF = { kernelName: Xa, backendName: "webgl", kernelFunc: Oee };
var Mee = Wt.nonMaxSuppressionV5Impl;
function Lee(r) {
w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t8.readSync(n.dataId), l = t8.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Mee(c, l, m, d, f, h);
return [t8.makeTensorInfo([g.length], "int32", new Int32Array(g)), t8.makeTensorInfo([x.length], "float32", new Float32Array(x))];
}
var JF = { kernelName: Zn, backendName: "webgl", kernelFunc: Lee };
var fg = class {
constructor(e, t8, o, n) {
this.variableNames = ["indices"], this.outputShape = [e, t8], this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${n}), float(${o}),
float(index == coords.y)));
}
`;
}
};
var Bee = (r) => {
let { inputs: e, backend: t8, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new fg(u, a, i, p), l = te({ inputs: { x: n }, backend: t8, attrs: { shape: [u] } }), m = t8.runWebGLProgram(c, [l], s);
t8.disposeIntermediateTensorInfo(l);
let d = [...n.shape, a], f = te({ inputs: { x: m }, backend: t8, attrs: { shape: d } });
return t8.disposeIntermediateTensorInfo(m), f;
};
var e3 = { kernelName: Jn, backendName: "webgl", kernelFunc: Bee };
function rm(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
if (o.dtype === "complex64") {
let n = bi({ inputs: { input: o }, backend: t8 }), s = rm({ inputs: { x: n }, backend: t8 }), a = kp({ inputs: { input: o }, backend: t8 }), i = rm({ inputs: { x: a }, backend: t8 }), p = Or({ inputs: { real: s, imag: i }, backend: t8 });
return t8.disposeIntermediateTensorInfo(n), t8.disposeIntermediateTensorInfo(s), t8.disposeIntermediateTensorInfo(a), t8.disposeIntermediateTensorInfo(i), p;
} else
return Ci({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t8 });
}
var t3 = { kernelName: ba, backendName: "webgl", kernelFunc: rm };
function r3(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
if (o.dtype === "string")
throw new Error("onesLike is not supported under string dtype");
if (o.dtype === "complex64") {
let n = bi({ inputs: { input: o }, backend: t8 }), s = r3({ inputs: { x: n }, backend: t8 }), a = kp({ inputs: { input: o }, backend: t8 }), i = rm({ inputs: { x: a }, backend: t8 }), p = Or({ inputs: { real: s, imag: i }, backend: t8 });
return t8.disposeIntermediateTensorInfo(n), t8.disposeIntermediateTensorInfo(s), t8.disposeIntermediateTensorInfo(a), t8.disposeIntermediateTensorInfo(i), p;
} else
return Ci({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t8 });
}
var o3 = { kernelName: pa, backendName: "webgl", kernelFunc: r3 };
function zee(r) {
let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o;
if (e.length === 1)
return Zh({ inputs: { input: e[0] }, backend: t8, 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 = Zh({ inputs: { input: c }, backend: t8, attrs: { dim: n } });
return i.push(l), l;
}), u = fv({ inputs: p, backend: t8, attrs: { axis: n } });
return i.forEach((c) => t8.disposeIntermediateTensorInfo(c)), u;
}
var n3 = { kernelName: ca, backendName: "webgl", kernelFunc: zee };
var hg = class {
constructor(e, t8, o) {
this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t8.map((u, c) => u[0] + e[c] + u[1]);
let n = e.length, s = Re(n), a = t8.map((u) => u[0]).join(","), i = t8.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 gg = class {
constructor(e, t8, o) {
this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t8.map((h, g) => h[0] + e[g] + h[1]);
let n = e.length, s = Re(n), a = t8.map((h) => h[0]).join(","), i = t8.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 Sv = (r) => {
let { inputs: e, backend: t8, 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 Ci({ backend: t8, attrs: { shape: u, value: a, dtype: n.dtype } });
}
let i = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new gg(n.shape, s, a) : new hg(n.shape, s, a), p = [[a]];
return t8.runWebGLProgram(i, [n], n.dtype, p);
};
var s3 = { kernelName: es, backendName: "webgl", kernelFunc: Sv };
var Vee = `
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 Wee = `
// 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);
` + Xr + `
return result;
`;
var Uee = nt({ opSnippet: Vee, packedOpSnippet: Wee });
var a3 = { kernelName: ts, backendName: "webgl", kernelFunc: Uee };
function Gee(r) {
let { inputs: e, backend: t8, 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 = w.getAxesPermutation(c, i), m = n;
l != null && (m = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: l } }), c = w.getInnerMostAxes(c.length, i), p.push(m)), w.assertAxesAreInnerMostDims("prod", c, i);
let d;
if (t8.shouldExecuteOnCPU([m])) {
let f = t8.texData.get(m.dataId).values, { outVals: h, outShape: g, outDtype: x } = qR(m.shape, m.dtype, f, c);
d = t8.makeTensorInfo(g, x, h);
} else {
let [f, h] = w.computeOutAndReduceShapes(m.shape, c), g = y.sizeFromShape(h), x = te({ inputs: { x: m }, backend: t8, attrs: { shape: [-1, g] } }), b = ti(n.dtype), C = Yr(x, b, "prod", t8);
d = te({ inputs: { x: C }, backend: t8, attrs: { shape: f } }), p.push(x), p.push(C);
}
if (a) {
p.push(d);
let f = w.expandShapeToKeepDim(d.shape, u);
d = te({ inputs: { x: d }, backend: t8, attrs: { shape: f } });
}
return p.forEach((f) => t8.disposeIntermediateTensorInfo(f)), d;
}
var i3 = { kernelName: os, backendName: "webgl", kernelFunc: Gee };
function Hee(r) {
let { inputs: e, backend: t8, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t8.readSync(x.dataId)), u = n.map((x) => x.shape), c = t8.readSync(s.dataId), l = t8.readSync(a.dataId), [m, d, f] = jR(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t8.makeTensorInfo([x.length], "int32", x)), g = t8.makeTensorInfo(f, s.dtype, d);
return h.concat([g]);
}
var u3 = { kernelName: jp, backendName: "webgl", kernelFunc: Hee };
function Kee(r) {
let { inputs: e, backend: t8 } = r, { starts: o, limits: n, deltas: s } = e, a = t8.readSync(o.dataId), i = t8.readSync(n.dataId), p = t8.readSync(s.dataId), [u, c] = XR(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t8.makeTensorInfo([u.length], "int32", u), m = t8.makeTensorInfo([c.length], o.dtype, c);
return [l, m];
}
var p3 = { kernelName: Xp, backendName: "webgl", kernelFunc: Kee };
function qee(r) {
let { inputs: e, backend: t8, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t8.readSync(n.dataId), c = t8.readSync(s.dataId), l = t8.readSync(a.dataId), m = i.map((g) => t8.readSync(g.dataId)), d = i.map((g) => g.shape), [f, h] = YR(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p);
return t8.makeTensorInfo(f, s.dtype, h);
}
var c3 = { kernelName: Yp, backendName: "webgl", kernelFunc: qee };
var Iv = (r) => {
let { backend: e, attrs: t8 } = r, { start: o, stop: n, step: s, dtype: a } = t8, i = QR(o, n, s, a);
return e.makeTensorInfo([i.length], a, i);
};
var l3 = { kernelName: la, backendName: "webgl", kernelFunc: Iv };
var jee = "return 1.0 / x;";
var Xee = xe({ opSnippet: jee });
var m3 = { kernelName: ns, backendName: "webgl", kernelFunc: Xee };
var Yee = Ut + `
return (x < 0.0) ? 0.0 : x;
`;
var Qee = `
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 Zee = xe({ opSnippet: Yee, packedOpSnippet: Qee });
var d3 = { kernelName: ss, backendName: "webgl", kernelFunc: Zee };
var Jee = Ut + `
return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var ete = `
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 tte = xe({ opSnippet: Jee, packedOpSnippet: ete });
var f3 = { kernelName: us, backendName: "webgl", kernelFunc: tte };
var xg = class {
constructor(e, t8, o, n, s) {
this.variableNames = ["A"], this.outputShape = [];
let [a, i, p, u] = e;
this.outputShape = [a, t8, o, u];
let c = [n && t8 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t8 > 1 ? t8 - 1 : t8, 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 yg = class {
constructor(e, t8, o, n, s) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
let [a, i, p, u] = e;
this.outputShape = [a, t8, o, u];
let c = [n && t8 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t8 > 1 ? t8 - 1 : t8, 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 rte(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = A().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new yg(n.shape, p, u, s, a) : new xg(n.shape, p, u, s, a);
return t8.runWebGLProgram(c, [n], "float32");
}
var h3 = { kernelName: is, backendName: "webgl", kernelFunc: rte };
var bg = class {
constructor(e, t8, o) {
this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t8;
let [, n, s] = t8, [, 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 ote(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new bg(s.shape, n.shape, a);
return t8.runWebGLProgram(i, [s], s.dtype);
}
var g3 = { kernelName: Qa, backendName: "webgl", kernelFunc: ote };
var Cg = class {
constructor(e, t8, o, n, s) {
this.variableNames = ["A"], this.outputShape = [];
let [a, i, p, u] = e;
this.outputShape = [a, t8, o, u];
let c = [n && t8 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t8 > 1 ? t8 - 1 : t8, 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 wg = class {
constructor(e, t8, o, n, s) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
let [a, i, p, u] = e;
this.outputShape = [a, t8, o, u];
let c = [n && t8 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t8 > 1 ? t8 - 1 : t8, 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 < ${o - 1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`;
}
};
function nte(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = A().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new wg(n.shape, p, u, s, a) : new Cg(n.shape, p, u, s, a);
return t8.runWebGLProgram(c, [n], n.dtype);
}
var x3 = { kernelName: as, backendName: "webgl", kernelFunc: nte };
var Sg = class {
constructor(e, t8, o) {
this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t8;
let [, n, s] = t8, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2;
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${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),
${o} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`;
}
};
function ste(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new Sg(s.shape, n.shape, a);
return t8.runWebGLProgram(i, [s], s.dtype);
}
var y3 = { kernelName: Ya, backendName: "webgl", kernelFunc: ste };
var Ig = class {
constructor(e, t8) {
this.variableNames = ["x"];
let o = e.length;
if (o > 4)
throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);
if (this.outputShape = e, o === 1) {
this.userCode = `
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;
return;
}
let n = (i) => t8.indexOf(i) !== -1 && e[i] !== 1 ? `${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 vg = class {
constructor(e, t8) {
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 = ${l(n.slice())};
}
}
setOutput(result);
}
`;
function p(f) {
return m(f);
}
function u(f) {
return f[o - 1] = "(" + f[o - 1] + " + 1)", m(f);
}
function c(f) {
return f[o - 2] = "(" + f[o - 2] + " + 1)", m(f);
}
function l(f) {
return f[o - 1] = "(" + f[o - 1] + " + 1)", f[o - 2] = "(" + f[o - 2] + " + 1)", m(f);
}
function m(f) {
let h = e.map((b, C) => d(C, f)), g = h.join(","), x = h.slice(-2).join(",");
return `getChannel(getX(${g}), vec2(${x}))`;
}
function d(f, h) {
return t8.indexOf(f) !== -1 && e[f] !== 1 ? `${e[f]} - ${h[f]} - 1` : `${h[f]}`;
}
}
};
function ate(r) {
let { inputs: e, backend: t8, 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: t8 });
let p = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new vg(n.shape, i) : new Ig(n.shape, i);
return t8.runWebGLProgram(p, [n], n.dtype);
}
var b3 = { kernelName: ps, backendName: "webgl", kernelFunc: ate };
var kg = class {
constructor(e, t8) {
this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }];
let o = e[1], n = e[2];
this.outputShape = e;
let s = "";
typeof t8 == "number" ? s = `float outputValue = ${t8.toFixed(2)};` : s = `
vec3 fill = vec3(${t8.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 < ${o}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`;
}
};
var C3 = { kernelName: Ds, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => {
let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t8, p = new kg(o.shape, s), [u, c] = w.getImageCenter(a, o.shape[1], o.shape[2]), l = [[u, c, Math.sin(n), Math.cos(n)]];
return i.runWebGLProgram(p, [o], o.dtype, l);
} };
var ite = `
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`;
var ute = xe({ opSnippet: ite });
var w3 = { kernelName: cs, backendName: "webgl", kernelFunc: ute };
var pte = "return inversesqrt(x);";
var cte = xe({ opSnippet: pte, cpuKernelImpl: ZR });
var S3 = { kernelName: ls, backendName: "webgl", kernelFunc: cte };
var Su = class {
constructor(e, t8, o, n, s, a, i = true, p = false) {
this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a;
let u = Re(s.length), c = Re(a.length), l = "";
o === 1 ? l = "i" : o === 2 && (l = "i, j");
let m = `getIndices(${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 = t8 > 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 < ${t8}; j++) {
int index = round(${m});
flattenedIndex += index * ${x};
}
if (flattenedIndex == coords[0]) {
sum += ${f};
found = true;
}
}
setOutput(mix(${g}, sum, float(found)));
}
`;
}
};
var Ng = class {
constructor(e, t8, 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 = t8 > 1 ? "strides[j]" : "strides", b = t8 > 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 < ${t8}; j+=2) {
ivec4 index = round(${m});
flattenedIndex += index.xz * ${x};
if (j + 1 < ${t8}) {
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 lte(r) {
let { inputs: e, backend: t8, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = [l / u, u];
if (l === 0)
return t8.makeTensorInfo(a, n.dtype);
let d = te({ inputs: { x: n }, backend: t8, attrs: { shape: [p, i] } }), f = te({ inputs: { x: s }, backend: t8, attrs: { shape: [p, u] } }), h = t8.makeTensorInfo([], "float32", new Float32Array([0])), g;
A().getBool("WEBGL_PACK") ? g = new Ng(p, i, d.shape.length, f.shape.length, c, m) : g = new Su(p, i, d.shape.length, f.shape.length, c, m);
let x = t8.runWebGLProgram(g, [f, d, h], f.dtype), b = te({ inputs: { x }, backend: t8, attrs: { shape: a } });
return t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), t8.disposeIntermediateTensorInfo(x), t8.disposeIntermediateTensorInfo(h), b;
}
var I3 = { kernelName: ms, backendName: "webgl", kernelFunc: lte };
var Tg = class {
constructor(e, t8, 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(t8 + 1))}; ++i) { if (left >= right) break;`, i = A().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 mte(r) {
let { inputs: e, backend: t8, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new Tg(n.shape[0], n.shape[1], s.shape[1], a), p = [[n.shape[1]]];
return t8.runWebGLProgram(i, [n, s], "int32", p);
}
var v3 = { kernelName: fs, backendName: "webgl", kernelFunc: mte };
var _g = class {
constructor(e, t8, o) {
this.variableNames = ["c", "a", "b"], this.outputShape = t8;
let n, s;
if (o > 4)
throw Error(`Where for rank ${o} is not yet supported`);
if (o === 1)
s = "resRC", n = "resRC";
else {
let i = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], p = [], u = [];
for (let c = 0; c < t8.length; c++)
u.push(`${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 dte(r) {
let { inputs: e, backend: t8 } = r, { condition: o, t: n, e: s } = e, a = new _g(o.shape.length, n.shape, n.shape.length);
return t8.runWebGLProgram(a, [o, n, s], dt(n.dtype, s.dtype));
}
var k3 = { kernelName: da, backendName: "webgl", kernelFunc: dte };
var fte = `
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${w.SELU_SCALEALPHA};
float scale = ${w.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;
var hte = xe({ opSnippet: fte });
var N3 = { kernelName: hs, backendName: "webgl", kernelFunc: hte };
var gte = Fo + `
return 1.0 / (1.0 + exp(-1.0 * x));
`;
var xte = `
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 yte = xe({ opSnippet: gte, packedOpSnippet: xte, cpuKernelImpl: eD });
var T3 = { kernelName: bs, backendName: "webgl", kernelFunc: yte };
var bte = `
if (isnan(x)) { return 0.0; }
return sign(x);
`;
var Cte = xe({ opSnippet: bte });
var _3 = { kernelName: ys, backendName: "webgl", kernelFunc: Cte };
var wte = Fo + `
return sin(x);
`;
var Ste = `
vec4 result = sin(x);
bvec4 isNaN = isnan(x);
${Xr}
return result;
`;
var Ite = xe({ opSnippet: wte, packedOpSnippet: Ste });
var $3 = { kernelName: gs, backendName: "webgl", kernelFunc: Ite };
var vte = `
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`;
var kte = xe({ opSnippet: vte });
var E3 = { kernelName: xs, backendName: "webgl", kernelFunc: kte };
var Nte = `
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 Tte = xe({ opSnippet: Nte });
var R3 = { kernelName: Cs, backendName: "webgl", kernelFunc: Tte };
var _te = (r) => {
let { inputs: e, backend: t8, 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 = Sv({ inputs: { x: n }, backend: t8, attrs: { paddings: p, constantValue: 0 } }), l = w.getReshaped(c.shape, s, i, false), m = w.getPermuted(l.length, s.length, false), d = w.getReshapedPermuted(c.shape, s, i, false), f = te({ inputs: { x: c }, backend: t8, attrs: { shape: l } }), h = Ct({ inputs: { x: f }, backend: t8, attrs: { perm: m } }), g = te({ inputs: { x: h }, backend: t8, attrs: { shape: d } });
return u.push(c), u.push(f), u.push(h), u.forEach((x) => t8.disposeIntermediateTensorInfo(x)), g;
};
var D3 = { kernelName: ha, backendName: "webgl", kernelFunc: _te };
function $te(r) {
let { inputs: e, backend: t8 } = 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 = t8.readSync(o.dataId), p = t8.readSync(n.dataId), u = t8.readSync(s.dataId), c = t8.readSync(a.dataId)[0], [l, m, d, f, h] = rD(i, o.shape, o.dtype, p, n.dtype, u, c);
return [t8.makeTensorInfo(m, o.dtype, l), t8.makeTensorInfo([m[0]], n.dtype, d), t8.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t8.makeTensorInfo([h.length], o.dtype, new Int32Array(h))];
}
var A3 = { kernelName: Hi, backendName: "webgl", kernelFunc: $te };
function Ete(r) {
let { inputs: e, backend: t8 } = 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(t8.readSync(n.dataId)), i = t8.readSync(o.dataId), p = Array.from(t8.readSync(s.dataId)), [u, c, l] = oD(i, o.shape, o.dtype, a, p);
return [t8.makeTensorInfo(c, o.dtype, u), t8.makeTensorInfo([l.length], s.dtype, new Int32Array(l))];
}
var F3 = { kernelName: Za, backendName: "webgl", kernelFunc: Ete };
function Rte(r) {
let { inputs: e, backend: t8 } = 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 = t8.readSync(o.dataId), i = t8.readSync(n.dataId), p = t8.readSync(s.dataId), [u, c] = ph(a, o.shape, o.dtype, i, p, true);
return t8.makeTensorInfo(c, o.dtype, u);
}
var P3 = { kernelName: Ki, backendName: "webgl", kernelFunc: Rte };
function Dte(r) {
let { inputs: e, backend: t8 } = 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 = t8.readSync(o.dataId), i = t8.readSync(n.dataId), p = t8.readSync(s.dataId), [u, c] = ph(a, o.shape, o.dtype, i, p);
return t8.makeTensorInfo(c, o.dtype, u);
}
var O3 = { kernelName: qi, backendName: "webgl", kernelFunc: Dte };
function Ate(r) {
let { inputs: e, backend: t8, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false;
if (s.dtype === "string") {
let x = t8.bufferSync(n), b = t8.bufferSync(s), C = y.decodeString(t8.readSync(a.dataId)[0]), S = JR(x, b, i, m, c, u, p, l, C, d);
return t8.makeTensorInfo(i, S.dtype, S.values);
}
let f = new Su(u, p, n.shape.length, s.shape.length, l, [m, 1], d), h = t8.runWebGLProgram(f, [s, n, a], s.dtype), g = te({ inputs: { x: h }, backend: t8, attrs: { shape: i } });
return t8.disposeIntermediateTensorInfo(h), g;
}
var M3 = { kernelName: vs, backendName: "webgl", kernelFunc: Ate };
function Fte(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.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 = Ws({ inputs: { x: n }, backend: t8, attrs: { begin: c, size: d } });
return c[i] += m, f;
});
}
var L3 = { kernelName: ga, backendName: "webgl", kernelFunc: Fte };
var B3 = "return sqrt(x);";
var Pte = xe({ opSnippet: B3, packedOpSnippet: B3, cpuKernelImpl: nD });
var z3 = { kernelName: ws, backendName: "webgl", kernelFunc: Pte };
var Ote = "return x * x;";
var Mte = xe({ opSnippet: Ote });
var V3 = { kernelName: ji, backendName: "webgl", kernelFunc: Mte };
var W3 = "return (a - b) * (a - b);";
var Lte = nt({ opSnippet: W3, packedOpSnippet: W3 });
var U3 = { kernelName: ks, backendName: "webgl", kernelFunc: Lte };
function Bte(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e;
if (n.dtype !== "string")
throw new Error("Input must be of datatype string");
let s = t8.readSync(n.dataId), a = w.fromUint8ToStringArray(s), i = sD(a, "string", o);
return t8.makeTensorInfo(n.shape, "string", i);
}
var G3 = { kernelName: Du, backendName: "webgl", kernelFunc: Bte };
function zte({ inputs: r, attrs: e, backend: t8 }) {
let { x: o } = r, n = Ut + `
return x > 0.0 ? 1.0 : float(${e.alpha});
`, s = new rr(o.shape, n);
return t8.runWebGLProgram(s, [o], o.dtype);
}
var H3 = { kernelName: wo, backendName: "webgl", kernelFunc: zte };
var $g = class {
constructor(e, t8, o) {
this.variableNames = ["x"], this.outputShape = o;
let n = o.length, s = Re(o.length), a = Re(o.length), i = "";
if (n === 1)
i = "coords * strides + begin";
else {
let p = 0;
i = o.map((u, c) => (p++, o.length === 1 ? `coords * strides[${c}] + begin[${c}]` : `coords[${p - 1}] * strides[${c}] + begin[${c}]`)).join(",");
}
this.userCode = `
${s} begin = ${s}(${e});
${s} strides = ${s}(${t8});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${i}));
}
`;
}
};
function Vte(r) {
let { inputs: e, backend: t8, 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: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
if (h)
k = te({ inputs: { x: n }, backend: t8, 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, C, S), R = Ws({ inputs: { x: n }, backend: t8, attrs: { begin: b, size: E } });
k = te({ inputs: { x: R }, backend: t8, attrs: { shape: f } }), t8.disposeIntermediateTensorInfo(R);
} else if (t8.shouldExecuteOnCPU([n])) {
let R = t8.readSync(n.dataId), D = me(n.shape, n.dtype, R), P = aD(d, D, S, b);
k = t8.makeTensorInfo(f, n.dtype, P.values);
} else {
let R = new $g(b, S, d);
k = t8.runWebGLProgram(R, [n], n.dtype);
}
let _ = te({ inputs: { x: k }, backend: t8, attrs: { shape: f } });
return t8.disposeIntermediateTensorInfo(k), _;
}
var K3 = { kernelName: Ns, backendName: "webgl", kernelFunc: Vte };
function Wte(r) {
let { inputs: e, backend: t8, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t8.readSync(c.dataId), d = t8.readSync(l.dataId), [f, h] = iD(m, d, n, s, a, i, p, u);
return [t8.makeTensorInfo([f.length], "string", f), t8.makeTensorInfo(l.shape, "int32", h)];
}
var q3 = { kernelName: xa, backendName: "webgl", kernelFunc: Wte };
function Ute(r) {
let { inputs: e, backend: t8, 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 = t8.readSync(s.dataId), p = t8.readSync(a.dataId)[0], [u, c, l] = uD(i, p, n), m = c.length;
return [t8.makeTensorInfo([m, 2], "int32", u), t8.makeTensorInfo([m], "string", c), t8.makeTensorInfo([2], "int32", new Int32Array(l))];
}
var j3 = { kernelName: Xi, backendName: "webgl", kernelFunc: Ute };
function Gte(r) {
let { inputs: e, backend: t8, 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 = t8.readSync(s.dataId), i = pD(a, n);
return t8.makeTensorInfo(s.shape, "int32", i);
}
var X3 = { kernelName: Yi, backendName: "webgl", kernelFunc: Gte };
var Hte = "return tan(x);";
var Kte = xe({ opSnippet: Hte });
var Y3 = { kernelName: _s, backendName: "webgl", kernelFunc: Kte };
var qte = `
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`;
var jte = xe({ opSnippet: qte });
var Q3 = { kernelName: $s, backendName: "webgl", kernelFunc: jte };
function Xte(r) {
let { inputs: e, backend: t8, attrs: o } = r, { tensor: n, indices: s, updates: a } = e, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(a, s, n.shape), m = [l / u, u];
if (l === 0)
return t8.makeTensorInfo(n.shape, s.dtype);
let d = te({ inputs: { x: s }, backend: t8, attrs: { shape: [p, i] } }), f = te({ inputs: { x: a }, backend: t8, attrs: { shape: [p, u] } }), h = te({ inputs: { x: n }, backend: t8, attrs: { shape: m } }), g = new Su(p, i, d.shape.length, f.shape.length, c, m, false, true), x = t8.runWebGLProgram(g, [f, d, h], h.dtype), b = te({ inputs: { x }, backend: t8, attrs: { shape: n.shape } });
return t8.disposeIntermediateTensorInfo(d), t8.disposeIntermediateTensorInfo(f), t8.disposeIntermediateTensorInfo(h), t8.disposeIntermediateTensorInfo(x), b;
}
var Z3 = { kernelName: ds, backendName: "webgl", kernelFunc: Xte };
var Eg = class {
constructor(e, t8) {
this.variableNames = ["A"];
let o = new Array(e.length);
for (let a = 0; a < o.length; a++)
o[a] = e[a] * t8[a];
this.outputShape = o, this.rank = o.length;
let n = Re(this.rank), s = Yte(e);
this.userCode = `
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`;
}
};
function Yte(r) {
let e = r.length;
if (e > 5)
throw Error(`Tile for rank ${e} is not yet supported`);
if (e === 1)
return `imod(resRC, ${r[0]})`;
let t8 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"], o = [];
for (let n = 0; n < r.length; n++)
o.push(`imod(${t8[n]}, ${r[n]})`);
return o.join();
}
function vv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reps: s } = o;
if (n.dtype === "string" || n.shape.length > 5) {
let p = t8.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = lD(c, s);
return t8.makeTensorInfo(l.shape, l.dtype, l.values);
}
let a = new Eg(n.shape, s);
return t8.runWebGLProgram(a, [n], n.dtype);
}
var J3 = { kernelName: uo, backendName: "webgl", kernelFunc: vv };
var Rg = 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 Dg = 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 Tp(r, e) {
e !== null && r.disposeIntermediateTensorInfo(e);
}
function eP(r) {
let e = 1;
for (; e < r; )
e *= 2;
return e;
}
function Qte(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = A().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), p = A().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), u = n.shape, c = u[u.length - 1];
if (t8.shouldExecuteOnCPU([n]) || c < i || s > p) {
let P = t8.readSync(n.dataId), [O, M] = mD(P, u, n.dtype, s, a);
return [t8.makeTensorInfo(O.shape, O.dtype, O.values), t8.makeTensorInfo(M.shape, M.dtype, M.values)];
}
if (s === 0)
return u[u.length - 1] = 0, [t8.makeTensorInfo(u, n.dtype, []), t8.makeTensorInfo(u, "int32", [])];
if (c === 1)
return [n, Ci({ attrs: { shape: u, dtype: "int32", value: 0 }, backend: t8 })];
let l = t8.texData.get(n.dataId), m = l !== null && l.isPacked, d = m ? t8.unpackTensor(n) : n, h = y.sizeFromShape(u) / c, g = te({ inputs: { x: d }, attrs: { shape: [h, c] }, backend: t8 });
m && Tp(t8, d);
let x = eP(s), b = eP(c), C = null, S = () => C === null ? [g, g] : [g, C], k = (P, O, M) => {
let L = S(), B = new Rg(M), U = [[c], [C === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [O]], j = C;
C = t8.runWebGLProgram(B, L, "int32", U), Tp(t8, j);
};
for (let P = 1; P < x; P *= 2) {
let O = P * 2;
for (let M = P; M >= 1; M /= 2)
k(O, M, [h, b]);
}
for (let P = b; P > x; P /= 2) {
let O = S(), M = new Dg([h, P / 2]), B = [[c], [C === null ? 1 : 0], [x]], z = C;
C = t8.runWebGLProgram(M, O, "int32", B), Tp(t8, z);
let U = x / 2, j = U * 2;
for (let q = U; q >= 1; q /= 2)
k(j, q, C.shape);
}
let _ = C;
C = Ws({ inputs: { x: C }, backend: t8, attrs: { begin: 0, size: [h, s] } }), Tp(t8, _);
let E = xv({ inputs: { x: g, indices: C }, backend: t8, attrs: { axis: 1, batchDims: 1 } });
Tp(t8, g);
let R = u.slice(0, -1);
R.push(s), _ = C, C = te({ inputs: { x: C }, attrs: { shape: R }, backend: t8 }), Tp(t8, _);
let D = E;
return E = te({ inputs: { x: E }, attrs: { shape: R }, backend: t8 }), Tp(t8, D), [E, C];
}
var tP = { kernelName: Es, backendName: "webgl", kernelFunc: Qte };
var Ag = class {
constructor(e, t8, 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 < ${t8}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${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(${s});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t8}));
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 Zte(r) {
let { inputs: e, backend: t8, 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 Ag(l, m, a, i, p, g);
return t8.runWebGLProgram(x, [n, s], "float32");
}
var rP = { kernelName: Rs, backendName: "webgl", kernelFunc: Zte };
function Jte(r) {
let { inputs: e, attrs: t8, backend: o } = r, { axis: n } = t8, { x: s } = e;
Bs(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 } = dD(a, n, s.shape, s.dtype);
return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)];
}
var oP = { kernelName: Qi, backendName: "webgl", kernelFunc: Jte };
function ere(r) {
let { inputs: e, backend: t8, 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 = Ws({ inputs: { x: a }, backend: t8, attrs: { begin: m, size: d } }), x = te({ inputs: { x: g }, backend: t8, attrs: { shape: u } });
f[h] = x, l.push(g);
}
return l.forEach((h) => t8.disposeIntermediateTensorInfo(h)), f;
}
var nP = { kernelName: ya, backendName: "webgl", kernelFunc: ere };
var Fg = class {
constructor(e, t8) {
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 tre(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = w.getAxesPermutation([u], i), l = n;
c != null && (l = Ct({ inputs: { x: n }, backend: t8, attrs: { perm: c } }), p.push(l), u = w.getInnerMostAxes(1, i)[0]);
let m = w.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = te({ inputs: { x: l }, backend: t8, attrs: { shape: [-1, d] } });
p.push(f);
let h = ti(n.dtype), g = (S, k, _, E, R) => {
let D = S.shape[0], P = S.shape[1], O = w.segment_util.segOpComputeOptimalWindowSize(P, R), M = { windowSize: O, inSize: P, batchSize: D, numSegments: R }, L = new Fg(M, k), B = t8.compileAndRun(L, [S, _], E);
if (p.push(B), B.shape[1] === R)
return B;
let z = Iv({ backend: t8, attrs: { start: 0, stop: R, step: 1, dtype: "float32" } }), U = vv({ inputs: { x: z }, backend: t8, attrs: { reps: [P / 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: t8, attrs: { shape: m } }), C = b;
if (c != null) {
p.push(b);
let S = w.getUndoAxesPermutation(c);
C = Ct({ inputs: { x: C }, backend: t8, attrs: { perm: S } });
}
return p.forEach((S) => t8.disposeIntermediateTensorInfo(S)), C;
}
var sP = { kernelName: Zi, backendName: "webgl", kernelFunc: tre };
var rre = [VD, UD, GD, HD, qD, jD, XD, YD, JD, eA, tA, rA, oA, nA, sA, aA, iA, uA, pA, cA, lA, dA, fA, hA, gA, CA, SA, IA, RD, kA, TA, _A, $A, EA, RA, DA, AA, FA, PA, OA, BA, zA, VA, WA, UA, GA, HA, KA, qA, jA, XA, YA, QA, ZA, JA, eF, rF, oF, nF, sF, iF, uF, pF, cF, lF, mF, dF, fF, hF, ED, gF, NA, xF, yF, bF, DD, CF, wF, SF, IF, vF, kF, NF, TF, _F, $F, RF, DF, AF, FF, PF, OF, LF, zF, VF, WF, UF, GF, XF, PD, YF, QF, ZF, JF, xA, e3, o3, n3, s3, a3, AD, i3, u3, p3, c3, l3, yA, HF, m3, d3, f3, MD, h3, g3, x3, y3, b3, C3, w3, S3, I3, v3, k3, N3, T3, _3, $3, E3, mA, jF, R3, D3, A3, F3, P3, O3, M3, L3, z3, V3, U3, G3, H3, K3, q3, j3, X3, qF, BD, Y3, Q3, Z3, J3, tP, rP, zD, oP, nP, sP, t3];
for (let r of rre)
Ja(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 Iu;
(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";
})(Iu || (Iu = {}));
var aP;
function ore(r) {
aP = r.wasm.cwrap(So, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function nre(r) {
let { inputs: e, backend: t8, 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 = t8.dataIdMap.get(n.dataId).id, d = t8.dataIdMap.get(s.dataId).id, f = 0;
if (a != null) {
let R = t8.dataIdMap.get(a.dataId);
if (R.shape.length !== 1)
throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);
f = R.id;
}
let h = i == null ? 0 : t8.dataIdMap.get(i.dataId).id, g = Iu[c];
if (g == null)
throw new Error(`${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], C = Ir.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)), S = t8.makeOutput([...C, x, b], n.dtype), k = t8.dataIdMap.get(S.dataId).id, _ = new Uint8Array(new Int32Array(n.shape).buffer), E = new Uint8Array(new Int32Array(s.shape).buffer);
return aP(m, _, n.shape.length, d, E, s.shape.length, p, u, g, f, h, l || 0, k), S;
}
var iP = { kernelName: So, backendName: "wasm", setupFunc: ore, kernelFunc: nre };
function he(r, e) {
let t8;
function o(s) {
t8 = 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 || t8(p, we[i.dtype], c), u;
}
return { kernelName: r, backendName: "wasm", setupFunc: o, kernelFunc: n };
}
var uP = he(js);
var pP = he(Vo);
var cP = he(Wo);
function We(r, e, t8) {
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 = t8 != null ? t8 : u.dtype, f = w.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 sre = true;
var lP = We(io, sre);
var mP;
function are(r) {
mP = r.wasm.cwrap(Uo, null, ["array", "number", "number", "number"]);
}
function ire(r) {
let { inputs: e, backend: t8 } = r, o = t8.makeOutput(e[0].shape, e[0].dtype);
if (y.sizeFromShape(o.shape) === 0)
return o;
let n = e.map((i) => t8.dataIdMap.get(i.dataId).id), s = new Uint8Array(new Int32Array(n).buffer), a = t8.dataIdMap.get(o.dataId).id;
return mP(s, n.length, we[o.dtype], a), o;
}
var dP = { kernelName: Uo, backendName: "wasm", setupFunc: are, kernelFunc: ire };
function _p(r) {
let { inputs: { x: e }, backend: t8 } = r;
if (e.dtype === "string")
return ir(t8.readSync(e.dataId), e.shape, e.dtype);
let o = t8.makeOutput(e.shape, e.dtype), n = t8.typedArrayFromHeap(e);
return t8.typedArrayFromHeap(o).set(n), o;
}
var fP = { kernelName: Co, backendName: "wasm", kernelFunc: _p };
var hP;
function ure(r) {
hP = r.wasm.cwrap(po, null, ["number", "array", "number", "number", "number", "array", "number"]);
}
function ho(r) {
let { inputs: e, backend: t8, attrs: o } = r, [n, s] = cre(e.x.shape, o.perm), a = true;
for (let f = 0; f < s.length; f++)
s[f] !== f && (a = false);
let i = pre(e.x.shape, o.perm), p = { dataId: e.x.dataId, shape: n, dtype: e.x.dtype };
if (a) {
let f = _p({ inputs: e, backend: t8 });
return f.shape = i, f;
}
let u = t8.makeOutput(i, p.dtype), c = t8.dataIdMap.get(p.dataId).id, l = t8.dataIdMap.get(u.dataId).id, m = new Uint8Array(new Int32Array(s).buffer), d = new Uint8Array(new Int32Array(p.shape).buffer);
return hP(c, d, p.shape.length, we[p.dtype], l, m, s.length), u;
}
function pre(r, e) {
let t8 = new Array(r.length);
for (let o = 0; o < t8.length; o++)
t8[o] = r[e[o]];
return t8;
}
function cre(r, e) {
let t8 = [], o = [];
for (let n = 0; n < r.length; ++n)
r[n] !== 1 && t8.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 [t8, o];
}
var gP = { kernelName: po, backendName: "wasm", kernelFunc: ho, setupFunc: ure };
function _r(r, e, t8) {
let o = r.shape, n = r.shape.length, s = y.parseAxisParam(e, o), a = s, i = w.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 = w.getInnerMostAxes(a.length, n), p = ho({ inputs: { x: r }, attrs: { perm: i }, backend: t8 });
let l = t8.dataIdMap.get(r.dataId).id;
t8.dataIdMap.get(p.dataId).id !== l && (u = true);
}
return { transposed: p, originalAxes: s, axes: a, inputWasTransposed: u };
}
var xP;
function lre(r) {
xP = r.wasm.cwrap(Go, null, ["number, number, number"]);
}
function mre(r) {
let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e);
if (d) {
let C = e.dataIdMap.get(c.dataId).id;
u = c, p = C;
}
let f = u.shape.length;
w.assertAxesAreInnerMostDims("all", l, f);
let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let C = e.dataIdMap.get(b.dataId).id;
xP(p, x, C);
}
if (d && e.disposeData(c.dataId), s) {
let C = w.expandShapeToKeepDim(b.shape, m);
b.shape = C;
}
return b;
}
var yP = { kernelName: Go, backendName: "wasm", setupFunc: lre, kernelFunc: mre };
var bP;
function dre(r) {
bP = r.wasm.cwrap(Ho, null, ["number, number, number"]);
}
function fre(r) {
let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e);
if (d) {
let C = e.dataIdMap.get(c.dataId).id;
u = c, p = C;
}
let f = u.shape.length;
w.assertAxesAreInnerMostDims("any", l, f);
let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let C = e.dataIdMap.get(b.dataId).id;
bP(p, x, C);
}
if (d && e.disposeData(c.dataId), s) {
let C = w.expandShapeToKeepDim(b.shape, m);
b.shape = C;
}
return b;
}
var CP = { kernelName: Ho, backendName: "wasm", setupFunc: dre, kernelFunc: fre };
function Pg(r) {
let e;
function t8(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 } = _r(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, C = y.sizeFromShape(x.shape), S = m.shape[f[0]];
return e(l, we[m.dtype], C, S, b), h && s.disposeData(d.dataId), x;
}
return { kernelName: r, backendName: "wasm", setupFunc: t8, kernelFunc: o };
}
var wP = Pg(Xs);
var SP = Pg(Ys);
var IP = he(Ko);
var vP = he(qo);
var kP = he(jo);
var NP = We(Yo, false);
var TP = he(Xo);
var _P;
function hre(r) {
_P = r.wasm.cwrap(Qo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function gre(r) {
let { inputs: e, attrs: t8, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t8, c = w.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, C = 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 _P(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, k), S;
}
var $P = { kernelName: Qo, backendName: "wasm", setupFunc: hre, kernelFunc: gre };
var EP;
function xre(r) {
EP = 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 yre(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t8.makeOutput(c.outShape, n.dtype);
return EP(t8.dataIdMap.get(n.dataId).id, t8.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 RP = { kernelName: Qs, backendName: "wasm", setupFunc: xre, kernelFunc: yre };
var DP;
function bre(r) {
DP = 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 Cre(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = t8.makeOutput(s.shape, s.dtype);
return DP(t8.dataIdMap.get(n.dataId).id, t8.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 AP = { kernelName: Ei, backendName: "wasm", setupFunc: bre, kernelFunc: Cre };
var FP;
function wre(r) {
FP = r.wasm.cwrap("AvgPoolGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Sre(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p } = o, u = w.computePool2DInfo(s.shape, a, i, 1, p), c = t8.makeOutput(s.shape, s.dtype);
return FP(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(c.dataId).id, u.batchSize, u.inChannels, u.inHeight, u.inWidth, u.outHeight, u.outWidth, u.strideHeight, u.strideWidth, u.dilationHeight, u.dilationWidth, u.effectiveFilterHeight, u.effectiveFilterWidth, u.padInfo.top, u.padInfo.left, u.filterHeight, u.filterWidth), c;
}
var PP = { kernelName: $i, backendName: "wasm", setupFunc: wre, kernelFunc: Sre };
function zt(r) {
let { inputs: e, attrs: t8 } = r, { x: o } = e, { shape: n } = t8, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s);
return y.assert(s === y.sizeFromShape(a), () => `new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype };
}
var OP = { kernelName: ma, backendName: "wasm", kernelFunc: zt };
var MP;
function Ire(r) {
MP = r.wasm.cwrap(Zo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
}
function vre(r) {
let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
if (n.dtype !== "float32" || s.dtype !== "float32")
throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = Ir.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]);
y.assert(c === l, () => `Error in matMul: inner shapes (${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: t8, attrs: { shape: S } }), E = zt({ inputs: { x: s }, backend: t8, attrs: { shape: k } }), R = t8.dataIdMap.get(_.dataId).id, D = t8.dataIdMap.get(E.dataId).id, P = a ? _.shape[2] : _.shape[1], O = i ? E.shape[1] : E.shape[2], M = Math.max(g, x), L = t8.makeOutput([M, P, O], _.dtype), B = t8.dataIdMap.get(L.dataId).id, z = new Uint8Array(new Int32Array(_.shape).buffer), U = new Uint8Array(new Int32Array(E.shape).buffer);
return MP(R, z, _.shape.length, D, U, E.shape.length, a, i, B), t8.disposeData(_.dataId), t8.disposeData(E.dataId), L.shape = C, L;
}
var LP = { kernelName: Zo, backendName: "wasm", setupFunc: Ire, kernelFunc: vre };
function Po(r) {
let { inputs: { x: e }, attrs: { begin: t8, size: o }, backend: n } = r, [s, a] = ct.parseSliceParams(e, t8, 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 = lp(p, s, a, e.shape, e.dtype);
return l.stringBytes = f, u;
}
let m = n.typedArrayFromHeap(u), d = e.shape.length;
if (d === 2)
kre(p, c[0], m, s, a);
else if (d === 3)
Nre(p, c[0], c[1], m, s, a);
else if (d === 4)
Tre(p, c[0], c[1], c[2], m, s, a);
else {
let f = lp(p, s, a, e.shape, e.dtype);
m.set(f);
}
return u;
}
function kre(r, e, t8, 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;
t8.set(r.subarray(c, c + n[1]), s), s += n[1];
}
}
function Nre(r, e, t8, 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 * t8 + u;
o.set(r.subarray(f, f + s[2]), a), a += s[2];
}
}
function Tre(r, e, t8, 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 * t8 + x * o + f;
n.set(r.subarray(b, b + a[3]), i), i += a[3];
}
}
var BP = { kernelName: fa, backendName: "wasm", kernelFunc: Po };
function _re(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o, i = s.reduce((x, b) => x * b), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = zt({ inputs: { x: n }, backend: t8, attrs: { shape: p } }), f = ho({ inputs: { x: d }, backend: t8, attrs: { perm: u } }), h = zt({ inputs: { x: f }, backend: t8, attrs: { shape: c } }), g = Po({ inputs: { x: h }, backend: t8, attrs: { begin: l, size: m } });
return t8.disposeData(d.dataId), t8.disposeData(f.dataId), t8.disposeData(d.dataId), g;
}
var zP = { kernelName: Zs, backendName: "wasm", kernelFunc: _re };
var VP;
function $re(r) {
VP = r.wasm.cwrap(Jo, null, ["number", "number", "boolean", "number", "number", "number"]);
}
function Ere(r) {
let { backend: e, inputs: t8, attrs: o } = r, { x: n, weights: s } = t8, { 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 WP = { kernelName: Jo, backendName: "wasm", setupFunc: $re, kernelFunc: Ere };
var Rre = true;
var UP = We(Ha, Rre);
function Dre(r) {
let { inputs: e, backend: t8 } = r, { s0: o, s1: n } = e, s = t8.typedArrayFromHeap(o), a = t8.typedArrayFromHeap(n), i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a));
return t8.makeOutput([i.length], "int32", void 0, new Int32Array(i));
}
var GP = { kernelName: Js, backendName: "wasm", kernelFunc: Dre };
function Mr(r) {
let { inputs: { x: e }, attrs: { dtype: t8 }, backend: o } = r, n = o.makeOutput(e.shape, t8), s = o.typedArrayFromHeap(e);
return o.typedArrayFromHeap(n).set(s), n;
}
var HP = { kernelName: yo, backendName: "wasm", kernelFunc: Mr };
var KP = he(en);
var qP;
function Are(r) {
qP = r.wasm.cwrap(bo, null, ["number", "number", "number", "number"]);
}
function Fre(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i = t8.dataIdMap.get(n.dataId).id, p = t8.makeOutput(n.shape, n.dtype), u = t8.dataIdMap.get(p.dataId).id;
return qP(i, s, a, u), p;
}
var jP = { kernelName: bo, backendName: "wasm", setupFunc: Are, kernelFunc: Fre };
function kv(r) {
let { inputs: e, backend: t8 } = r, o = y.parseAxisParam(r.attrs.axis, e[0].shape)[0], n = e.map((d) => d.shape);
w.assertParamsConsistent(n, o);
let s = w.computeOutShape(e.map((d) => d.shape), o), a = e.filter((d) => y.sizeFromShape(d.shape) > 0);
if (a.length === 1)
return _p({ inputs: { x: a[0] }, backend: t8 });
let i = t8.makeOutput(s, e[0].dtype);
if (y.sizeFromShape(s) === 0)
return i;
if (a[0].dtype === "string") {
let d = a.map((C) => {
let k = [-1, y.sizeFromShape(C.shape.slice(o))];
return zt({ inputs: { x: C }, backend: t8, attrs: { shape: k } });
}), f = d.map((C) => ({ vals: t8.readSync(C.dataId), shape: C.shape }));
s = w.computeOutShape(d.map((C) => C.shape), 1);
let h = d[0].shape[0] === 1, g = up(f, s, e[0].dtype, h), x = w.computeOutShape(a.map((C) => C.shape), o);
i.shape = x;
let b = t8.dataIdMap.get(i.dataId);
return b.stringBytes = w.fromStringArrayToUint8(g), d.forEach((C) => t8.disposeData(C.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) => t8.typedArrayFromHeap(d)), m = t8.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 XP = { kernelName: ea, backendName: "wasm", kernelFunc: kv };
var YP;
function Pre(r) {
YP = r.wasm.cwrap(tn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Ore(r) {
let { inputs: e, attrs: t8, 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 } = t8, d = w.convertConv2DDataFormat(m), f = w.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, C = f.padInfo.bottom, S = f.padInfo.left, k = f.dilationHeight, _ = f.dilationWidth, E = f.strideHeight, R = f.strideWidth, D = f.inChannels, P = 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 YP(a, n.shape[0], n.shape[1], n.shape[2], i, h, g, x, b, C, S, O, k, _, E, R, D, P, L), M;
}
var QP = { kernelName: tn, backendName: "wasm", setupFunc: Pre, kernelFunc: Ore };
var ZP;
function Mre(r) {
ZP = 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", "number", "number", "number"]);
}
function Lre(r) {
let { backend: e, inputs: t8, attrs: o } = r, { dy: n, filter: s } = t8, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, inputShape: c } = o, l = 1, m = w.convertConv2DDataFormat(p), d = w.computeConv2DInfo(c, s.shape, a, l, i, u, false, m), { batchSize: f, filterHeight: h, filterWidth: g, inChannels: x, inHeight: b, inWidth: C, outChannels: S, outHeight: k, outWidth: _, strideHeight: E, strideWidth: R } = d, D = h - 1 - d.padInfo.top, P = 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], q = O ? M[1] : M[2], Y = 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, be = e.dataIdMap.get(n.dataId).id, _e = e.dataIdMap.get(s.dataId).id;
return ZP(be, _e, f, h, g, b, C, x, k, _, S, E, R, D, P, B, z, U, j, q, Y, J, re, ne, ee, oe, le), ie;
}
var JP = { kernelName: rn, backendName: "wasm", setupFunc: Mre, kernelFunc: Lre };
var eO;
function Bre(r) {
eO = 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 zre(r) {
let { inputs: e, backend: t8, 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 = w.computeConv3DInfo(n.shape, s.shape, a, p, i), c = t8.makeOutput(u.outShape, n.dtype);
return eO(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.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 tO = { kernelName: on, backendName: "wasm", setupFunc: Bre, kernelFunc: zre };
var rO;
function Vre(r) {
rO = r.wasm.cwrap(Ka, 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 Wre(r) {
let { inputs: e, backend: t8, 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 = w.computeConv3DInfo(n.shape, p, a, 1, i), c = t8.makeOutput(u.filterShape, s.dtype);
return rO(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.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 oO = { kernelName: Ka, backendName: "wasm", setupFunc: Vre, kernelFunc: Wre };
var nO;
function Ure(r) {
nO = r.wasm.cwrap(nn, 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 Gre(r) {
let { inputs: e, backend: t8, 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 = w.computeConv3DInfo(p, s.shape, i, 1, a), c = t8.makeOutput(u.inShape, n.dtype);
return nO(t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(n.dataId).id, t8.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 sO = { kernelName: nn, backendName: "wasm", setupFunc: Ure, kernelFunc: Gre };
var aO = he(sn);
var iO = he(an);
var Nv;
(function(r) {
r[r.bilinear = 0] = "bilinear", r[r.nearest = 1] = "nearest";
})(Nv || (Nv = {}));
var uO;
function Hre(r) {
uO = r.wasm.cwrap(cn, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
}
function Kre(r) {
let { backend: e, inputs: t8, attrs: o } = r, { method: n, extrapolationValue: s, cropSize: a } = o, { image: i, boxes: p, boxInd: u } = t8, 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 = Mr({ 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, C = e.makeOutput(d, "float32"), S = e.dataIdMap.get(C.dataId).id, k = new Uint8Array(new Int32Array(i.shape).buffer);
return uO(g, x, b, c, k, l, m, Nv[n], s, S), h != null && e.disposeData(h.dataId), C;
}
var pO = { kernelName: cn, backendName: "wasm", setupFunc: Hre, kernelFunc: Kre };
var cO;
function qre(r) {
cO = r.wasm.cwrap(un, null, ["number", "number", "number", "number", "number", "number"]);
}
function jre(r) {
let { inputs: e, backend: t8, 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 = w.getAxesPermutation([s], p), c = n;
u !== null && (c = ho({ inputs: { x: n }, attrs: { perm: u }, backend: t8 }));
let l = w.getInnerMostAxes(1, p)[0];
w.assertAxesAreInnerMostDims("cumprod", [l], p);
let m = t8.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t8.dataIdMap.get(c.dataId).id, h = t8.dataIdMap.get(m.dataId).id;
cO(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]);
let g = m;
if (u !== null) {
let x = w.getUndoAxesPermutation(u);
g = ho({ inputs: { x: m }, attrs: { perm: x }, backend: t8 }), t8.disposeData(c.dataId), t8.disposeData(m.dataId);
}
return g;
}
var lO = { kernelName: un, backendName: "wasm", setupFunc: qre, kernelFunc: jre };
var mO;
function Xre(r) {
mO = r.wasm.cwrap(pn, null, ["number", "number", "number", "number", "number", "number"]);
}
function Yre(r) {
let { inputs: e, backend: t8, 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 = w.getAxesPermutation([s], p), c = n;
u !== null && (c = ho({ inputs: { x: n }, attrs: { perm: u }, backend: t8 }));
let l = w.getInnerMostAxes(1, p)[0];
w.assertAxesAreInnerMostDims("cumsum", [l], p);
let m = t8.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t8.dataIdMap.get(c.dataId).id, h = t8.dataIdMap.get(m.dataId).id;
mO(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]);
let g = m;
if (u !== null) {
let x = w.getUndoAxesPermutation(u);
g = ho({ inputs: { x: m }, attrs: { perm: x }, backend: t8 }), t8.disposeData(c.dataId), t8.disposeData(m.dataId);
}
return g;
}
var dO = { kernelName: pn, backendName: "wasm", setupFunc: Xre, kernelFunc: Yre };
var fO;
function Qre(r) {
fO = r.wasm.cwrap("DenseBincount", null, ["number", "array", "number", "number", "boolean", "number", "number", "boolean", "number"]);
}
function Zre(r) {
let { backend: e, inputs: t8, attrs: o } = r, { x: n, weights: s } = t8, { 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 fO(l(n), new Uint8Array(new Int32Array(n.shape).buffer), n.shape.length, a, p, l(s), we[s.dtype], i, l(c)), c;
}
var hO = { kernelName: ta, backendName: "wasm", setupFunc: Qre, kernelFunc: Zre };
var gO;
function Jre(r) {
gO = r.wasm.cwrap(ln, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
}
function eoe(r) {
let { backend: e, inputs: t8, attrs: o } = r, { x: n } = t8, { 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), C = new Uint8Array(new Int32Array(f).buffer), S = new Uint8Array(new Int32Array(y.computeStrides(f)).buffer), k = e.dataIdMap.get(h.dataId).id;
return gO(x, s, a === "NHWC" ? 1 : 0, b, n.shape.length - 1, C, S, f.length, k), h;
}
var xO = { kernelName: ln, backendName: "wasm", setupFunc: Jre, kernelFunc: eoe };
var yO;
function toe(r) {
yO = r.wasm.cwrap(mn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function roe(r) {
let { inputs: e, attrs: t8, 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 } = t8, m = u == null ? [1, 1] : u, d = w.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, C = d.padInfo.left, S = d.dilationHeight, k = d.dilationWidth, _ = d.strideHeight, E = d.strideWidth, R = d.inChannels, D = d.outChannels, P = 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 yO(a, n.shape[0], n.shape[1], n.shape[2], i, f, h, g, x, b, C, P, S, k, _, E, R, D, M), O;
}
var bO = { kernelName: mn, backendName: "wasm", setupFunc: toe, kernelFunc: roe };
var CO;
function ooe(r) {
CO = r.wasm.cwrap("Diag", null, ["number", "number", "number", "number"]);
}
function noe(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t8.makeOutput([...o.shape, ...o.shape], o.dtype);
return CO(t8.dataIdMap.get(o.dataId).id, we[o.dtype], n, t8.dataIdMap.get(s.dataId).id), s;
}
var wO = { kernelName: ra, backendName: "wasm", setupFunc: ooe, kernelFunc: noe };
var SO;
function soe(r) {
SO = r.wasm.cwrap(dn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function aoe(r) {
let { inputs: e, backend: t8, 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 = w.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = t8.makeOutput(u.outShape, n.dtype);
return SO(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.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 IO = { kernelName: dn, backendName: "wasm", setupFunc: soe, kernelFunc: aoe };
var vO;
function ioe(r) {
vO = r.wasm.cwrap(Mi, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function uoe(r) {
let { inputs: e, backend: t8, 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 = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = t8.makeOutput(s.shape, s.dtype);
return vO(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(a.dataId).id, t8.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 kO = { kernelName: Mi, backendName: "wasm", setupFunc: ioe, kernelFunc: uoe };
var NO;
function poe(r) {
NO = r.wasm.cwrap(Oi, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function coe(r) {
let { inputs: e, backend: t8, 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 = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = t8.makeOutput(n.shape, n.dtype);
return NO(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(a.dataId).id, t8.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 TO = { kernelName: Oi, backendName: "wasm", setupFunc: poe, kernelFunc: coe };
var _O = he(hn);
var $O;
function loe(r) {
$O = r.wasm.cwrap(qa, null, ["number", "number", "number"]);
}
function moe(r) {
let { inputs: e, backend: t8 } = r, { dy: o, y: n } = e, s = t8.makeOutput(n.shape, "float32"), a = (i) => t8.dataIdMap.get(i.dataId).id;
return $O(a(n), a(o), a(s)), s;
}
var EO = { kernelName: qa, backendName: "wasm", setupFunc: loe, kernelFunc: moe };
var doe = false;
var RO = We(xn, doe, "bool");
var DO = he(gn);
var AO = he(yn, "float32");
function Og(r) {
let { inputs: e, attrs: t8, backend: o } = r, { input: n } = e, { dim: s } = t8, a = n.shape.length, i = n.shape.slice(), p = s;
return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), zt({ inputs: { x: n }, backend: o, attrs: { shape: i } });
}
var FO = { kernelName: oa, backendName: "wasm", kernelFunc: Og };
var PO = he(bn, "float32");
function Tv(r) {
let { attrs: { shape: e, value: t8, dtype: o }, backend: n } = r, s = n.makeOutput(e, o);
return n.typedArrayFromHeap(s).fill(t8), s;
}
var OO = { kernelName: na, backendName: "wasm", kernelFunc: Tv };
var MO;
function foe(r) {
MO = r.wasm.cwrap(Cn, null, ["number", "number", "number", "number", "number", "number"]);
}
function hoe(r) {
let { inputs: e, backend: t8 } = r, { image: o } = e, n = t8.makeOutput(o.shape, o.dtype), s = t8.dataIdMap.get(o.dataId).id, a = t8.dataIdMap.get(n.dataId).id, [i, p, u, c] = o.shape;
return MO(s, i, p, u, c, a), n;
}
var LO = { kernelName: Cn, backendName: "wasm", kernelFunc: hoe, setupFunc: foe };
var BO = he(wn);
var goe = false;
var zO = We(Sn, goe);
var VO;
function xoe(r) {
VO = r.wasm.cwrap(In, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function yoe(r) {
let { backend: e, inputs: t8, attrs: o } = r, { varianceEpsilon: n } = o, { x: s, mean: a, variance: i, offset: p, scale: u } = t8, 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 VO(c, l, m, d, f, n, g), h;
}
var WO = { kernelName: In, backendName: "wasm", setupFunc: xoe, kernelFunc: yoe };
var UO;
function boe(r) {
UO = r.wasm.cwrap(Io, 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 Coe(r) {
let { inputs: e, attrs: t8, 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 } = t8, h = w.computeConv2DInfo(n.shape, s.shape, p, c, u, m), g = Iu[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, C = 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 ${ee.shape.length}.`);
if (ee.shape[0] !== C)
throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);
S = ee.id;
}
let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, R = h.padInfo.right, D = h.padInfo.bottom, P = h.padInfo.left, O = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, B = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, j = h.batchSize, q = h.inHeight, Y = h.inWidth;
if (l !== "NHWC")
throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${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 UO(x, j, q, Y, b, k, _, S, E, R, D, P, U, O, M, L, B, z, C, g, ne, f || 0, re), J;
}
var GO = { kernelName: Io, backendName: "wasm", setupFunc: boe, kernelFunc: Coe };
var HO;
function woe(r) {
HO = r.wasm.cwrap(vo, 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 Soe(r) {
let { inputs: e, attrs: t8, 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 } = t8, h = w.computeConv2DInfo(n.shape, s.shape, p, c, u, m, true), g = Iu[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, C = 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 ${ee.shape.length}.`);
if (ee.shape[0] !== C)
throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);
S = ee.id;
}
let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, R = h.padInfo.right, D = h.padInfo.bottom, P = h.padInfo.left, O = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, B = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, j = h.batchSize, q = h.inHeight, Y = h.inWidth;
if (l !== "NHWC")
throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${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 HO(x, j, q, Y, b, k, _, S, E, R, D, P, U, O, M, L, B, z, C, g, ne, f || 0, re), J;
}
var KO = { kernelName: vo, backendName: "wasm", setupFunc: woe, kernelFunc: Soe };
var qO;
function Ioe(r) {
qO = r.wasm.cwrap(vn, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
}
function voe(r) {
let { backend: e, inputs: t8 } = r, { params: o, indices: n } = t8, [s, a, i, p] = nf.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 qO(d, we[o.dtype], h, a, l, i, g, x), u;
}
var jO = { kernelName: vn, backendName: "wasm", setupFunc: Ioe, kernelFunc: voe };
var XO;
function koe(r) {
XO = r.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
}
function Noe(r) {
let { backend: e, inputs: t8, attrs: o } = r, { x: n, indices: s } = t8, { 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 P = u[D];
y.assert(P <= c - 1 && P >= 0, () => `GatherV2: the index value ${P} is not in [0, ${c - 1}]`);
}
let l = w.segment_util.collectGatherOpShapeInfo(n, s, p, i), m = zt({ inputs: { x: n }, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] }, backend: e }), d = y.sizeFromShape(s.shape), f = zt({ inputs: { x: s }, attrs: { shape: [l.batchSize, d / l.batchSize] }, backend: e }), h = [l.batchSize, l.outerSize, d / l.batchSize, l.sliceSize], g = e.makeOutput(h, n.dtype);
if (y.sizeFromShape(n.shape) === 0)
return g;
let x = m.shape.length - 1, C = e.dataIdMap.get(m.dataId).id, k = e.dataIdMap.get(f.dataId).id, _ = e.dataIdMap.get(g.dataId).id, E = new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer), R = new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);
return XO(C, we[n.dtype], E, x, k, l.batchSize, R, _), e.disposeData(m.dataId), e.disposeData(f.dataId), g.shape = l.outputShape, g;
}
var YO = { kernelName: sa, backendName: "wasm", setupFunc: koe, kernelFunc: Noe };
var Toe = false;
var QO = We(kn, Toe, "bool");
var _oe = false;
var ZO = We(Nn, _oe, "bool");
var JO = he(Tn, "bool");
var eM = he(_n, "bool");
var tM = he($n, "bool");
var rM;
function $oe(r) {
rM = r.wasm.cwrap(En, null, ["number", "number", "number", "number"]);
}
function Eoe(r) {
let { inputs: { x: e }, attrs: { alpha: t8 }, 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;
rM(n, we[e.dtype], t8, a);
}
return s;
}
var oM = { kernelName: En, backendName: "wasm", setupFunc: $oe, kernelFunc: Eoe };
var Roe = false;
var nM = We(Rn, Roe, "bool");
var Doe = false;
var sM = We(Dn, Doe, "bool");
var aM;
function Aoe(r) {
aM = r.wasm.cwrap(An, null, ["number", "number", "number", "number"]);
}
function Foe(r) {
let { attrs: e, backend: t8 } = r, { start: o, stop: n, num: s } = e, a = Math.floor(s), i = t8.makeOutput([a], "float32");
return aM(t8.dataIdMap.get(i.dataId).id, o, n, a), i;
}
var iM = { kernelName: An, backendName: "wasm", setupFunc: Aoe, kernelFunc: Foe };
var uM = he(Fn);
var pM = he(Pn);
var Poe = false;
var cM = We(On, Poe, "bool");
var lM = he(Mn);
var Ooe = false;
var mM = We(Ln, Ooe, "bool");
var Moe = false;
var dM = We(w0, Moe, "bool");
var fM;
function Loe(r) {
fM = r.wasm.cwrap(Bn, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function Boe(r) {
let { inputs: e, backend: t8, 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 = t8.makeOutput(n.shape, n.dtype);
return fM(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(u.dataId).id, n.shape[3], s, a, i, p), u;
}
var hM = { kernelName: Bn, backendName: "wasm", setupFunc: Loe, kernelFunc: Boe };
var gM;
function zoe(r) {
gM = r.wasm.cwrap(ja, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Voe(r) {
let { inputs: e, backend: t8, 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 = t8.makeOutput(n.shape, n.dtype);
return gM(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(a.dataId).id, t8.dataIdMap.get(l.dataId).id, a.shape[3], i, p, u, c), l;
}
var xM = { kernelName: ja, backendName: "wasm", setupFunc: zoe, kernelFunc: Voe };
var yM;
function Woe(r) {
yM = r.wasm.cwrap(zn, null, ["number", "number", "number", "number"]);
}
function Uoe(r) {
let { backend: e, inputs: t8, attrs: o } = r, { reductionIndices: n, keepDims: s } = o, { x: a } = t8, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e);
if (d) {
let C = e.dataIdMap.get(c.dataId).id;
u = c, p = C;
}
let f = u.shape.length;
w.assertAxesAreInnerMostDims("max", l, f);
let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let C = e.dataIdMap.get(b.dataId).id;
yM(p, we[a.dtype], x, C);
}
if (d && e.disposeData(c.dataId), s) {
let C = w.expandShapeToKeepDim(b.shape, m);
b.shape = C;
}
return b;
}
var bM = { kernelName: zn, backendName: "wasm", setupFunc: Woe, kernelFunc: Uoe };
var Goe = false;
var CM = We(Vn, Goe);
var wM;
function Hoe(r) {
wM = r.wasm.cwrap(Wn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Koe(r) {
let { inputs: e, attrs: t8, 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 ${n.dtype}.`);
let { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t8, c = w.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.dilationHeight, b = c.dilationWidth, C = c.strideHeight, S = c.strideWidth, k = c.inChannels, _ = c.outChannels;
if (c.dataFormat !== "channelsLast")
throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
let E = o.makeOutput(c.outShape, "float32"), R = o.dataIdMap.get(E.dataId).id;
return wM(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, S, k, _, R), E;
}
var SM = { kernelName: Wn, backendName: "wasm", setupFunc: Hoe, kernelFunc: Koe };
var IM;
function qoe(r) {
IM = 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 joe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t8.makeOutput(c.outShape, n.dtype);
return IM(t8.dataIdMap.get(n.dataId).id, t8.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 vM = { kernelName: aa, backendName: "wasm", setupFunc: qoe, kernelFunc: joe };
var kM;
function Xoe(r) {
kM = 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 Yoe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = t8.makeOutput(s.shape, s.dtype);
return kM(t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(n.dataId).id, t8.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 NM = { kernelName: Ui, backendName: "wasm", setupFunc: Xoe, kernelFunc: Yoe };
var TM;
function Qoe(r) {
TM = r.wasm.cwrap("MaxPoolGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Zoe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool2DInfo(s.shape, a, i, 1, p, u), l = t8.makeOutput(s.shape, s.dtype);
return TM(t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.top, c.padInfo.left), l;
}
var _M = { kernelName: Wi, backendName: "wasm", setupFunc: Qoe, kernelFunc: Zoe };
var $M;
function Joe(r) {
$M = r.wasm.cwrap("MaxPoolWithArgmax", null, ["number", "number", "number", "number", "boolean", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function ene(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, includeBatchInIndex: p } = o;
y.assert(n.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);
let u = [1, 1];
y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
let c = w.computePool2DInfo(n.shape, s, a, [1, 1], i), l = t8.makeOutput(c.outShape, n.dtype), m = t8.makeOutput(c.outShape, "int32");
return $M(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(l.dataId).id, t8.dataIdMap.get(m.dataId).id, we[n.dtype], p, c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.top, c.padInfo.left), [l, m];
}
var EM = { kernelName: ia, backendName: "wasm", setupFunc: Joe, kernelFunc: ene };
var RM;
function tne(r) {
RM = r.wasm.cwrap(Un, null, ["number, number, number"]);
}
function rne(r) {
let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e), f = l;
if (d) {
let S = e.dataIdMap.get(c.dataId).id;
S !== i && (u = c, p = S, f = w.getInnerMostAxes(f.length, u.shape.length));
}
w.assertAxesAreInnerMostDims("mean", f, u.shape.length);
let [h, g] = w.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = u;
u.dtype !== "float32" && (b = Mr({ backend: e, inputs: { x: u }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(b.dataId).id);
let C = e.makeOutput(h, "float32");
if (y.sizeFromShape(u.shape) !== 0) {
let S = e.dataIdMap.get(C.dataId).id;
RM(p, x, S);
}
if (d && e.disposeData(c.dataId), s) {
let S = w.expandShapeToKeepDim(C.shape, m);
C.shape = S;
}
return u.dtype !== "float32" && e.disposeData(b.dataId), C;
}
var DM = { kernelName: Un, backendName: "wasm", setupFunc: tne, kernelFunc: rne };
var AM;
function one(r) {
AM = r.wasm.cwrap(Gn, null, ["number", "number", "number", "number"]);
}
function nne(r) {
let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e);
if (d) {
let C = e.dataIdMap.get(c.dataId).id;
C !== i && (u = c, p = C);
}
let f = u.shape.length;
w.assertAxesAreInnerMostDims("min", l, f);
let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let C = e.dataIdMap.get(b.dataId).id;
AM(p, we[a.dtype], x, C);
}
if (d && e.disposeData(c.dataId), s) {
let C = w.expandShapeToKeepDim(b.shape, m);
b.shape = C;
}
return b;
}
var FM = { kernelName: Gn, backendName: "wasm", setupFunc: one, kernelFunc: nne };
var sne = false;
var PM = We(Hn, sne);
var _v;
(function(r) {
r[r.reflect = 0] = "reflect", r[r.symmetric = 1] = "symmetric";
})(_v || (_v = {}));
var OM;
function ane(r) {
OM = r.wasm.cwrap(Kn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function ine(r) {
let { inputs: { x: e }, backend: t8, attrs: { paddings: o, mode: n } } = r, s = o.map((f, h) => f[0] + e.shape[h] + f[1]), a = t8.dataIdMap.get(e.dataId).id, i = t8.makeOutput(s, e.dtype), p = t8.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 OM(a, u, e.shape.length, we[e.dtype], m, d, _v[n], p), i;
}
var MM = { kernelName: Kn, backendName: "wasm", kernelFunc: ine, setupFunc: ane };
var LM;
function une(r) {
LM = r.wasm.cwrap(Is, null, ["number", "number", "number", "number"]);
}
function $v(r) {
let { backend: e, inputs: { logits: t8 }, attrs: { dim: o } } = r, n = e.dataIdMap.get(t8.dataId).id, s = e.makeOutput(t8.shape, t8.dtype), a = e.dataIdMap.get(s.dataId).id, i = t8.shape[o], p = y.sizeFromShape(t8.shape) / i;
return y.sizeFromShape(s.shape) === 0 || LM(n, a, i, p), s;
}
var BM = { kernelName: Is, backendName: "wasm", setupFunc: une, kernelFunc: $v };
var zM;
function pne(r) {
zM = r.wasm.cwrap(jn, null, ["number", "number", "number", "number", "number", "number"]);
}
function cne(r) {
let { inputs: e, backend: t8, 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 : $v({ inputs: { logits: n }, backend: t8, attrs: { dim: n.shape.length - 1 } }), [u, c] = p.shape, l = t8.makeOutput([u, s], "int32");
return zM(t8.dataIdMap.get(p.dataId).id, u, c, s, a, t8.dataIdMap.get(l.dataId).id), i || t8.disposeData(p.dataId), l;
}
var VM = { kernelName: jn, backendName: "wasm", setupFunc: pne, kernelFunc: cne };
var WM = We(qn, true);
var lne = true;
var UM = We(Xn, lne);
var GM = he(ua);
function Kc(r, e) {
let t8 = new Int32Array(r.wasm.HEAPU8.buffer, e, 4), o = t8[0], n = t8[1], s = t8[2], a = t8[3];
return r.wasm._free(e), { pSelectedIndices: o, selectedSize: n, pSelectedScores: s, pValidOutputs: a };
}
var HM;
function mne(r) {
HM = r.wasm.cwrap(Qn, "number", ["number", "number", "number", "number", "number"]);
}
function dne(r) {
let { backend: e, inputs: t8, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a } = o, { boxes: i, scores: p } = t8, u = e.dataIdMap.get(i.dataId).id, c = e.dataIdMap.get(p.dataId).id, l = HM(u, c, s, n, a), { pSelectedIndices: m, selectedSize: d, pSelectedScores: f, pValidOutputs: h } = Kc(e, l);
return e.wasm._free(f), e.wasm._free(h), e.makeOutput([d], "int32", m);
}
var KM = { kernelName: Qn, backendName: "wasm", setupFunc: mne, kernelFunc: dne };
var qM;
function fne(r) {
qM = r.wasm.cwrap(Xa, "number", ["number", "number", "number", "number", "number", "bool"]);
}
function hne(r) {
let { backend: e, inputs: t8, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, padToMaxOutputSize: i } = o, { boxes: p, scores: u } = t8, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = qM(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Kc(e, m);
e.wasm._free(h);
let x = e.makeOutput([f], "int32", d), b = e.makeOutput([], "int32", g);
return [x, b];
}
var jM = { kernelName: Xa, backendName: "wasm", setupFunc: fne, kernelFunc: hne };
var XM;
function gne(r) {
XM = r.wasm.cwrap(Zn, "number", ["number", "number", "number", "number", "number", "number"]);
}
function xne(r) {
let { backend: e, inputs: t8, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, softNmsSigma: i } = o, { boxes: p, scores: u } = t8, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = XM(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Kc(e, m);
e.wasm._free(g);
let x = e.makeOutput([f], "int32", d), b = e.makeOutput([f], "float32", h);
return [x, b];
}
var YM = { kernelName: Zn, backendName: "wasm", setupFunc: gne, kernelFunc: xne };
var yne = false;
var QM = We(Yn, yne, "bool");
var ZM;
function bne(r) {
ZM = r.wasm.cwrap(Jn, null, ["number", "number", "number", "number", "number"]);
}
function Cne(r) {
let { inputs: e, backend: t8, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = t8.makeOutput([...n.shape, a], s), c = t8.dataIdMap.get(u.dataId).id, m = t8.dataIdMap.get(n.dataId).id;
return ZM(m, a, i, p, c), u;
}
var JM = { kernelName: Jn, backendName: "wasm", setupFunc: bne, kernelFunc: Cne };
function wne(r) {
let { inputs: { x: e }, backend: t8 } = r, o = t8.makeOutput(e.shape, e.dtype);
return t8.typedArrayFromHeap(o).fill(1), o;
}
var eL = { kernelName: pa, backendName: "wasm", kernelFunc: wne };
function Sne(r) {
let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o;
if (e.length === 1)
return Og({ inputs: { input: e[0] }, backend: t8, 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 = Og({ inputs: { input: c }, backend: t8, attrs: { dim: n } });
return i.push(l), l;
}), u = kv({ inputs: p, backend: t8, attrs: { axis: n } });
return i.forEach((c) => t8.disposeData(c.dataId)), u;
}
var tL = { kernelName: ca, backendName: "wasm", kernelFunc: Sne };
var rL;
function Ine(r) {
rL = r.wasm.cwrap(es, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function vne(r) {
let { inputs: { x: e }, backend: t8, 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 Tv({ backend: t8, attrs: { shape: s, value: n, dtype: e.dtype } });
let a = t8.dataIdMap.get(e.dataId).id, i = t8.makeOutput(s, e.dtype), u = t8.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 rL(a, c, e.shape.length, we[e.dtype], d, f, n, u), i;
}
var Mg = { kernelName: es, backendName: "wasm", kernelFunc: vne, setupFunc: Ine };
var kne = false;
var oL = We(ts, kne);
var nL;
function Nne(r) {
nL = r.wasm.cwrap(rs, null, ["number", "number", "number"]);
}
function Tne(r) {
let { inputs: e, backend: t8 } = r, { x: o, alpha: n } = e, s = t8.dataIdMap.get(o.dataId).id, a = t8.dataIdMap.get(n.dataId).id, i = s, p = o, u = p;
p.dtype !== "float32" && (u = Mr({ backend: t8, inputs: { x: o }, attrs: { dtype: "float32" } }), i = t8.dataIdMap.get(u.dataId).id);
let c = t8.makeOutput(o.shape, "float32"), l = t8.dataIdMap.get(c.dataId).id;
return nL(i, a, l), p.dtype !== "float32" && t8.disposeData(u.dataId), c;
}
var sL = { kernelName: rs, backendName: "wasm", setupFunc: Nne, kernelFunc: Tne };
var aL;
function _ne(r) {
aL = r.wasm.cwrap(os, null, ["number", "number", "number", "number"]);
}
function $ne(r) {
let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e), f = l;
if (d) {
let C = e.dataIdMap.get(c.dataId).id;
C !== i && (u = c, p = C, f = w.getInnerMostAxes(f.length, u.shape.length));
}
w.assertAxesAreInnerMostDims("prod", f, u.shape.length);
let [h, g] = w.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let C = e.dataIdMap.get(b.dataId).id;
aL(p, x, we[b.dtype], C);
}
if (d && e.disposeData(c.dataId), s) {
let C = w.expandShapeToKeepDim(b.shape, m);
b.shape = C;
}
return b;
}
var iL = { kernelName: os, backendName: "wasm", setupFunc: _ne, kernelFunc: $ne };
var Ene = (r) => {
let { backend: e, attrs: t8 } = r, { start: o, stop: n, step: s, dtype: a } = t8, i = cp(o, n, s, a), p = e.makeOutput([i.length], a);
return e.typedArrayFromHeap(p).set(i), p;
};
var uL = { kernelName: la, backendName: "wasm", kernelFunc: Ene };
var Rne = true;
var pL = We(fn, Rne);
var cL = he(ns);
var lL = he(ss);
var mL = he(us);
var dL;
function Dne(r) {
dL = r.wasm.cwrap(is, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Ane(r) {
let { backend: e, inputs: t8, attrs: o } = r, { images: n } = t8, { 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 = Mr({ 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 C = e.dataIdMap.get(b.dataId).id;
return dL(x, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), g != null && e.disposeData(g.dataId), b;
}
var fL = { kernelName: is, backendName: "wasm", setupFunc: Dne, kernelFunc: Ane };
var hL;
function Fne(r) {
hL = r.wasm.cwrap(Qa, null, ["number", "number", "number", "array", "array", "boolean"]);
}
function Pne(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = t8.makeOutput(n.shape, "float32"), p = t8.dataIdMap.get(n.dataId), u;
return p.dtype !== "float32" && (u = Mr({ backend: t8, inputs: { x: n }, attrs: { dtype: "float32" } }), p = t8.dataIdMap.get(u.dataId)), hL(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && t8.disposeData(u.dataId), i;
}
var gL = { kernelName: Qa, backendName: "wasm", setupFunc: Fne, kernelFunc: Pne };
var xL;
function One(r) {
xL = r.wasm.cwrap(as, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Mne(r) {
let { backend: e, inputs: t8, attrs: o } = r, { images: n } = t8, { 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 = Mr({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), g = e.dataIdMap.get(x.dataId));
let b = g.id, C = e.dataIdMap.get(h.dataId).id;
return xL(b, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), x != null && e.disposeData(x.dataId), h;
}
var yL = { kernelName: as, backendName: "wasm", setupFunc: One, kernelFunc: Mne };
var bL;
function Lne(r) {
bL = r.wasm.cwrap(Ya, null, ["number", "number", "number", "array", "array", "boolean"]);
}
function Bne(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = t8.makeOutput(n.shape, "float32"), p = t8.dataIdMap.get(n.dataId), u;
return p.dtype !== "float32" && (u = Mr({ backend: t8, inputs: { x: n }, attrs: { dtype: "float32" } }), p = t8.dataIdMap.get(u.dataId)), bL(t8.dataIdMap.get(n.dataId).id, t8.dataIdMap.get(s.dataId).id, t8.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && t8.disposeData(u.dataId), i;
}
var CL = { kernelName: Ya, backendName: "wasm", setupFunc: Lne, kernelFunc: Bne };
var wL;
function zne(r) {
wL = r.wasm.cwrap(ps, null, ["number", "array", "number", "array", "number", "number"]);
}
function Vne(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dims: s } = o, a = y.parseAxisParam(s, n.shape);
if (n.shape.length === 0)
return _p({ inputs: { x: n }, backend: t8 });
let i = t8.makeOutput(n.shape, n.dtype), p = t8.dataIdMap.get(n.dataId).id, u = t8.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(a).buffer), l = new Uint8Array(new Int32Array(n.shape).buffer);
wL(p, c, a.length, l, n.shape.length, u);
let m = zt({ inputs: { x: i }, attrs: { shape: n.shape }, backend: t8 });
return t8.disposeData(i.dataId), m;
}
var SL = { kernelName: ps, backendName: "wasm", kernelFunc: Vne, setupFunc: zne };
var IL;
function Wne(r) {
IL = r.wasm.cwrap(Ds, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function Une(r) {
let { inputs: e, backend: t8, attrs: o } = r, { image: n } = e, { radians: s, fillValue: a, center: i } = o, p = t8.makeOutput(n.shape, n.dtype), u = t8.dataIdMap.get(n.dataId).id, c = t8.dataIdMap.get(p.dataId).id, [l, m, d, f] = n.shape, [h, g] = w.getImageCenter(i, m, d), x = a === 0, b = 255, C = typeof a == "number" ? [a, a, a, x ? 0 : b] : [...a, b], S = new Uint8Array(new Int32Array(C).buffer);
return IL(u, l, m, d, f, s, h, g, S, C.length, c), p;
}
var vL = { kernelName: Ds, backendName: "wasm", kernelFunc: Une, setupFunc: Wne };
var kL = he(cs);
var NL = he(ls);
var TL;
function Gne(r) {
TL = r.wasm.cwrap(ms, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function Hne(r) {
let { backend: e, inputs: t8, attrs: o } = r, { indices: n, updates: s } = t8, { 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 } = du.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 TL(f, g, we[s.dtype], p, u, c, x, m, b), i;
}
var _L = { kernelName: ms, backendName: "wasm", setupFunc: Gne, kernelFunc: Hne };
var $L;
function Kne(r) {
$L = r.wasm.cwrap(fs, null, ["number", "number", "number", "number", "number", "number", "bool", "number"]);
}
function qne(r) {
let { inputs: e, backend: t8, 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 = t8.makeOutput(s.shape, "int32");
function p(u) {
return t8.dataIdMap.get(u.dataId).id;
}
return $L(p(n), p(s), n.shape[0], n.shape[1], s.shape[1], we[n.dtype], a === "left", p(i)), i;
}
var EL = { kernelName: fs, backendName: "wasm", setupFunc: Kne, kernelFunc: qne };
var RL;
function jne(r) {
RL = r.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
}
function Xne(r) {
let { inputs: e, backend: t8 } = r, { condition: o, t: n, e: s } = e, a = t8.dataIdMap.get(o.dataId).id, i = t8.dataIdMap.get(n.dataId).id, p = t8.dataIdMap.get(s.dataId).id, u = t8.makeOutput(n.shape, n.dtype), c = t8.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 RL(a, i, p, d, c), u;
}
var DL = { kernelName: da, backendName: "wasm", kernelFunc: Xne, setupFunc: jne };
var AL = he(hs);
var FL;
function Yne(r) {
FL = r.wasm.cwrap(bs, null, ["number", "number"]);
}
function Qne(r) {
let { backend: e, inputs: { x: t8 } } = r, o = e.dataIdMap.get(t8.dataId).id, n = e.makeOutput(t8.shape, t8.dtype), s = e.dataIdMap.get(n.dataId).id;
return y.sizeFromShape(n.shape) === 0 || FL(o, s), n;
}
var PL = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: Yne, kernelFunc: Qne };
var OL = he(ys);
var ML = he(gs);
var LL = he(xs);
var BL = he(Cs);
function Zne(r) {
let { inputs: e, backend: t8, 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 = Mg.kernelFunc({ inputs: { x: n }, backend: t8, attrs: { paddings: p, constantValue: 0 } }), c = w.getReshaped(u.shape, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u.shape, s, i, false), h = zt({ inputs: { x: u }, backend: t8, attrs: { shape: c } }), b = ho({ inputs: { x: h }, backend: t8, attrs: { perm: l } }), k = zt({ inputs: { x: b }, backend: t8, attrs: { shape: m } });
return t8.disposeData(u.dataId), t8.disposeData(h.dataId), t8.disposeData(b.dataId), k;
}
var zL = { kernelName: ha, backendName: "wasm", kernelFunc: Zne };
var VL;
function Jne(r) {
VL = r.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function ese(r) {
let { backend: e, inputs: t8 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = t8, 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"), C = 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 = VL(l, m, we[n.dtype], i, u, p, d, h, x, C, k, E), D = e.readSync(_.dataId), P;
switch (D[0]) {
case 1: {
P = w.getSparseFillEmptyRowsIndicesDenseShapeMismatch(D[1]);
break;
}
case 2: {
P = w.getSparseFillEmptyRowsNegativeIndexErrorMessage(D[1], D[2]);
break;
}
case 3:
P = w.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(D[1], D[2], D[3]);
break;
default:
P = "";
}
if (e.disposeData(_.dataId), P)
throw e.disposeData(f.dataId), e.disposeData(g.dataId), e.disposeData(b.dataId), e.disposeData(S.dataId), new Error(P);
let O = f, M = g;
return R !== c[0] && (O = Po({ inputs: { x: f }, attrs: { begin: 0, size: [R, p] }, backend: e }), M = Po({ inputs: { x: g }, attrs: { begin: 0, size: R }, backend: e }), e.disposeData(f.dataId), e.disposeData(g.dataId)), [O, M, b, S];
}
var WL = { kernelName: Hi, backendName: "wasm", setupFunc: Jne, kernelFunc: ese };
var UL;
function tse(r) {
UL = r.wasm.cwrap(Za, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function rse(r) {
let { backend: e, inputs: t8 } = r, { inputIndices: o, inputShape: n, newShape: s } = t8;
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;
UL(a, i, p, u, m, f, g);
let x = e.readSync(h.dataId), b;
switch (x[0]) {
case 0: {
b = w.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(x[1], x[2]);
break;
}
case 1: {
b = w.getSparseReshapeNegativeOutputDimErrorMessage(x[1], x[2]);
break;
}
case 2:
b = w.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();
break;
case 3: {
let C = Array.from(e.readSync(n.dataId)), S = Array.from(e.readSync(d.dataId));
b = w.getSparseReshapeInputOutputMultipleErrorMessage(C, S);
break;
}
case 4: {
let C = Array.from(e.readSync(n.dataId)), S = Array.from(e.readSync(d.dataId));
b = w.getSparseReshapeInputOutputMismatchErrorMessage(C, 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 GL = { kernelName: Za, backendName: "wasm", setupFunc: tse, kernelFunc: rse };
var HL;
function Lg(r) {
HL = r.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Bg(r, e) {
let { backend: t8, inputs: o } = r, { data: n, indices: s, segmentIds: a } = o, i = s.shape[0], p = t8.readSync(a.dataId, i - 1, i)[0], c = i > 0 ? p + 1 : 0;
if (c < 0)
throw new Error(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
let l = n.shape.slice();
l[0] = c;
let m = t8.dataIdMap.get(n.dataId).id, d = t8.dataIdMap.get(s.dataId).id, f = t8.dataIdMap.get(a.dataId).id, h = t8.makeOutput(l, n.dtype), g = t8.dataIdMap.get(h.dataId).id, x = t8.makeOutput([4], "int32"), b = t8.dataIdMap.get(x.dataId).id;
HL(m, we[n.dtype], n.shape[0], d, f, g, b, e, 0);
let C = t8.readSync(x.dataId), S;
switch (C[0]) {
case 0: {
S = w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();
break;
}
case 1: {
S = w.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();
break;
}
case 2:
S = w.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(C[1], C[2]);
break;
case 3:
S = w.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(C[1], C[2], C[3]);
break;
default:
S = "";
}
if (t8.disposeData(x.dataId), S)
throw t8.disposeData(h.dataId), new Error(S);
return h;
}
function ose(r) {
return Bg(r, true);
}
var KL = { kernelName: Ki, backendName: "wasm", setupFunc: Lg, kernelFunc: ose };
function nse(r) {
return Bg(r, false);
}
var qL = { kernelName: qi, backendName: "wasm", setupFunc: Lg, kernelFunc: nse };
var jL;
function sse(r) {
jL = r.wasm.cwrap(vs, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function ase(r) {
let { backend: e, inputs: t8, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = t8, { 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 } = w.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 jL(f, h, s.shape.length, g, we[a.dtype], u, c, l, x, d, b), p;
}
var XL = { kernelName: vs, backendName: "wasm", setupFunc: sse, kernelFunc: ase };
function ise(r) {
let { inputs: e, attrs: t8, backend: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = t8, i = y.parseAxisParam(a, n.shape)[0], p = w.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 = Po({ inputs: { x: n }, attrs: { begin: u, size: m }, backend: o });
return u[i] += l, d;
});
}
var YL = { kernelName: ga, backendName: "wasm", kernelFunc: ise };
var QL = he(ws);
var ZL = he(ji);
var use = true;
var JL = We(ks, use);
var eB;
function pse(r) {
eB = r.wasm.cwrap(wo, null, ["number", "number", "number", "number"]);
}
function cse(r) {
let { backend: e, inputs: t8, attrs: o } = r, { alpha: n } = o, { x: s } = t8, a = e.dataIdMap.get(s.dataId).id, i = e.makeOutput(s.shape, s.dtype), p = e.dataIdMap.get(i.dataId).id;
return eB(a, n, we[s.dtype], p), i;
}
var tB = { kernelName: wo, backendName: "wasm", setupFunc: pse, kernelFunc: cse };
var rB;
function lse(r) {
rB = r.wasm.cwrap(Ns, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
}
function mse(r) {
let { backend: e, inputs: t8, attrs: o } = r, { x: n } = t8, { 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: C, 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, C, S), E = Po({ 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), P = new Uint8Array(new Int32Array(C).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;
rB(E, R, n.shape.length, D, P, O, M, L, d.length, B), k = zt({ inputs: { x: _ }, backend: e, attrs: { shape: f } }), e.disposeData(_.dataId);
}
return k;
}
var oB = { kernelName: Ns, backendName: "wasm", setupFunc: lse, kernelFunc: mse };
function dse(r) {
let { backend: e, inputs: t8, attrs: o } = r, { data: n, dataSplits: s } = t8, { 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] = mp(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 nB = { kernelName: xa, backendName: "wasm", kernelFunc: dse };
function fse(r) {
let { backend: e, inputs: t8, attrs: o } = r, { input: n, delimiter: s } = t8, { skipEmpty: a } = o, i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c, l] = dp(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 sB = { kernelName: Xi, backendName: "wasm", kernelFunc: fse };
function hse(r) {
let { backend: e, inputs: t8, attrs: o } = r, { input: n } = t8, { numBuckets: s } = o, a = e.readSync(n.dataId), i = fp(a, s), p = e.makeOutput(n.shape, "int32");
return e.typedArrayFromHeap(p).set(i), p;
}
var aB = { kernelName: Yi, backendName: "wasm", kernelFunc: hse };
var gse = true;
var iB = We(Ts, gse);
var uB;
function xse(r) {
uB = r.wasm.cwrap(Ss, null, ["number", "number", "number", "number"]);
}
function yse(r) {
let { backend: e, inputs: t8, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t8, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, e), f = l;
if (d) {
let C = e.dataIdMap.get(c.dataId).id;
C !== i && (u = c, p = C, f = w.getInnerMostAxes(f.length, u.shape.length));
}
w.assertAxesAreInnerMostDims("sum", f, u.shape.length);
let [h, g] = w.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let C = e.dataIdMap.get(b.dataId).id;
uB(p, x, we[b.dtype], C);
}
if (d && e.disposeData(c.dataId), s) {
let C = w.expandShapeToKeepDim(b.shape, m);
b.shape = C;
}
return b;
}
var pB = { kernelName: Ss, backendName: "wasm", setupFunc: xse, kernelFunc: yse };
var cB = he(_s);
var lB = he($s);
var mB;
function bse(r) {
mB = r.wasm.cwrap(ds, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number", "number"]);
}
function Cse(r) {
let { backend: e, inputs: t8, attrs: o } = r, { tensor: n, indices: s, updates: a } = t8, {} = 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 } = du.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, C = new Uint8Array(new Int32Array(l).buffer), S = e.dataIdMap.get(i.dataId).id;
return mB(f, g, we[a.dtype], p, u, c, C, m, S, b), i;
}
var dB = { kernelName: ds, backendName: "wasm", setupFunc: bse, kernelFunc: Cse };
var fB;
function wse(r) {
fB = r.wasm.cwrap(uo, null, ["number", "array", "number", "array", "number", "number"]);
}
function Sse(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, s = t8.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 = t8.makeOutput(i, n.dtype), l = t8.dataIdMap.get(c.dataId).id;
return fB(s, p, n.shape.length, u, i.length, we[c.dtype], l), c;
}
var hB = { kernelName: uo, backendName: "wasm", setupFunc: wse, kernelFunc: Sse };
var gB;
function Ise(r) {
gB = r.wasm.cwrap(Es, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
}
var vse = ({ inputs: r, backend: e, attrs: t8 }) => {
let { x: o } = r, { k: n, sorted: s } = t8, 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 gB(a, i, o.shape.length, we[o.dtype], n, s, c, m), [u, l];
};
var xB = { kernelName: Es, backendName: "wasm", setupFunc: Ise, kernelFunc: vse };
var yB;
function kse(r) {
yB = r.wasm.cwrap(Rs, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "array", "number", "number", "number", "number", "number"]);
}
function Nse(r) {
let { backend: e, inputs: t8, attrs: o } = r, { image: n, transforms: s } = t8, { 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), C = e.makeOutput(g, n.dtype), S = e.dataIdMap.get(C.dataId).id, _ = e.dataIdMap.get(n.dataId).id, R = e.dataIdMap.get(s.dataId).id, D = a === "nearest" ? 1 : 2, P;
switch (i) {
case "constant":
P = 1;
break;
case "reflect":
P = 2;
break;
case "wrap":
P = 3;
break;
case "nearest":
P = 4;
break;
default:
P = 1;
break;
}
return yB(_, R, s.shape[0] > 1, c, f, h, d, m, l, x, n.shape.length - 1, b, g.length - 1, D, P, p, S), C;
}
var bB = { kernelName: Rs, backendName: "wasm", setupFunc: kse, kernelFunc: Nse };
function Tse(r) {
let { inputs: e, attrs: t8, backend: o } = r, { axis: n } = t8, { x: s } = e, { outputValues: a, outputShape: i, indices: p } = hp(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 CB = { kernelName: Qi, backendName: "wasm", kernelFunc: Tse };
function _se(r) {
let { inputs: e, backend: t8, 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] = Po({ inputs: { x: n }, attrs: { begin: l, size: m }, backend: t8 });
return c.map(({ dataId: d, dtype: f }) => ({ dataId: d, dtype: f, shape: p }));
}
var wB = { kernelName: ya, backendName: "wasm", kernelFunc: _se };
function $se(r) {
let { inputs: { x: e }, backend: t8 } = r, o = t8.makeOutput(e.shape, e.dtype);
return t8.typedArrayFromHeap(o).fill(0), o;
}
var SB = { kernelName: ba, backendName: "wasm", kernelFunc: $se };
var Ese = [iP, uP, pP, cP, lP, dP, yP, CP, wP, SP, IP, vP, kP, NP, TP, $P, PP, RP, AP, LP, zP, WP, UP, GP, HP, KP, jP, XP, QP, JP, tO, oO, sO, aO, iO, pO, lO, dO, hO, xO, bO, wO, IO, kO, TO, _O, EO, RO, DO, AO, FO, PO, OO, LO, BO, zO, WO, GO, KO, jO, YO, QO, ZO, fP, JO, eM, tM, oM, nM, sM, iM, pM, uM, cM, lM, mM, dM, hM, xM, bM, CM, SM, vM, NM, _M, EM, DM, FM, PM, MM, VM, WM, UM, GM, KM, jM, YM, QM, JM, eL, tL, Mg, oL, sL, iL, uL, pL, cL, lL, mL, OP, fL, gL, yL, CL, SL, vL, kL, NL, _L, EL, DL, AL, PL, OL, ML, LL, BP, BM, BL, zL, WL, GL, KL, qL, XL, YL, QL, ZL, JL, tB, oB, nB, sB, aB, iB, pB, cB, lB, dB, hB, xB, bB, gP, CB, wB, SB];
for (let r of Ese)
Ja(r);
var Ev = A();
Ev.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => {
try {
return WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11]));
} catch (r) {
return false;
}
});
Ev.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
if (Ev.get("IS_NODE"))
return false;
try {
return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)), WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 5, 4, 1, 3, 1, 1, 10, 11, 1, 9, 0, 65, 0, 254, 16, 2, 0, 26, 11]));
} catch (r) {
return false;
}
});
var Lv = Up(NB());
var DB = Up(_B());
var Bv = Up($B());
var EB = Lv.default || Lv;
var Rse = Bv.default || Bv;
var am = class extends so {
constructor(e) {
super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(FB), Mv = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Bo(this, ur());
}
write(e, t8, o) {
let n = { id: this.dataIdNextNumber++ };
return this.move(n, e, t8, o, 1), n;
}
numDataIds() {
return this.dataIdMap.numDataIds();
}
async time(e) {
let t8 = y.now();
return e(), { kernelMs: y.now() - t8 };
}
move(e, t8, o, n, s) {
let a = this.dataIdNextNumber++;
if (n === "string") {
let c = t8;
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), t8 != null && this.wasm.HEAPU8.set(new Uint8Array(t8.buffer, t8.byteOffset, p), u);
}
async read(e) {
return this.readSync(e);
}
readSync(e, t8, o) {
let { memoryOffset: n, dtype: s, shape: a, stringBytes: i } = this.dataIdMap.get(e);
if (s === "string")
return (t8 == null || t8 === 0) && (o == null || o >= i.length) ? i : i.slice(t8, o);
t8 = t8 || 0, o = o || y.sizeFromShape(a);
let p = y.bytesPerElement(s), u = this.wasm.HEAPU8.slice(n + t8 * p, n + o * p);
return Ase(u.buffer, s);
}
disposeData(e, t8 = false) {
if (this.dataIdMap.has(e)) {
let o = this.dataIdMap.get(e);
if (o.refCount--, !t8 && 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 t8 = this.dataIdMap.get(e);
t8 != null && t8.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, t8, o, n) {
let s;
if (o == null)
s = this.write(n != null ? n : null, e, t8);
else {
let a = this.dataIdNextNumber++;
s = { id: a }, this.dataIdMap.set(s, { id: a, memoryOffset: o, shape: e, dtype: t8, refCount: 1 });
let i = y.sizeFromShape(e);
this.wasm.tfjs.registerTensor(a, i, o);
}
return { dataId: s, shape: e, dtype: t8 };
}
typedArrayFromHeap({ shape: e, dtype: t8, dataId: o }) {
let n = this.wasm.HEAPU8.buffer, { memoryOffset: s } = this.dataIdMap.get(o), a = y.sizeFromShape(e);
switch (t8) {
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 ${t8}`);
}
}
};
function Dse(r) {
return (e, t8) => (y.fetch(r, { credentials: "same-origin" }).then((o) => {
o.ok || e.env.a(`failed to load wasm binary file at '${r}'`), o.arrayBuffer().then((n) => {
WebAssembly.instantiate(n, e).then((s) => {
t8(s.instance, s.module);
});
});
}), {});
}
function RB(r, e, t8) {
if (Wg != null)
return Wg;
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] : t8 + o;
}
async function AB() {
let [r, e] = await Promise.all([A().getAsync("WASM_HAS_SIMD_SUPPORT"), A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);
return new Promise((t8, o) => {
let n = {};
n.locateFile = (i, p) => {
if (i.endsWith(".worker.js")) {
let u = DB.wasmWorkerContents.replace(/\n/g, "\\n"), c = new Blob([u], { type: "application/javascript" });
return URL.createObjectURL(c);
}
return i.endsWith(".wasm") ? RB(r, e, om != null ? om : p) : p + i;
}, zv && (n.instantiateWasm = Dse(RB(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 && Wg == null ? (n.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + EB.toString()], { type: "text/javascript" }), a = EB(n)) : a = Rse(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, []) }, t8({ wasm: i });
}).catch(o);
});
}
function Ase(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 Fse = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
var Wg = null;
var om = null;
var nm = {};
var sm = false;
var zv = false;
function Pse(r, e = false) {
if (_w("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()`");
Wg = r, zv = e;
}
function Ose(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 t8 = Fse.filter((o) => nm[o] == null);
if (t8.length > 0)
throw new Error(`There were no entries found for the following binaries: ${t8.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.`);
}
zv = e;
}
var FB = -1;
var Mv = -1;
function Mse(r) {
FB = r;
}
function Lse() {
if (Mv === -1)
throw new Error("WASM backend not initialized.");
return Mv;
}
var Bse = "4.7.0";
var zse = 2;
nu("wasm", async () => {
let { wasm: r } = await AB();
return new am(r);
}, zse);
var go = A();
go.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
go.registerFlag("WEBGPU_CPU_FORWARD", () => true);
go.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE", () => -1);
go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => true);
go.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
go.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
go.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
go.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE", () => true);
go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG", () => false);
go.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL", () => 0);
go.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false);
go.registerFlag("WEBGPU_PRINT_SHADER", () => "");
go.registerFlag("WEBGPU_ENGINE_COMPILE_ONLY", () => false);
var Ug = 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 Gg = 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, t8, o = false, n = true) {
let s, a = PB(e, t8);
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: t8, mappedAtCreation: o }), this.numBytesAllocated += e)) : (s = this.device.createBuffer({ size: e, usage: t8, 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, t8 = true) {
if (this.freeBuffers.size === 0)
return;
let o = e.size, n = e.usage, s = PB(o, n), 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 -= o, t8 ? (this.freeBuffers.get(s).push(e), this.numFreeBuffers++) : (e.destroy(), this.numBytesAllocated -= o);
}
getNumUsedBuffers() {
return this.numUsedBuffers;
}
getNumFreeBuffers() {
return this.numFreeBuffers;
}
dispose() {
this.freeBuffers.forEach((e, t8) => {
e.forEach((o) => {
o.destroy();
});
}), this.usedBuffers.forEach((e, t8) => {
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 PB(r, e) {
return `${r}_${e}`;
}
var Hg = class {
constructor(e) {
this.device = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
}
acquireTexture(e, t8, o, n) {
let s = MB(o), a = e * t8 * s, i = OB(e, t8, o, n);
if (this.freeTextures.has(i) || this.freeTextures.set(i, []), this.usedTextures.has(i) || this.usedTextures.set(i, []), this.numBytesUsed += a, this.numUsedTextures++, this.freeTextures.get(i).length > 0) {
this.numFreeTextures--;
let u = this.freeTextures.get(i).shift();
return this.usedTextures.get(i).push(u), u;
}
this.numBytesAllocated += a;
let p = this.device.createTexture({ size: [e, t8], format: o, usage: n });
return this.usedTextures.get(i).push(p), p;
}
releaseTexture(e) {
if (this.freeTextures.size === 0)
return;
let t8 = e.width, o = e.height, n = e.format, s = e.usage, a = OB(t8, o, n, s);
this.freeTextures.has(a) || this.freeTextures.set(a, []), this.freeTextures.get(a).push(e), this.numFreeTextures++, this.numUsedTextures--;
let i = this.usedTextures.get(a), p = i.indexOf(e);
if (p < 0)
throw new Error("Cannot release a texture that was never provided by this texture manager");
i.splice(p, 1);
let u = MB(n), c = t8 * o * u;
this.numBytesUsed -= c;
}
getNumUsedTextures() {
return this.numUsedTextures;
}
getNumFreeTextures() {
return this.numFreeTextures;
}
dispose() {
this.freeTextures.forEach((e, t8) => {
e.forEach((o) => {
o.destroy();
});
}), this.usedTextures.forEach((e, t8) => {
e.forEach((o) => {
o.destroy();
});
}), this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numUsedTextures = 0, this.numFreeTextures = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
}
};
function OB(r, e, t8, o) {
return `${r}_${e}_${t8}_${o}`;
}
function MB(r) {
if (r === "rgba8unorm")
return 16;
throw new Error(`${r} is not supported!`);
}
function LB(r, e) {
if (Math.max(...r) > 5)
throw new Error("Cannot symbolically compute strides for rank > 6 tensor.");
let t8 = r.length, o = "xyzwuv", n = r.map((a) => `${e}.${o[a]}`), s = new Array(t8 - 1);
s[t8 - 2] = n[t8 - 1];
for (let a = t8 - 3; a >= 0; --a)
s[a] = `(${s[a + 1]} * ${n[a + 1]})`;
return s;
}
var Us = (r, e, t8) => t8 === "int32" ? `atomicAdd(${r}, bitcast<i32>(${e}));` : `
{
var oldValue = 0;
loop {
let newValueF32 = bitcast<f32>(oldValue) + (${e});
let newValue = bitcast<i32>(newValueF32);
let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);
if res.exchanged {
break;
}
oldValue = res.old_value;
}
}`;
var WB = (r, e, t8, o, n) => {
let s = { dtype: o.dtype, shape: o.shape }, a = Wse(t8, s, e), i = r.createShaderModule({ code: a, label: e.constructor.name }), p = A().get("WEBGPU_PRINT_SHADER");
if (p !== "") {
p = p.toLowerCase();
let u = p.split(",");
(p === "all" || u.some((c) => e.shaderKey.toLowerCase().includes(c))) && (console.group(e.shaderKey), console.debug(a), console.groupEnd());
}
return n ? r.createComputePipelineAsync({ compute: { module: i, entryPoint: "_start" }, label: e.constructor.name, layout: "auto" }) : r.createComputePipeline({ compute: { module: i, entryPoint: "_start" }, label: e.constructor.name, layout: "auto" });
};
var Ae = (r, e = "f32") => {
switch (r) {
case 1:
return `${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 ht(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 Oo(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 ${r} is not yet supported`);
}
function H(...r) {
let e;
switch (r.length) {
case 0:
e = `
fn main()
`;
break;
case 1:
e = `
fn main(${r[0]} : i32)
`;
break;
default:
throw Error("Unreachable");
}
return e;
}
function BB(r, e) {
let t8;
return t8 = `
${Vse(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;
${r ? "main(getGlobalIndex());" : "main();"};
}
`, t8;
}
function Vse(r) {
return `
@compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})
`;
}
function Wse(r, e, t8) {
let o = [], n = t8.workgroupSize[0] * t8.workgroupSize[1] * t8.workgroupSize[2];
if (t8.outputComponent = t8.outputComponent ? t8.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 {
${GB(t8) ? " return i32(globalId.x);" : ` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +
workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +
localIndex);
`}
}
`), t8.isFromPixels) {
o.push(`
struct Uniform {
size : i32,
numChannels : i32,
outShapeStrides : vec2<i32>,
};
@group(0) @binding(0) var<storage, read_write> result: array<${$p(e.dtype, t8.outputComponent)}>;
@group(0) @binding(2) var<uniform> uniforms: Uniform;
`);
let f = VB(t8);
return [zB, o.join(`
`), im(e.shape), t8.getUserCode(), BB(f, t8)].join(`
`);
}
let s, a, i = "struct Uniforms { NAN : f32, INFINITY : f32, ";
t8.variableNames.forEach((f, h) => {
let g = ht(r[h].shape.length);
i += `${f.charAt(0).toLowerCase() + f.slice(1)}Shape : ${g}, `, s = r[h].shape.length - 1, a = ht(s), i += `${f.charAt(0).toLowerCase() + f.slice(1)}ShapeStrides: ${a}, `;
});
let p = ht(e.shape.length);
i += `outShape : ${p}, `, s = e.shape.length - 1, a = ht(s), i += `
outShapeStrides: ${a}, `, t8.size && (i += "size : i32, "), t8.uniforms && (i += t8.uniforms), i += "};", i = Yse(i), o.push(i), t8.atomic ? o.push(`
@group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
`) : o.push(`
@group(0) @binding(0) var<storage, read_write> result: array<${$p(e.dtype, t8.outputComponent)}>;
`), t8.variableNames.forEach((f, h) => {
o.push(`
@group(0) @binding(${1 + h}) var<storage, read> ${f}: array<${t8.variableComponents ? $p(r[h].dtype, t8.variableComponents[h]) : $p(r[h].dtype, t8.outputComponent)}>;
`);
}), i !== "" && o.push(`
@group(0) @binding(${1 + t8.variableNames.length}) var<uniform> uniforms: Uniforms;
`);
let u = qse(e.shape, t8.dispatchLayout), c = [zB, o.join(`
`) + Use, im(e.shape), u, jse(e.shape.length)];
t8.atomic || c.push(Xse(e.shape, e.dtype, t8.outputComponent)), t8.variableNames.forEach((f, h) => {
c.push(`${im(r[h].shape, f)}`);
});
let l = r.map((f, h) => Kse(f, e.shape, t8.variableComponents ? t8.variableComponents[h] : t8.outputComponent, t8.dispatchLayout.x.length === e.shape.length)).join(`
`);
c.push(l), c.push(t8.getUserCode());
let m = VB(t8);
return c.push(BB(m, t8)), c.join(`
`);
}
function UB(r, e, t8) {
let o = r.shaderKey;
if (r.isFromPixels)
return o;
let n = [], s = [];
e.forEach((c) => {
n.push(c.shape), s.push(c.dtype);
}), n.push(t8.shape), s.push(t8.dtype);
let a = e.map((c) => w.getBroadcastDims(c.shape, t8.shape)), i = e.map((c) => y.arraysEqual(c.shape, t8.shape)).join("_"), p = a.map((c) => c.join("_")).join(";"), u = GB(r) ? "flatDispatch" : "";
return o += "_" + (r.workgroupSize ? r.workgroupSize.join(",") : "") + n.map((c) => c.length).join(",") + s.join(",") + r.variableNames.join(",") + p + i + u, o;
}
var zB = `
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 Use = `
fn isinf(val: f32) -> bool {
return abs(val) == uniforms.INFINITY;
}
`;
function im(r, e = "") {
let t8 = 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 (t8 <= 1)
return `fn ${o}(index : i32) -> i32 { return index; }`;
let s = y.computeStrides(r), a = ht(t8), i = [];
for (let u = 0; u < t8; u++)
i.push(`d${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}.${Oo(c)}`, m = c === s.length - 1 ? `let ${i[c + 1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}` : `index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`;
return `${l}; ${m};`;
}).join(""), `
fn ${o}(index : i32) -> ${a} {
${p}
return ${a}(${i.join(",")});
}
`;
}
function Gse(r, e) {
let t8 = r.name, o = r.shape.length, n = ht(o), s = "get" + t8.charAt(0).toUpperCase() + t8.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 ${Ae(e)}(${t8}[0]);
}
`;
let p = `uniforms.${t8.charAt(0).toLowerCase() + t8.slice(1)}Shape`, u = `${o}D`;
return o === 0 && (u = "1D"), `
fn ${s}(${i}) -> ${Ae(e)} {
return ${Ae(e)}(${t8}[getIndexFromCoords${u}(${n}(${a.join(",")}),
${p})${e === 1 ? "" : ` / ${e}`}]);
}
`;
}
function Hse(r, e, t8, 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 = ht(p);
if (y.arraysEqual(r.shape, e) && o)
return `
fn ${a}Index(globalIndex : i32) -> ${Ae(t8)} {
return ${Ae(t8)}(${n}[globalIndex]);
}
fn ${a}Coords(coords : ${u}) -> ${Ae(t8)} {
return ${Ae(t8)}(${n}[${p > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}${t8 === 1 ? "" : ` / ${t8}`}]);
}
`;
let c = w.getBroadcastDims(r.shape, e), l = p - i, m = "";
if (i === 0)
return `
fn ${a}Index(globalIndex : i32) -> ${Ae(t8)}{
return get${s}();
}
fn ${a}Coords(coords : ${u}) -> ${Ae(t8)}{
return get${s}();
}
`;
p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((g) => `coords.${Oo(g + l)} = 0;`).join(`
`);
let d = "";
if (p < 2 && i > 0)
d = "coords";
else if (p > 1) {
let g = ht(i), x = r.shape.map((b, C) => `coords.${Oo(C + 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(t8)} {
var coords = getCoordsFromIndex(globalIndex);
${m}
return ${Ae(t8)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t8 === 1 ? "" : ` / ${t8}`}]);
}
fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t8)} {
var coords = coordsIn;
${m}
return ${Ae(t8)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t8 === 1 ? "" : ` / ${t8}`}]);
}
`;
}
function Kse(r, e, t8, o) {
let n = Gse(r, t8);
return r.shape.length <= e.length && (n += Hse(r, e, t8, o)), n;
}
function qse(r, e) {
let { x: t8, y: o = [], z: n = [] } = e, s = r.length, a = t8.length + o.length + n.length;
if (a !== s)
return "";
if (t8.length === s)
return `fn getOutputCoords() -> ${ht(s)}{
let globalIndex = getGlobalIndex();
return getCoordsFromIndex(globalIndex);
}
`;
let i = "", p = [t8, o, n];
for (let m = 0; m < p.length; m++) {
let d = p[m];
if (d.length !== 0)
if (d.length === 1)
i += `let d${d[0]} = i32(globalId[${m}]);`;
else {
let f = LB(d, "uniforms.outShape");
i += `var index${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 = ht(a), l = `fn getOutputCoords() -> ${c} {
${i}
`;
return u.length === 0 ? l += `return ${c}(0); }` : l += `return ${c}(${u.join(",")}); }`, l;
}
function jse(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 GB(r) {
return r.dispatch[1] === 1 && r.dispatch[2] === 1;
}
function $p(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 Xse(r, e, t8) {
let o = r.length, n = $p(e, t8), s = `fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t8)}) {
result[flatIndex] = ${n}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t8, "i32")}) {
result[flatIndex] = ${n}(value);
}
`;
if (o >= 2) {
let a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = ht(o);
s += `
fn setOutputAtCoords(${a.map((p) => `${p} : i32`).join(", ")}, value : ${Ae(t8)}) {
let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")}));
setOutputAtIndex(flatIndex${t8 === 1 ? "" : ` / ${t8}`}, value);
}
fn setOutputAtCoordsI32(${a.map((p) => `${p} : i32`).join(", ")}, value : ${Ae(t8, "i32")}) {
let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")}));
setOutputAtIndexI32(flatIndex${t8 === 1 ? "" : ` / ${t8}`}, value);
}
`;
}
return s;
}
function Yse(r) {
let e = /(\w+)\s*:\s*vec(5|6)/g;
r = r.replace(e, (o) => "@align(16) " + o);
let t8 = /vec(5|6)\s*,\s*(\w+)/g;
return r = r.replace(t8, (o, n, s) => `vec${n}, @align(16) ${s}`), r;
}
function VB(r) {
return !(r.dispatchLayout.hasOwnProperty("y") && r.dispatchLayout.y.length !== 0 || r.dispatchLayout.hasOwnProperty("z") && r.dispatchLayout.z.length !== 0);
}
var Wv = {};
Ke(Wv, { GPUBytesPerElement: () => Kg, MatMulProgramType: () => Mo, assertNotComplex: () => lm, computeDispatch: () => K, computeWorkPerThreadForConv2d: () => pm, computeWorkgroupInfoForMatMul: () => Vv, computeWorkgroupSizeForConv2d: () => um, flatDispatchLayout: () => X, isWebGPUSupported: () => cm, tilesFitEvenlyIntoShape: () => Zse });
var Ep = (r) => {
let e = 1;
for (let t8 = 0; t8 < r.length; t8++)
e *= r[t8];
return e;
};
function Zse(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((t8, o) => t8 % r[o] === 0);
}
function K(r, e, t8 = [1, 1, 1], o = [1, 1, 1]) {
let [n, s, a] = [Math.ceil(Ep(r.x.map((i) => e[i])) / (t8[0] * o[0])), r.y ? Math.ceil(Ep(r.y.map((i) => e[i])) / (t8[1] * o[1])) : 1, r.z ? Math.ceil(Ep(r.z.map((i) => e[i])) / (t8[2] * o[2])) : 1];
return [n, s, a];
}
function Vv(r, e, t8, o = false) {
let n = [8, 8, 1], s = [4, 4, 1];
return o || (r <= 8 && (s[1] = 1), e <= 16 && t8 <= 16 && (n[0] = 4)), { workgroupSize: n, elementsPerThread: s };
}
function um(r, e, t8 = false) {
if (t8)
return [8, 8, 1];
let o = Ep(r.x.map((s) => e[s])), n = Ep(r.y.map((s) => e[s]));
return o <= 4 ? [4, 16, 1] : n <= 4 ? [16, 4, 1] : [16, 16, 1];
}
function pm(r, e, t8 = false) {
if (t8)
return [4, 4, 1];
let o = Ep(r.x.map((s) => e[s])), n = Ep(r.y.map((s) => e[s]));
return o <= 4 ? [1, 2, 1] : n <= 4 ? [2, 1, 1] : [2, 2, 1];
}
function X(r) {
return { x: r.map((e, t8) => t8) };
}
function Kg(r) {
if (r === "float32" || r === "int32" || r === "bool" || r === "string")
return 4;
if (r === "complex64")
return 8;
throw new Error(`Unknown dtype ${r}`);
}
function cm() {
return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
}
function lm(r, e) {
Array.isArray(r) || (r = [r]), r.forEach((t8) => {
t8 != null && y.assert(t8.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGPU backend.`);
});
}
var Mo;
(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";
})(Mo || (Mo = {}));
var Jse = A().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
var eae = (r, e) => {
let t8 = r.limits.maxComputeWorkgroupsPerDimension, o = e.dispatchLayout, n = e.dispatch;
if (n.every((a) => a <= t8))
return n;
y.assert(n[0] > t8 && 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 > t8 ? (s = Math.ceil(Math.cbrt(n[0])), y.assert(s <= t8, () => "Total dispatch size exceeds WebGPU maximum."), [s, s, s]) : [s, s, 1];
};
var vu = class extends so {
nextDataId() {
return vu.nextDataId++;
}
constructor(e, t8) {
if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.dispatchCountInPass = 0, this.disposed = false, this.downloadWaitMs = 0, this.tensorDataPendingDisposal = [], this.queryResolveBuffer = null, this.querySet = null, this.querySetCount = 2, this.stagingPendingDisposal = [], this.uniformPendingDisposal = [], this.uploadWaitMs = 0, this.hasReadSyncWarned = false, this.hasTimestampQueryWarned = false, !cm())
throw new Error("WebGPU is not supported on this device");
this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.commandEncoder = null, this.computePassEncoder = null, this.adapterInfo = new Ug(t8), this.supportTimestampQuery = this.device.features.has("timestamp-query"), this.thresholdToIncreaseWorkgroups = this.adapterInfo.intelGPUGeneration >= 12 ? 16 : 8, this.bufferManager = new Gg(this.device), this.textureManager = new Hg(this.device), this.tensorMap = new Bo(this, ur()), A().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;
}
disposeData(e, t8 = false) {
if (!this.tensorMap.has(e))
return true;
let o = this.tensorMap.get(e);
return t8 ? o.refCount = 0 : o.refCount--, o.refCount > 0 ? false : (o.complexTensorInfos != null && (this.disposeData(o.complexTensorInfos.real.dataId), this.disposeData(o.complexTensorInfos.imag.dataId)), this.commandQueueOwnedIds.has(e) ? (this.tensorDataPendingDisposal.push(e), true) : (this.releaseResource(e), this.tensorMap.delete(e), true));
}
memory() {
return { numBytesInGPU: this.bufferManager.numBytesUsed, numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated, unreliable: false };
}
releaseResource(e) {
let t8 = this.tensorMap.get(e);
if (!(!t8 || !t8.resource)) {
if (t8.external) {
t8.resource = null;
return;
}
t8.resource instanceof GPUBuffer ? this.bufferManager.releaseBuffer(t8.resource) : t8.resource instanceof GPUTexture && this.textureManager.releaseTexture(t8.resource), t8.resource = null;
}
}
refCount(e) {
return this.tensorMap.has(e) ? this.tensorMap.get(e).refCount : 0;
}
incRef(e) {
let t8 = this.tensorMap.get(e);
t8.refCount++;
}
decRef(e) {
if (this.tensorMap.has(e)) {
let t8 = this.tensorMap.get(e);
t8.refCount--;
}
}
write(e, t8, 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: t8, values: e, refCount: 1 }), n;
}
move(e, t8, 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: t8, refCount: s });
}
submitQueue() {
this.queue.submit([this.commandEncoder.finish()]), this.commandEncoder = null, this.dispatchCountInPass = 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)), this.stagingPendingDisposal.forEach((e) => this.bufferManager.releaseBuffer(e, false)), this.tensorDataPendingDisposal = [], this.uniformPendingDisposal = [], this.stagingPendingDisposal = [];
}
ensureCommandEncoderReady() {
this.commandEncoder || (this.commandEncoder = this.device.createCommandEncoder());
}
endComputePassEncoder() {
this.computePassEncoder && (this.computePassEncoder.end(), this.computePassEncoder = null);
}
async checkCompileCompletionAsync() {
let e;
try {
e = await Promise.all(Object.values(this.pipelineCache));
} catch (t8) {
throw new Error(t8.message);
}
Object.keys(this.pipelineCache).map((t8, o) => {
this.pipelineCache[t8] = e[o];
});
}
async getBufferData(e) {
if (A().getBool("WEBGPU_ENGINE_COMPILE_ONLY"))
return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"), null;
let t8 = e.size, o = this.bufferManager.acquireBuffer(t8, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);
this.ensureCommandEncoderReady(), this.endComputePassEncoder(), this.commandEncoder.copyBufferToBuffer(e, 0, o, 0, t8), this.submitQueue(), await o.mapAsync(GPUMapMode.READ);
let n = o.getMappedRange().slice(0);
return o.unmap(), o != null && this.bufferManager.releaseBuffer(o), A().getBool("WEBGPU_USE_PROFILE_TOOL") && (y.assert(this.dummyContext !== void 0, () => "Fail to get context for profiling tool"), this.dummyContext.getCurrentTexture()), n;
}
convertAndCacheOnCPU(e, t8) {
let o = this.tensorMap.get(e);
return o.values = t8, o.values;
}
readSync(e) {
let t8 = this.tensorMap.get(e), { values: o, complexTensorInfos: n } = t8;
if (o != null || t8.dtype === "string")
return o;
if (t8.dtype === "complex64") {
let h = this.readSync(n.real.dataId), g = this.readSync(n.imag.dataId), x = y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h, g).buffer, "float32");
return this.convertAndCacheOnCPU(e, x), x;
}
this.hasReadSyncWarned || (this.hasReadSyncWarned = true, console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead."));
let s = ["opaque", "premultiplied"], a = t8.resource, 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.endComputePassEncoder(), 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, P) => {
this.ensureCommandEncoderReady(), this.commandEncoder.copyBufferToTexture({ buffer: a, bytesPerRow: x, offset: P }, { 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, P, 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;
}
}, C = Math.floor(p / (c * l)), S = c, k = l, _ = 0;
for (let R = 0; R < C; 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, t8.dtype);
return this.convertAndCacheOnCPU(e, f), f;
}
async read(e) {
if (!this.tensorMap.has(e))
throw new Error(`Tensor ${e} was not registered!`);
let t8 = this.tensorMap.get(e), { values: o } = t8;
if (o != null)
return o;
let n;
if (t8.dtype === "complex64") {
let s = await Promise.all([this.read(t8.complexTensorInfos.real.dataId), this.read(t8.complexTensorInfos.imag.dataId)]), a = s[0], i = s[1];
n = w.mergeRealAndImagArrays(a, i);
} else {
let s = await this.getBufferData(t8.resource);
n = y.convertBackendValuesAndArrayBuffer(s, t8.dtype);
}
return this.convertAndCacheOnCPU(e, n), n;
}
copyBuffer(e) {
let t8 = e.size, o = e.usage, n = this.bufferManager.acquireBuffer(t8, o);
return this.ensureCommandEncoderReady(), this.endComputePassEncoder(), this.commandEncoder.copyBufferToBuffer(e, 0, n, 0, t8), this.submitQueue(), n;
}
createTensorFromGPUData(e, t8, o) {
let n = e.buffer;
if (o === "complex64")
throw new Error("Cannot write to a complex64 dtype. ");
let s = { id: this.nextDataId() };
this.tensorMap.set(s, { dtype: o, shape: t8, values: null, refCount: 1, external: e.zeroCopy });
let a = this.tensorMap.get(s), i = Kg(a.dtype) * y.sizeFromShape(a.shape);
if (e.buffer.size < i)
throw new Error(`GPUBuffer size(${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)), a.resource = n, ur().makeTensorFromDataId(s, t8, o, this);
}
readToGPU(e) {
let t8 = this.tensorMap.get(e), { values: o, dtype: n, shape: s, resource: a } = t8;
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, p = i.size, u = i.usage, c = this.bufferManager.acquireBuffer(p, u);
this.ensureCommandEncoderReady(), this.endComputePassEncoder(), this.commandEncoder.copyBufferToBuffer(a, 0, c, 0, p), this.submitQueue();
let l = this.makeTensorInfo(s, n), m = ur().makeTensorFromTensorInfo(l), d = this.tensorMap.get(l.dataId);
return d.resource = c, { tensorRef: m, buffer: c };
}
bufferSync(e) {
let t8 = this.readSync(e.dataId);
if (e.dtype === "string")
try {
let o = t8.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, t8);
}
async time(e) {
!this.supportTimestampQuery && !this.hasTimestampQueryWarned && (console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."), this.hasTimestampQueryWarned = true);
let t8 = 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 = t8, 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, t8, o) {
return t8 === "string" && o != null && o.length > 0 && y.isString(o[0]) && (o = o.map((s) => y.encodeString(s))), { dataId: this.write(o, e, t8), shape: e, dtype: t8 };
}
tensorToBinding(e) {
if (!e)
return null;
let o = this.tensorMap.get(e.dataId).resource;
return o instanceof GPUBuffer ? { buffer: o } : o instanceof GPUTexture ? o.createView() : o;
}
uploadToGPU(e) {
let t8 = this.tensorMap.get(e);
if (t8.resource != null)
return;
let o = Kg(t8.dtype) * y.sizeFromShape(t8.shape), n, s = GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST;
if (t8.values) {
if (n = this.bufferManager.acquireBuffer(o, s, true), n.mapState === "unmapped") {
let a = this.bufferManager.acquireBuffer(o, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, true, false), i = a.getMappedRange();
t8.dtype === "int32" || t8.dtype === "bool" ? new Int32Array(i).set(t8.values) : new Float32Array(i).set(t8.values), a.unmap(), this.ensureCommandEncoderReady(), this.endComputePassEncoder(), this.commandEncoder.copyBufferToBuffer(a, 0, n, 0, o), this.stagingPendingDisposal.push(a);
} else {
let a = n.getMappedRange();
t8.dtype === "int32" || t8.dtype === "bool" ? new Int32Array(a).set(t8.values) : new Float32Array(a).set(t8.values), n.unmap();
}
t8.values = null;
} else
n = this.bufferManager.acquireBuffer(o, s);
t8.resource = n;
}
makeUniforms(e) {
let t8 = 0, o = 0, n = [], s = 1;
e.forEach((p) => {
p.data.length === 0 && (p.data = [1]);
let u;
switch (p.data.length) {
case 1:
u = 4;
break;
case 2:
u = 8;
break;
case 3:
u = 16;
break;
case 4:
u = 16;
break;
case 5:
u = 16;
break;
case 6:
u = 16;
break;
default:
y.assert(false, () => `Unsupported ${p.data.length}D shape`);
}
(o === 5 || o === 6) && (u = 16), u > s && (s = u), t8 = Math.ceil(t8 / u) * u, o = p.data.length, n.push(t8), t8 += p.data.length * 4;
}), t8 = Math.ceil(t8 / s) * s;
let a = new ArrayBuffer(t8);
e.forEach((p, u) => {
let c = n[u];
p.type === "int32" ? new Int32Array(a, c, p.data.length).set(p.data) : p.type === "uint32" ? new Uint32Array(a, c, p.data.length).set(p.data) : new Float32Array(a, c, p.data.length).set(p.data);
});
let i = this.bufferManager.acquireBuffer(t8, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);
return this.queue.writeBuffer(i, 0, a, 0, t8), this.uniformPendingDisposal.push(i), { offset: 0, size: t8, buffer: i };
}
runWebGPUProgram(e, t8, 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 = eae(this.device, e);
let a = t8.map((p, u) => {
if (p.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(p.dataId), { dtype: this.tensorMap.get(p.dataId).dtype, shape: p.shape, name: e.variableNames[u] };
});
e.shaderKey = UB(e, a, s);
let i = A().getBool("WEBGPU_ENGINE_COMPILE_ONLY");
return e.shaderKey in this.pipelineCache || (this.pipelineCache[e.shaderKey] = WB(this.device, e, a, s, i)), e.pipeline = this.pipelineCache[e.shaderKey], i || this.recordAndSubmit(e, s, t8, n), s;
}
recordAndSubmit(e, t8, o, n) {
if (e.pipeline instanceof Promise)
throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");
let s = [], a = [];
if (!e.isFromPixels) {
s.push({ type: "float32", data: [NaN] }, { type: "float32", data: [1 / 0] }), a = o.concat(t8).map((m) => m.shape);
let l = "int32";
if (a.map((m) => {
s.push({ type: l, data: m });
let d = y.computeStrides(m);
s.push({ type: l, data: d });
}), e.size) {
let m = y.sizeFromShape(e.outputShape);
s.push({ type: l, data: [e.outputComponent ? m / e.outputComponent : m] });
}
}
n && (s = [...s, ...n]);
let i = [this.tensorToBinding(t8), ...o.map((l) => this.tensorToBinding(l)), this.makeUniforms(s)];
o.forEach((l) => {
this.commandQueueOwnedIds.add(l.dataId);
}), this.commandQueueOwnedIds.add(t8.dataId);
let p = this.device.createBindGroup({ layout: e.pipeline.getBindGroupLayout(0), entries: i.map((l, m) => ({ binding: m, resource: l })) }), u = this.activeTimers != null;
this.ensureCommandEncoderReady();
let c = {};
u && this.supportTimestampQuery ? (this.endComputePassEncoder(), this.querySet == null && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: this.querySetCount })), c.timestampWrites = [{ querySet: this.querySet, queryIndex: 0, location: "beginning" }, { querySet: this.querySet, queryIndex: 1, location: "end" }], this.computePassEncoder = this.commandEncoder.beginComputePass(c)) : this.computePassEncoder || (this.computePassEncoder = this.commandEncoder.beginComputePass(c)), this.computePassEncoder.setPipeline(e.pipeline), this.computePassEncoder.setBindGroup(0, p), this.computePassEncoder.dispatchWorkgroups(e.dispatch[0], e.dispatch[1], e.dispatch[2]), this.dispatchCountInPass++, (u || A().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchCountInPass) && (this.endComputePassEncoder(), u ? this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime() }) : this.submitQueue());
}
async getQueryTime() {
if (!this.supportTimestampQuery)
return 0;
this.queryResolveBuffer == null && (this.queryResolveBuffer = this.bufferManager.acquireBuffer(this.querySetCount * 8, GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST | GPUBufferUsage.QUERY_RESOLVE)), this.commandEncoder.resolveQuerySet(this.querySet, 0, this.querySetCount, this.queryResolveBuffer, 0);
let e = this.bufferManager.acquireBuffer(this.querySetCount * 8, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);
this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer, 0, e, 0, this.querySetCount * 8), this.submitQueue(), await e.mapAsync(GPUMapMode.READ);
let t8 = new BigUint64Array(e.getMappedRange()), o = Number(t8[1] - t8[0]) / 1e6;
return e.unmap(), this.bufferManager.releaseBuffer(e), o;
}
shouldExecuteOnCPU(e, t8 = Jse) {
return A().getBool("WEBGPU_CPU_FORWARD") && e.every((o) => this.tensorMap.get(o.dataId).resource == null && y.sizeFromShape(o.shape) < t8);
}
numDataIds() {
return this.tensorMap.numDataIds() - this.tensorDataPendingDisposal.length;
}
dispose() {
this.disposed || (this.querySet != null && this.querySet.destroy(), this.bufferManager.dispose(), this.textureManager.dispose(), this.disposed = true);
}
};
vu.nextDataId = 0;
cm() && nu("webgpu", async () => {
let r = { powerPreference: A().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, e = await navigator.gpu.requestAdapter(r), t8 = {};
e.features.has("timestamp-query") && (t8.requiredFeatures = ["timestamp-query"]);
let o = e.limits;
t8.requiredLimits = { maxComputeWorkgroupStorageSize: o.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: o.maxComputeWorkgroupsPerDimension, maxStorageBufferBindingSize: o.maxStorageBufferBindingSize, maxBufferSize: o.maxBufferSize, maxComputeWorkgroupSizeX: o.maxComputeWorkgroupSizeX, maxComputeInvocationsPerWorkgroup: o.maxComputeInvocationsPerWorkgroup };
let n = await e.requestDevice(t8), s = await e.requestAdapterInfo();
return new vu(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 tae = "return a + b;";
var rae = "var resultTemp = atan2(a, b);";
var oae = "return areal * breal - aimag * bimag;";
var nae = "return areal * bimag + aimag * breal;";
var sae = "return a / b;";
var aae = "return select(a * (b + 1.0), a, b >= 0.);";
var iae = "return select(a * (b + vec4<f32>(1.0)), a, b >= vec4<f32>(0.));";
var uae = "return f32(a == b);";
var pae = "return vec4<f32>(a == b);";
var cae = "return f32(a > b);";
var lae = "return vec4<f32>(a > b);";
var mae = "return f32(a >= b);";
var dae = "return vec4<f32>(a >= b);";
var fae = `
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`;
var hae = `
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 gae = "return f32(a < b);";
var xae = "return vec4<f32>(a < b);";
var yae = "return f32(a <= b);";
var bae = "return vec4<f32>(a <= b);";
var Cae = "return f32(a >= 1.0 && b >= 1.0);";
var wae = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`;
var Sae = "return f32(a >= 1.0 || b >= 1.0);";
var Iae = `return min(vec4<f32>(a >= vec4<f32>(1.0)) +
vec4<f32>(b >= vec4<f32>(1.0)), vec4<f32>(1.0));`;
var vae = "var resultTemp = max(a, b);";
var kae = "var resultTemp = min(a, b);";
var Nae = `
let isNaN = b == 0.;
var resultTemp = a % b;
resultTemp = select((resultTemp + b) % b, resultTemp,
(a < 0. && b < 0.) || (a >= 0. && b > 0.));
`;
var Tae = `
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 _ae = "return a * b;";
var $ae = `
var resultTemp = f32(a != b);
let valueForNaN = 1.0;
`;
var Eae = `
var resultTemp = vec4<f32>(a != b);
let valueForNaN = 1.0;
`;
var Rae = `
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 Dae = `
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 Aae = "if (a < 0.0) { return b * a; } return a;";
var Fae = `
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;
var Pae = "return (a - b) * (a - b);";
var Oae = "return a - b;";
function qc(r, e) {
let t8;
do {
switch (r) {
case fe.ATAN2:
t8 = rae;
break;
case fe.MAX:
t8 = vae;
break;
case fe.MIN:
t8 = kae;
break;
case fe.MOD:
t8 = e ? Tae : Nae;
break;
case fe.NOT_EQUAL:
t8 = e ? Eae : $ae;
break;
case fe.POW:
t8 = e ? Dae : Rae;
break;
default:
continue;
}
let o, n, s;
return e ? (o = "isnanVec4", n = "vec4<f32>", s = "vec4<bool>") : (o = "isnan", n = "f32", s = "bool"), `
let aIsNaN = ${o}(a);
let aPostLegalization = select(a, ${n}(42), aIsNaN);
let bIsNaN = ${o}(b);
let bPostLegalization = select(b, ${n}(42), bIsNaN);
let isNaN = false;
let valueForNaN = uniforms.NAN;
{
let a = aPostLegalization;
let b = bPostLegalization;
${t8}
return select(
resultTemp, ${n}(valueForNaN),
${s}(isNaN) | aIsNaN | bIsNaN);
}
`;
} while (false);
switch (r) {
case fe.ADD:
return tae;
case fe.COMPLEX_MULTIPLY_IMAG:
return nae;
case fe.COMPLEX_MULTIPLY_REAL:
return oae;
case fe.DIV:
return sae;
case fe.ELU_DER:
return e ? iae : aae;
case fe.EQUAL:
return e ? pae : uae;
case fe.GREATER:
return e ? lae : cae;
case fe.GREATER_EQUAL:
return e ? dae : mae;
case fe.INT_DIV:
return e ? hae : fae;
case fe.LESS:
return e ? xae : gae;
case fe.LESS_EQUAL:
return e ? bae : yae;
case fe.LOGICAL_AND:
return e ? wae : Cae;
case fe.LOGICAL_OR:
return e ? Iae : Sae;
case fe.MUL:
return _ae;
case fe.PRELU:
return e ? Fae : Aae;
case fe.SQUARED_DIFFERENCE:
return Pae;
case fe.SUB:
return Oae;
default:
}
return `
${t8}
return resultTemp;
`;
}
var Z;
(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";
})(Z || (Z = {}));
var Mae = "return abs(a);";
var Lae = `
if (abs(a) > 1.) {
return uniforms.NAN;
}
return acos(a);
`;
var Bae = `
if (a < 1.) {
return uniforms.NAN;
}
return acosh(a);
`;
var zae = `
if (abs(a) > 1.) {
return uniforms.NAN;
}
return asin(a);
`;
var Vae = "return asinh(a);";
var Wae = `
if (isnan(a)) {
return uniforms.NAN;
}
return atan(a);
`;
var Uae = `
if (abs(a) > 1.) {
return uniforms.NAN;
}
if (a == 1.) {
return uniforms.INFINITY;
}
if (a == -1.) {
return -uniforms.INFINITY;
}
return atanh(a);
`;
var Gae = "return ceil(a);";
var Hae = "return cos(a);";
var Kae = `
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`;
var qae = "return exp(a) - 1.0;";
var jae = "if (a >= 0.0) { return a; } return (exp(a) - 1.0);";
var Xae = `
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 Yae = `
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
let p = ${w.ERF_P};
let a1 = ${w.ERF_A1};
let a2 = ${w.ERF_A2};
let a3 = ${w.ERF_A3};
let a4 = ${w.ERF_A4};
let a5 = ${w.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 Qae = "return exp(a);";
var Zae = "return floor(a);";
var Jae = "return f32(!isnan(a) && !isinf(a));";
var eie = "return f32(isinf(a));";
var tie = "return f32(isnan(a));";
var rie = "return a;";
var oie = `if (a < 0.0) { return uniforms.NAN; }
return log(a);`;
var nie = `
if (isnan(a)) { return a; }
return log(1.0 + a);
`;
var sie = "return f32(!(a >= 1.0));";
var aie = "return -a;";
var iie = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
var uie = `
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;
var pie = "return 1.0 / a;";
var cie = "return select(a, 0.0, a < 0.0);";
var lie = "return clamp(a, 0.0, 6.0);";
var mie = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
var die = `
return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
`;
var fie = "return round(a);";
var hie = "return inverseSqrt(a);";
var gie = `
if (a >= 0.0) {
return ${w.SELU_SCALE} * a;
} else {
return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0);
}
`;
var xie = "return 1.0 / (1.0 + exp(-1.0 * a));";
var yie = "return sign(a);";
var bie = "return sin(a);";
var Cie = `
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`;
var wie = `
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 Sie = "return sqrt(a);";
var Iie = "return a * a;";
var vie = `
if (isnan(a)) {
return a;
}
return select(uniforms.stepAlpha, 1.0, a > 0.0);
`;
var kie = "return tan(a);";
var Nie = `
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`;
var Tie = "return f32(i32((a)));";
function wi(r, e) {
switch (r) {
case Z.ABS:
return Mae;
case Z.ACOS:
return Lae;
case Z.ACOSH:
return Bae;
case Z.ASIN:
return zae;
case Z.ASINH:
return Vae;
case Z.ATAN:
return Wae;
case Z.ATANH:
return Uae;
case Z.COS:
return Hae;
case Z.COSH:
return Kae;
case Z.CEIL:
return Gae;
case Z.ELU:
return e ? Xae : jae;
case Z.ERF:
return Yae;
case Z.EXP:
return Qae;
case Z.EXPM1:
return qae;
case Z.FLOOR:
return Zae;
case Z.IS_FINITE:
return Jae;
case Z.IS_INF:
return eie;
case Z.IS_NAN:
return tie;
case Z.LINEAR:
return rie;
case Z.LOG:
return oie;
case Z.LOG1P:
return nie;
case Z.LOGICAL_NOT:
return sie;
case Z.NEG:
return aie;
case Z.LEAKYRELU:
return e ? uie : iie;
case Z.RECIPROCAL:
return pie;
case Z.RELU:
return e ? die : cie;
case Z.RELU6:
return e ? mie : lie;
case Z.ROUND:
return fie;
case Z.RSQRT:
return hie;
case Z.SELU:
return gie;
case Z.SIGMOID:
return xie;
case Z.SIGN:
return yie;
case Z.SIN:
return bie;
case Z.SINH:
return Cie;
case Z.SOFTPLUS:
return wie;
case Z.SQRT:
return Sie;
case Z.SQUARE:
return Iie;
case Z.STEP:
return vie;
case Z.TAN:
return kie;
case Z.TANH:
return Nie;
case Z.TO_INT:
return Tie;
default:
throw new Error(`BinaryType ${r} is not implemented!`);
}
}
function dr(r, e = false, t8 = false, o = 3) {
if (r === null)
return "";
let n = "";
if (r === "linear")
n = wi(Z.LINEAR);
else if (r === "relu")
n = wi(Z.RELU, t8);
else if (r === "elu")
n = wi(Z.ELU, t8);
else if (r === "relu6")
n = wi(Z.RELU6, t8);
else if (r === "prelu")
n = qc(fe.PRELU, t8);
else if (r === "sigmoid")
n = wi(Z.SIGMOID, t8);
else if (r === "leakyrelu")
n = wi(Z.LEAKYRELU, t8);
else
throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);
let a = Ae(t8 ? 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 Qr(r, e) {
return `
${r ? "value = value + getBiasByOutputCoords(coords);" : ""}
${e ? "value = activation(value, coords);" : ""}
`;
}
function Uv(r, e, t8 = 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};
${t8 && 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 mm(r, e, t8, o, n = false, s = false, a = false, i = 1) {
return `
${Uv(t8, 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);
${Qr(r, e)}
setOutputAtCoords(coords[0], coords[1], coords[2], value);
}
}
`;
}
var _ie = (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 $ie = (r, e, t8) => 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 < ${t8}; 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 < ${t8}; 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 Rp(r, e, t8 = false, o = 32, n = false, s = 32, a = false) {
let i = e[1] * r[1], p = e[0] * r[0], u = t8 ? i : o, c = t8 ? o : i, l = u / e[0], m = o / e[1], d = r[1];
return y.assert((t8 && l === 4 && r[1] === 4 || !t8 && (l === 3 || l === 4)) && u % e[0] === 0 && o % e[1] === 0 && r[0] === 4, () => `If transposeA ${t8} 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}>;
${H()} {
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;
${_ie(t8, 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];`}
${$ie(t8, l, d)}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < ${d}; innerRow++) {
mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
}
}`;
}
var HB = (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 Eie = (r) => r ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];";
function Dp(r, e, t8 = false, o = 32, n = false, s = 32, a = false, i = false) {
let p = r[1] * e[1], u = r[0] * e[0], c = t8 ? p : o, l = t8 ? 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]}) {
${HB(t8)}
}
}
// 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 = ${t8 ? `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;
${HB(t8)}
}
}
// 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++) {
${Eie(t8)}
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}>;
${H()} {
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 Rie = (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 Die(r, e = false) {
y.assert(r[1] === 1 && r[2] === 1, () => `A linear work group size is required. But got ${r}.`);
let t8 = r[0] * 4;
return `
var<workgroup> mm_Asub : array<vec4<f32>, ${r[0]}>;
${H()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / ${t8} + 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 * ${t8} + tileCol * 4;
mm_Asub[tileCol] = vec4<f32>(${Rie(e)});
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${t8 / 4}; k++) {
let rowB = t * ${t8} + k * 4;
let BCached = vec4<f32>(mm_readB(batchB, rowB, globalCol),
mm_readB(batchB, rowB + 1, globalCol),
mm_readB(batchB, rowB + 2, globalCol),
mm_readB(batchB, rowB + 3, globalCol));
let ACached = mm_Asub[k];
acc = acc + dot(ACached, BCached);
}
workgroupBarrier();
}
mm_write(batch, globalRow, globalCol, acc);
}
`;
}
var qg = class {
constructor(e, t8, o = false, n = false, s = null, a = null, i = null, p = false) {
this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t8, this.dispatchLayout = { x: [2], y: [1], z: [0] };
let u = o ? e[1] : e[2];
if (this.isVec4 = (u % 4 === 0 && !o || t8[1] % 4 === 0 && o) && t8[2] % 4 === 0 && !n, this.outputComponent = this.isVec4 ? 4 : 1, this.isVectorA = t8[1] === 1 && !o, !this.isVec4 && this.isVectorA)
this.elementsPerThread = [1, 1, 1], this.workgroupSize = [32, 1, 1];
else {
let m = Vv(t8[1], u, t8[2], o);
this.workgroupSize = m.workgroupSize, this.elementsPerThread = m.elementsPerThread;
}
this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread);
let c = s != null, l = i != null;
c && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.sequentialAccessByThreads = p, this.transposeA = o, this.transposeB = n, this.addBias = c, this.activation = a, this.hasPreluActivationWeights = l, [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(t8[1], t8[2], u), this.shaderKey = `matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`;
}
getShapeFit(e, t8, 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 = t8 % s === 0, p = o % this.tileInner === 0;
return [a, i, p];
}
getUserCode() {
return `
${dr(this.activation, this.hasPreluActivationWeights, this.isVec4)}
${mm(this.addBias, this.activation, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)}
${this.isVec4 ? Rp(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, true) : this.isVectorA ? Die(this.workgroupSize, this.transposeA) : Dp(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.sequentialAccessByThreads, true)}
`;
}
};
function Aie(r) {
return `
var<workgroup> sumValues : array<f32, ${r}>;
${H()} {
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 = ${r / 2}u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`;
}
var jg = class {
constructor(e, t8 = false, o = false, n = null, s = null, a = null) {
this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize);
let i = n != null, p = a != null;
i && this.variableNames.push("bias"), p && this.variableNames.push("preluActivationWeights"), this.transposeA = t8, this.transposeB = o, this.addBias = i, this.activation = s, this.hasPreluActivationWeights = p, this.shaderKey = `matMulReduce_${this.activation}_${t8}_${o}`;
}
getUserCode() {
return `
${dr(this.activation, this.hasPreluActivationWeights)}
${mm(this.addBias, this.activation, this.transposeA, this.transposeB)}
${Aie(this.workgroupSize[0])}
`;
}
};
function Fie(r) {
let e = r[1], t8 = r[0], o = e > t8 ? e : t8;
return `
var<workgroup> mm_Asub : array<array<f32, ${o}>, ${e}>;
var<workgroup> mm_Bsub : array<array<f32, ${t8}>, ${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.
${H()} {
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 < ${o}; k = k + 1) {
acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];
}
workgroupBarrier();
}
mm_write(batch, globalRow, globalCol, acc);
}
`;
}
var Xg = class {
constructor(e, t8, o, n = false, s = false, a = null, i = null, p = null) {
this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [16, 8, 1], this.outputShape = o, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(o[2] / this.workgroupSize[0]), Math.ceil(o[1] / this.workgroupSize[1]), o[0]];
let u = a != null;
u && this.variableNames.push("bias");
let c = p != null;
c && this.variableNames.push("preluActivationWeights"), this.transposeA = n, this.transposeB = s, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = c, this.shaderKey = `matMulSmallOutputSize_${this.activation}_${n}_${s}`;
}
getUserCode() {
return `
${dr(this.activation, this.hasPreluActivationWeights)}
${mm(this.addBias, this.activation, this.transposeA, this.transposeB)}
${Fie(this.workgroupSize)}
`;
}
};
var Yg = class {
constructor(e, t8, o = false, n = false) {
this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [8, 8, 1], this.atomic = true, this.splitedDimInner = 128, y.assert(e[0] === 1, () => "MatMulSplitKProgram only supports batch = 1."), this.outputShape = e, this.dispatchLayout = { x: [2], y: [1], z: [0, 3] };
let s = (o && this.outputShape[1] % 4 === 0 || !o && t8 % 4 === 0) && this.outputShape[2] % 4 === 0;
this.elementsPerThread = [4, 4, this.splitedDimInner], this.outputComponent = s ? 4 : 1, s || (this.outputShape[1] < 16 && (this.elementsPerThread[1] = 1), this.outputShape[2] < 16 && (this.elementsPerThread[0] = 1)), this.dispatch = K(this.dispatchLayout, [this.outputShape[0], this.outputShape[1], this.outputShape[2], t8], this.workgroupSize, this.elementsPerThread), this.transposeA = o, this.transposeB = n, this.shaderKey = `matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`;
}
getUserCode() {
let e = this.outputComponent;
return `
${Uv(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) {
${Us("&result[flatIndex + i]", `${e > 1 ? "value[i]" : "value"}`, "float32")}
}
}
}
${e === 4 ? Rp(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner) : Dp(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner)}
`;
}
};
var Qg = class {
constructor(e, t8 = null, o = null, n = null) {
this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t8 != null, this.hasPreluActivationWeights = n != null, this.activation = o, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.shaderKey = `biasActivation_${o}`;
}
getUserCode() {
return `
${dr(this.activation, this.hasPreluActivationWeights)}
${H("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
var value = getXByOutputIndex(index);
${Qr(this.addBias, this.activation)}
setOutputAtIndex(index, value);
}
}
`;
}
};
var Zg = class {
constructor(e) {
this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "fill";
}
getUserCode() {
return `
${H("index")} {
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.value);
}
}
`;
}
};
function Vt(r) {
let { backend: e, attrs: t8 } = r, { shape: o, value: n } = t8, { dtype: s } = t8;
if (s = s || y.inferDtype(n), s === "string") {
let a = y.getArrayFromDType(s, y.sizeFromShape(o));
return a.fill(n), e.makeTensorInfo(o, s, a);
} else {
let a = new Zg(o), i = [{ type: "float32", data: [n] }];
return e.runWebGPUProgram(a, [], s, i);
}
}
var KB = { kernelName: na, backendName: "webgpu", kernelFunc: Vt };
function pe(r) {
let { inputs: e, attrs: t8 } = r, { x: o } = e, { shape: n } = t8, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s), i = y.sizeFromShape(a);
return y.assert(s === i, () => `The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype };
}
var qB = { kernelName: ma, backendName: "webgpu", kernelFunc: pe };
function Ap({ a: r, b: e, transposeA: t8, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) {
let u = r.shape.length, c = e.shape.length, l = t8 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t8 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Ir.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]);
y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t8} and transposeB=${o} must match.`);
let k = t8 ? [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], P = Math.max(x, b), O = [E, R], M = [{ type: "int32", data: [d] }, { type: "int32", data: [f] }, { type: "int32", data: [l] }], L, B, z = [P, d, f], U = A().get("WEBGPU_MATMUL_PROGRAM_TYPE");
if (U < 0) {
let q = A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), Y = q > 0 ? q : n.thresholdToIncreaseWorkgroups, J = P * Math.ceil(d / 32) * Math.ceil(f / 32);
J <= Y || d <= 8 && J <= Y * 2 ? P * d * f <= 128 ? U = Mo.MatMulReduceProgram : P === 1 && m >= 2e3 ? U = Mo.MatMulSplitKProgram : U = Mo.MatMulSmallOutputSizeProgram : U = Mo.MatMulPackedProgram;
}
switch (U) {
case Mo.MatMulReduceProgram:
L = new jg(z, t8, o, s, p, a);
break;
case Mo.MatMulSplitKProgram: {
if (B = Vt({ backend: n, attrs: { shape: z, value: 0, dtype: r.dtype } }), L = new Yg(z, m, t8, o), s || p) {
B = n.runWebGPUProgram(L, O, r.dtype, M, B);
let Y = new Qg(B.shape, s, p, a), J = null, re = [B];
s && re.push(s), a && re.push(a), p === "leakyrelu" && (J = [{ type: "float32", data: [i] }], Y.uniforms += " alpha : f32,");
let ne = n.runWebGPUProgram(Y, 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 Mo.MatMulSmallOutputSizeProgram:
L = new Xg(k, _, z, t8, o, s, p, a);
break;
case Mo.MatMulPackedProgram:
let q = n.adapterInfo.isIntel();
L = new qg(k, z, t8, o, s, p, a, q);
break;
default:
throw new Error(`Unsupported MatMulProgramType ${U}.`);
}
s && O.push(s), a && O.push(a), p === "leakyrelu" && (M.push({ type: "float32", data: [i] }), L.uniforms += " alpha : f32,"), B = n.runWebGPUProgram(L, O, r.dtype, M, B);
let j = pe({ inputs: { x: B }, backend: n, attrs: { shape: S } });
D.push(B);
for (let q of D)
n.disposeData(q.dataId);
return j;
}
function Pie(r) {
let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o;
return Ap({ a: n, b: s, transposeA: p, transposeB: u, backend: t8, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c });
}
var jB = { kernelName: So, backendName: "webgpu", kernelFunc: Pie };
var dm = class {
constructor(e, t8, o) {
this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = w.assertAndGetBroadcastShape(t8, o), this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e;
}
getUserCode() {
return `
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${qc(this.op, false)}
}
${H("index")} {
if(index < uniforms.size) {
let areal = getARealByOutputIndex(index);
let aimag = getAImagByOutputIndex(index);
let breal = getBRealByOutputIndex(index);
let bimag = getBImagByOutputIndex(index);
setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`;
}
};
var Si = class {
constructor(e, t8, o) {
if (this.size = true, this.variableNames = ["A", "B"], this.outputShape = w.assertAndGetBroadcastShape(t8, o), this.dispatchLayout = X(this.outputShape), this.op = e, this.useSharedMemoryWithA = t8.length <= 1 && o.length > 1 && t8[0] < 128, this.useSharedMemoryWithB = o.length <= 1 && t8.length > 1 && o[0] < 128, this.useSharedMemoryWithA || this.useSharedMemoryWithB)
this.outputComponent = 1, this.variableComponents = [1, 1], this.lastDimensionSize = this.useSharedMemoryWithB ? o[0] : t8[0], this.shaderKey = `binary_${e}_${this.lastDimensionSize}`, this.type = "shared", this.workgroupSize = [256, 1, 1];
else {
let n = t8.length > 0 && t8[t8.length - 1] % 4 === 0, s = o.length > 0 && o[o.length - 1] % 4 === 0;
n && s ? (this.outputComponent = 4, this.variableComponents = [4, 4]) : n && (y.isScalarShape(o) || o[o.length - 1] === 1) || s && (y.isScalarShape(t8) || t8[t8.length - 1] === 1) ? (this.outputComponent = 4, this.variableComponents = n ? [4, 1] : [1, 4]) : (this.outputComponent = 1, this.variableComponents = [1, 1]), this.type = "nonshared", this.shaderKey = `binary_${e}_${this.variableComponents}`, this.workgroupSize = [128, 1, 1];
}
this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.outputComponent, 1, 1]);
}
getUserCode() {
let e, t8 = this.outputComponent === 4 ? "vec4<f32>" : "f32", o = `
fn binaryOperation(a : ${t8}, b : ${t8}) -> ${t8} {
${qc(this.op, this.outputComponent === 4)}
};
`;
if (this.type === "shared") {
let n = this.lastDimensionSize > 1 ? `coords[${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}>;
${H("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}
${H("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index * ${this.outputComponent});
let a = ${t8}(getAByOutputCoords(coords));
let b = ${t8}(getBByOutputCoords(coords));
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`;
return e;
}
};
function At(r) {
let { inputs: e } = r, { x: t8 } = e;
return r.backend.incRef(t8.dataId), { dataId: t8.dataId, shape: t8.shape, dtype: t8.dtype };
}
var XB = { kernelName: Co, backendName: "webgpu", kernelFunc: At };
function xo(r) {
let { inputs: e, backend: t8 } = r, { real: o, imag: n } = e, s = t8.makeTensorInfo(o.shape, "complex64"), a = t8.tensorMap.get(s.dataId), i = At({ inputs: { x: o }, backend: t8 }), p = At({ inputs: { x: n }, backend: t8 });
return a.complexTensorInfos = { real: i, imag: p }, s;
}
var YB = { kernelName: Ri, backendName: "webgpu", kernelFunc: xo };
var Zr = class {
constructor(e, t8, o = "") {
this.variableNames = ["A"], this.size = true;
let n = 128;
this.workgroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.op = t8, o !== "" && (this.uniforms = o), this.shaderKey = `unary_${t8}`;
}
getUserCode() {
return `
fn unaryOperation(a : f32) -> f32 {
${wi(this.op, false)}
}
${H("index")} {
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
setOutputAtIndex(index, unaryOperation(a));
}
}
`;
}
};
function ye({ opType: r, cpuKernelImpl: e, dtype: t8 }) {
return ({ inputs: o, backend: n }) => {
let { x: s } = o, a = n, i = t8 || 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 Zr(s.shape, r);
return a.runWebGPUProgram(p, [s], i);
};
}
function et({ opType: r, cpuKernelImpl: e, supportsComplex: t8 = false, dtype: o }) {
return ({ inputs: n, backend: s }) => {
let { a, b: i } = n, p = s;
if (t8 && 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, C = { dataId: x.dataId, dtype: x.dtype, shape: a.shape }, S = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, k = new Si(r, a.shape, i.shape);
return p.runWebGPUProgram(k, [C, S], dt(x.dtype, b.dtype));
});
else {
let g = new dm(fe.COMPLEX_MULTIPLY_REAL, a.shape, i.shape), x = new dm(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 = xo({ 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" ? w.fromUint8ToStringArray(l) : l, f = a.dtype === "string" ? w.fromUint8ToStringArray(m) : m, [h, g] = e(a.shape, i.shape, d, f, u);
return p.makeTensorInfo(g, u, h);
}
let c = new Si(r, a.shape, i.shape);
return p.runWebGPUProgram(c, [a, i], u);
};
}
var { addImpl: QB, castImpl: ZB, ceilImpl: JB, concatImpl: ez, equalImpl: tz, expImpl: rz, expm1Impl: oz, floorImpl: nz, floorDivImpl: sz, gatherNdImpl: az, gatherV2Impl: iz, greaterEqualImpl: uz, greaterImpl: pz, lessEqualImpl: cz, lessImpl: lz, logImpl: mz, maxImpl: dz, maximumImpl: fz, minimumImpl: hz, multiplyImpl: gz, negImpl: xz, notEqualImpl: yz, prodImpl: bz, rangeImpl: Cz, rsqrtImpl: wz, scatterImpl: Sz, simpleAbsImpl: Iz, sliceImpl: vz, stridedSliceImpl: kz, stringNGramsImpl: Nz, subImpl: Tz, tileImpl: _z, topKImpl: $z, transposeImpl: Ez, uniqueImpl: NPt } = Ic;
var Oie = ye({ opType: Z.ABS, cpuKernelImpl: Iz });
var Rz = { kernelName: js, backendName: "webgpu", kernelFunc: Oie };
var Mie = ye({ opType: Z.ACOS });
var Dz = { kernelName: Vo, backendName: "webgpu", kernelFunc: Mie };
var Lie = ye({ opType: Z.ACOSH });
var Az = { kernelName: Wo, backendName: "webgpu", kernelFunc: Lie };
var Bie = et({ opType: fe.ADD, cpuKernelImpl: QB, supportsComplex: true });
var Fz = { kernelName: io, backendName: "webgpu", kernelFunc: Bie };
var Jg = class {
constructor(e) {
this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t8, o) => `T${o}`), this.dispatchLayout = X(this.outputShape), this.dispatch = K(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 t8 = this.variableNames.map((n) => `v${n}`).join(" + ");
return `
${H("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, ${t8});
}
}
}
`;
}
};
function zie(r) {
let { inputs: e, backend: t8 } = r, o = e;
if (o.length === 1)
return At({ inputs: { x: o[0] }, backend: t8 });
let n = o.map((i) => i.dtype).reduce((i, p) => dt(i, p)), s = o.map((i) => i.shape), a = new Jg(s);
return t8.runWebGPUProgram(a, o, n);
}
var Pz = { kernelName: Uo, backendName: "webgpu", kernelFunc: zie };
var ex = class {
constructor(e, t8) {
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[t8[n]];
this.outputShape = o, this.dispatchLayout = { x: [0], y: [1] }, this.dispatch = K(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]}>;
${H()} {
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) * ${e} + i32(localId.y);
if (x < height && y < width) {
setOutputAtIndex((y * height + x), tile[localId.x]
[localId.y]);
}
}
`;
}
};
var tx = class {
constructor(e, t8) {
this.variableNames = ["A"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true;
let o = new Array(e.length);
for (let n = 0; n < o.length; n++)
o[n] = e[t8[n]];
this.outputShape = o, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.newDim = t8, this.shaderKey = `transpose_${t8}`;
}
getUserCode() {
let e = ht(this.outputShape.length), t8 = Gv(this.newDim);
return `
${H("index")} {
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(
${e}(${t8}), uniforms.aShape)]);
}
}
}
`;
}
};
function Gv(r) {
let e = r.length;
if (e > 6)
throw Error(`Transpose for rank ${e} is not yet supported`);
let t8 = new Array(e);
for (let o = 0; o < r.length; o++)
t8[r[o]] = `coords.${Oo(o)}`;
return t8.join();
}
function yr(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t8, i = n.shape.length, p = new Array(i);
for (let c = 0; c < p.length; c++)
p[c] = n.shape[s[c]];
if (t8.shouldExecuteOnCPU([n])) {
let l = a.tensorMap.get(n.dataId).values, m = Ez(l, n.shape, n.dtype, s, p);
return t8.makeTensorInfo(p, n.dtype, m);
}
if (n.shape.length === 2 && y.arraysEqual(s, [1, 0])) {
let c = new ex(n.shape, s);
return a.runWebGPUProgram(c, [n], n.dtype);
}
let u = new tx(n.shape, s);
return a.runWebGPUProgram(u, [n], n.dtype);
}
var Oz = { kernelName: po, backendName: "webgpu", kernelFunc: yr };
var rx = class {
constructor(e, t8, o) {
this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [e.batchSize, e.inSize];
let [n] = w.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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t8, this.shaderKey = `reduce_${t8}`;
}
getUserCode() {
let e = "", t8 = "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; }`, t8 = "f32(x[offset])") : this.reduceType === "sum" || this.reduceType === "mean" ? e = " bestValue = bestValue + candidate; " : this.reduceType === "prod" ? (e = " bestValue = bestValue * candidate; ", t8 = "1.0") : this.reduceType === "all" ? (e = " bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ", t8 = "1.0") : this.reduceType === "any" && (e = " bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ", t8 = "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;
}
${H("index")} {
let outputIndex = index / ${o};
let offset = getOffset(outputIndex);
var bestValue = ${t8};
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) {
${n}
}
}
`;
}
};
function Jr(r, e, t8, o, n) {
let s = r.shape.length, a = [], i = y.parseAxisParam(e, r.shape), p = i, u = w.getAxesPermutation(p, s), c = r;
u != null && (c = yr({ inputs: { x: r }, attrs: { perm: u }, backend: n }), p = w.getInnerMostAxes(p.length, s), a.push(c)), w.assertAxesAreInnerMostDims(o, p, s);
let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = l;
t8 && (d = w.expandShapeToKeepDim(l, i));
let f;
if ((o === "max" || o === "prod") && n.shouldExecuteOnCPU([c])) {
let h = n.tensorMap.get(c.dataId).values;
switch (o) {
case "max":
let g = dz(h, y.sizeFromShape(m), d, r.dtype);
f = n.makeTensorInfo(d, r.dtype, g);
break;
case "prod":
let { outVals: x, outShape: b, outDtype: C } = bz(c.shape, c.dtype, h, p);
f = n.makeTensorInfo(b, C, x);
break;
default:
throw new Error(`${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 }, C = o === "mean" ? "float32" : ti(r.dtype), S = [{ type: "int32", data: [h] }], k = new rx(b, o, n.device.limits.maxComputeWorkgroupSizeX), _ = n.runWebGPUProgram(k, [c], C, S);
a.push(_), f = pe({ inputs: { x: _ }, attrs: { shape: d }, backend: n });
}
return a.forEach((h) => n.disposeData(h.dataId)), f;
}
function Vie(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o;
return Jr(n, a, s, "all", t8);
}
var Mz = { kernelName: Go, backendName: "webgpu", kernelFunc: Vie };
function Wie(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o;
return Jr(n, a, s, "any", t8);
}
var Lz = { kernelName: Ho, backendName: "webgpu", kernelFunc: Wie };
var jc = class {
constructor(e, t8, o) {
this.workgroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true;
let n = [t8];
this.op = o === "min" ? "<" : ">";
let [s, a] = w.computeOutAndReduceShapes(e, n);
this.outputShape = s.length === 0 ? [1] : s, this.dispatchLayout = X(this.outputShape), y.sizeFromShape(a) < 32 || y.sizeFromShape(s) > 1e3 ? (this.type = "plain", this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize)) : (this.type = "shared", this.dispatch = K(this.dispatchLayout, this.outputShape, [1, 1, 1])), this.inputShape = e, this.shaderKey = `argMinMax_${this.op}_${this.type}`;
}
getUserCode() {
let e = this.workgroupSize[0], t8 = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${Oo(this.inputShape.length - 1)}`, o = () => {
let n = "";
if (this.outputShape.length === 1)
this.inputShape.length !== 1 && (n += "outputCoords,");
else
for (let s = 0; s < this.outputShape.length; s++)
n += `outputCoords.${Oo(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}>;
`}
${H("index")} {
let outputIndex = index / ${e};
let reduceLength = ${t8()};
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]);
}
}
` : `
${H("index")} {
if (index < uniforms.size) {
let outputCoords = getCoordsFromIndex(index);
var bestIndex = 0;
var bestValue = getX(${o()} 0);
let reduceLength = ${t8()};
for (var i = 1; i < reduceLength; i++) {
let candidate = getX(${o()} i);
if (candidate ${this.op} bestValue) {
bestValue = candidate;
bestIndex = i;
}
}
setOutputAtIndexI32(index, bestIndex);
}
}
`;
}
};
function Uie(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = yr({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length);
let c = new jc(p.shape, a[0], "max"), l = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], m = t8.runWebGPUProgram(c, [p], "int32", l);
return u.forEach((d) => t8.disposeData(d.dataId)), m;
}
var Bz = { kernelName: Xs, backendName: "webgpu", kernelFunc: Uie };
function Gie(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = yr({ inputs: { x: n }, backend: t8, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length);
let c = new jc(p.shape, a[0], "min"), l = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], m = t8.runWebGPUProgram(c, [p], "int32", l);
return u.forEach((d) => t8.disposeData(d.dataId)), m;
}
var zz = { kernelName: Ys, backendName: "webgpu", kernelFunc: Gie };
var Hie = ye({ opType: Z.ASIN });
var Vz = { kernelName: Ko, backendName: "webgpu", kernelFunc: Hie };
var Kie = ye({ opType: Z.ASINH });
var Wz = { kernelName: qo, backendName: "webgpu", kernelFunc: Kie };
var qie = ye({ opType: Z.ATAN });
var Uz = { kernelName: jo, backendName: "webgpu", kernelFunc: qie };
var jie = et({ opType: fe.ATAN2 });
var Gz = { kernelName: Yo, backendName: "webgpu", kernelFunc: jie };
var Xie = ye({ opType: Z.ATANH });
var Hz = { kernelName: Xo, backendName: "webgpu", kernelFunc: Xie };
var ox = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne";
}
getUserCode() {
return `
${H("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d = coords[3];
let xRCCorner = coords.yz * uniforms.strides;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
let value = getX(batch, xRCorner, xCCorner, d);
setOutputAtIndex(index, value);
}
}
`;
}
};
var Ma = class {
constructor(e, t8, o = false, n = false, s = false) {
if (this.variableNames = ["x"], this.uniforms = "strides : vec2<i32>, pads : vec2<i32>, dilations : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,", this.workgroupSize = [128, 1, 1], this.size = true, t8 === "avg" && o)
throw new Error("Cannot compute positions for average pool.");
this.outputShape = e.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.poolType = t8, this.computePositions = o, this.flattenPositions = n, this.includeBatchIndex = s, this.shaderKey = `pool2D_${t8}_${o}_${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 t8 = "resultValue";
return this.poolType === "avg" && (t8 = "resultValue / max(count, 1.0)"), `
${H("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, ${t8});`}
}
}
`;
}
};
var ku = class {
constructor(e, t8, o = false, n = false, s = false) {
if (this.variableNames = ["x"], this.uniforms = "strides : vec3<i32>, pads : vec3<i32>, convDims : vec3<i32>, filterDims : vec3<i32>,", this.workgroupSize = [128, 1, 1], this.size = true, t8 === "avg" && o)
throw new Error("Cannot compute positions for average pool.");
this.outputShape = e.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.poolType = t8, this.computePositions = o, this.flattenPositions = n, this.includeBatchIndex = s, this.shaderKey = `pool3D_${t8}_${o}_${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 t8 = "resultValue";
return this.poolType === "avg" && (t8 = "resultValue / max(count, 1.0)"), `
${H("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, ${t8});`}
}
}
`;
}
};
function Hv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o;
return Jr(n, s, a, "max", t8);
}
var Kz = { kernelName: zn, backendName: "webgpu", kernelFunc: Hv };
function Kv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o;
return Jr(n, a, s, "mean", t8);
}
var qz = { kernelName: Un, backendName: "webgpu", kernelFunc: Kv };
function nx(r, e, t8, 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;
t8 === "avg" ? p = Kv({ inputs: { x: i }, backend: o, attrs: { axis: 0, keepDims: false } }) : (y.assert(t8 === "max", () => `Invalid pool type ${t8}`), p = Hv({ 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 ox(e) : (t8 === "avg" ? n = new Ma(e, "avg") : (y.assert(t8 === "max", () => `Invalid pool type ${t8}`), n = new Ma(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 Yie(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = w.computePool2DInfo(n.shape, s, a, u, i, p);
return nx(n, c, "avg", t8);
}
var jz = { kernelName: Qo, backendName: "webgpu", kernelFunc: Yie };
function Qie(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new ku(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 t8.runWebGPUProgram(m, [n], n.dtype, d);
}
var Xz = { kernelName: Qs, backendName: "webgpu", kernelFunc: Qie };
var sx = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avgPool2DBackprop";
}
getUserCode() {
return `
${H("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 ax = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avgPool3DBackprop";
}
getUserCode() {
return `
${H("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 Zie(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = w.computePool3DInfo(a.shape, i, p, 1, u, c), m = new ax(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 t8.runWebGPUProgram(m, [n], a.dtype, f);
}
var Yz = { kernelName: Ei, backendName: "webgpu", kernelFunc: Zie };
function Jie(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s;
lm([n, s], "avgPoolGrad");
let { filterSize: i, strides: p, pad: u } = o, c = w.computePool2DInfo(a.shape, i, p, 1, u), l = new sx(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 t8.runWebGPUProgram(l, [n], a.dtype, d);
}
var Qz = { kernelName: $i, backendName: "webgpu", kernelFunc: Jie };
function eue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
return Ap({ a: n, b: s, transposeA: a, transposeB: i, backend: t8 });
}
var Zz = { kernelName: Zo, backendName: "webgpu", kernelFunc: eue };
var ix = class {
constructor(e, t8) {
this.variableNames = ["source"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.rank = t8.length, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${ht(e.length)}, `, this.shaderKey = "slice";
}
getUserCode() {
let e = ht(this.rank), t8 = tue(this.rank), o;
return this.start.length === 1 ? o = this.outputShape.map((s, a) => "sourceLoc = uniforms.start + coords;") : o = this.outputShape.map((s, a) => `sourceLoc.${qv[a]} = uniforms.start.${Oo(a)} + coords.${qv[a]};`), `
${H("index")} {
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getCoordsFromIndex(index);
${o.join(`
`)}
setOutputAtIndex(index, getSource(${t8}));
}
}
`;
}
};
var qv = ["x", "y", "z", "w", "u", "v"];
function tue(r) {
if (r === 1)
return "sourceLoc";
if (r <= 6)
return qv.slice(0, r).map((e) => `sourceLoc.${e}`).join(",");
throw Error(`Slicing for rank ${r} is not yet supported`);
}
function Gs(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ct.parseSliceParams(n, s, a);
if (ct.assertParamsValid(n, i, p), t8.shouldExecuteOnCPU([n]) || n.dtype === "string") {
let l = t8.tensorMap.get(n.dataId), m = vz(l.values, i, p, n.shape, n.dtype);
return t8.makeTensorInfo(p, n.dtype, m);
}
if (y.sizeFromShape(p) === 0)
return t8.makeTensorInfo(p, n.dtype, []);
let u = new ix(i, p), c = [{ type: "int32", data: i }];
return t8.runWebGPUProgram(u, [n], n.dtype, c);
}
var Jz = { kernelName: fa, backendName: "webgpu", kernelFunc: Gs };
var rue = (r) => {
let { inputs: e, backend: t8, 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, C) => b * C), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = [], f = pe({ inputs: { x: n }, backend: t8, attrs: { shape: p } }), h = yr({ inputs: { x: f }, backend: t8, attrs: { perm: u } }), g = pe({ inputs: { x: h }, backend: t8, attrs: { shape: c } }), x = Gs({ inputs: { x: g }, backend: t8, attrs: { begin: l, size: m } });
return d.push(f), d.push(h), d.push(g), d.forEach((b) => t8.disposeData(b.dataId)), x;
};
var eV = { kernelName: Zs, backendName: "webgpu", kernelFunc: rue };
var oue = `
fn bincount_write(index: i32, value: f32) {
${Us("&result[index]", "value", "float32")}
}
`;
var nue = `
fn bincount_write(index: i32, value: f32) {
atomicStore(&result[index], bitcast<i32>(value));
}
`;
var Xc = class {
constructor(e, t8, o = false) {
this.outputShape = [], this.variableNames = ["x"], this.uniforms = "binCountSize : i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.hasWeights = true, this.binaryOutput = false, this.outputShape = e, this.rank = e.length, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.binaryOutput = o, o && (this.atomic = false), this.hasWeights = t8, this.hasWeights && this.variableNames.push("w"), this.shaderKey = `bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`;
}
getUserCode() {
return `
${this.binaryOutput ? nue : oue}
${H("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 sue(r) {
let { inputs: e, backend: t8, 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: t8, attrs: { shape: c, value: 0, dtype: l } }), d = new Xc([i], u), f = [{ type: "int32", data: [a] }], h = u ? [n, s] : [n];
return t8.runWebGPUProgram(d, h, l, f, m);
}
var tV = { kernelName: Jo, backendName: "webgpu", kernelFunc: sue };
var ux = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "broadcastArgs";
}
getUserCode() {
return `
${H("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 aue(r) {
let { inputs: e, backend: t8 } = r, { s0: o, s1: n } = e;
if (t8.shouldExecuteOnCPU([o, n])) {
let c = t8.tensorMap.get(o.dataId), l = t8.tensorMap.get(n.dataId), m = c.values, d = l.values, f = w.assertAndGetBroadcastShape(Array.from(m), Array.from(d));
return t8.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 ux(i), u = [{ type: "int32", data: [s] }, { type: "int32", data: [a] }];
return t8.runWebGPUProgram(p, [o, n], "int32", u);
}
var rV = { kernelName: Js, backendName: "webgpu", kernelFunc: aue };
var jv = et({ opType: fe.NOT_EQUAL, dtype: "bool", cpuKernelImpl: yz });
var oV = { kernelName: Yn, backendName: "webgpu", kernelFunc: jv };
function Ii(r) {
let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.tensorMap.get(o.dataId);
return At({ inputs: { x: n.complexTensorInfos.real }, backend: t8 });
}
var nV = { kernelName: Gi, backendName: "webgpu", kernelFunc: Ii };
function sV(r, e) {
let t8 = new Zr(r.shape, Z.TO_INT), o = e.runWebGPUProgram(t8, [r], "int32");
return { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
function Xv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dtype: s } = o;
if (s === "complex64") {
if (n.dtype === "complex64")
return At({ inputs: { x: n }, backend: t8 });
let a = Gr(n.shape), i = Xv({ inputs: { x: n }, backend: t8, attrs: { dtype: "float32" } }), p = xo({ inputs: { real: i, imag: a }, backend: t8 });
return a.dispose(), t8.disposeData(i.dataId), p;
}
if (n.dtype === "complex64") {
let a = Ii({ inputs: { input: n }, backend: t8 }), i = Xv({ inputs: { x: a }, backend: t8, attrs: { dtype: s } });
return t8.disposeData(a.dataId), i;
}
if (!y.hasEncodingLoss(n.dtype, s)) {
let a = At({ inputs: { x: n }, backend: t8 });
return { dataId: a.dataId, shape: a.shape, dtype: s };
}
if (t8.shouldExecuteOnCPU([n])) {
let a = t8.tensorMap.get(n.dataId).values, [i, p, u] = ZB(a, n.shape, n.dtype, s);
return t8.makeTensorInfo(i, p, u);
}
if (s === "int32")
return sV(n, t8);
if (s === "bool") {
let a = t8.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = jv({ inputs: { a: n, b: a }, backend: t8 });
return t8.disposeData(a.dataId), p;
}
throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`);
}
var aV = { kernelName: yo, backendName: "webgpu", kernelFunc: Xv };
var iue = ye({ opType: Z.CEIL, cpuKernelImpl: JB });
var iV = { kernelName: en, backendName: "webgpu", kernelFunc: iue };
var px = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4";
}
getUserCode() {
return `
${H("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 cx = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "clip";
}
getUserCode() {
return `
${H("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 uue(r) {
let { inputs: e, backend: t8, 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 px(n.shape) : i = new cx(n.shape), t8.runWebGPUProgram(i, [n], n.dtype, p);
}
var uV = { kernelName: bo, backendName: "webgpu", kernelFunc: uue };
var lx = class {
constructor(e) {
this.outputShape = [], this.variableNames = ["real", "imag"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "complexAbs";
}
getUserCode() {
return `
${H("index")} {
if (index < uniforms.size) {
let re = abs(getRealByOutputIndex(index));
let im = abs(getImagByOutputIndex(index));
let mx = max(re, im);
// The length function in wgsl may be not underflow-safe on some GPUs.
// So the safe solution is to ensure underflow-safety in all cases.
setOutputAtIndex(index, select(mx * length(vec2<f32>(1, min(re, im)/mx)), 0.0, mx == 0.0));
}
}
`;
}
};
function pV(r, e) {
return { dataId: e.dataId, dtype: e.dtype, shape: r.shape };
}
function pue(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e, n = t8.tensorMap.get(o.dataId), s = new lx(o.shape), a = [pV(o, n.complexTensorInfos.real), pV(o, n.complexTensorInfos.imag)];
return t8.runWebGPUProgram(s, a, a[0].dtype);
}
var cV = { kernelName: Di, backendName: "webgpu", kernelFunc: pue };
var mx = class {
constructor(e) {
this.uniforms = "", this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = w.computeOutShape(e, 1), this.variableNames = e.map((t8, o) => `T${o}`), this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1;
for (let t8 = 0; t8 < this.offsetLength; t8++)
this.uniforms += `offset${t8} : i32,`;
this.shaderKey = "concat";
}
getUserCode() {
let e = [];
if (this.offsetLength > 0) {
e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");
for (let s = 1; s < this.offsetLength; s++)
e.push(`else if (yC < uniforms.offset${[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 `
${H("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 Fp(r) {
let { inputs: e, backend: t8 } = r, { input: o } = e, n = t8.tensorMap.get(o.dataId);
return At({ inputs: { x: n.complexTensorInfos.imag }, backend: t8 });
}
var lV = { kernelName: Vi, backendName: "webgpu", kernelFunc: Fp };
function Yc(r, e, t8) {
let o = r[0].dtype;
if (o === "complex64") {
let f = r.map((C) => Ii({ inputs: { input: C }, backend: t8 })), h = r.map((C) => Fp({ inputs: { input: C }, backend: t8 })), g = Yc(f, e, t8), x = Yc(h, e, t8), b = xo({ inputs: { real: g, imag: x }, backend: t8 });
return f.forEach((C) => t8.disposeData(C.dataId)), h.forEach((C) => t8.disposeData(C.dataId)), t8.disposeData(g.dataId), t8.disposeData(x.dataId), b;
}
let n = t8.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: t8, attrs: { shape: E } });
}), h = f.map((k) => ({ vals: t8.readSync(k.dataId), shape: k.shape })), g = w.computeOutShape(f.map((k) => k.shape), 1), x = f[0].shape[0] === 1, b = ez(h, g, o, x), C = w.computeOutShape(r.map((k) => k.shape), e), S = t8.makeTensorInfo(C, o, b);
return f.forEach((k) => t8.disposeData(k.dataId)), S;
}
let s = t8.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(Yc(x, e, t8));
}
let h = Yc(f, e, t8);
for (let g of f)
t8.disposeData(g.dataId);
return h;
}
let { tensors2D: a, outShape: i } = cue(r, e, t8), p = a.map((f) => f.shape), u = new mx(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 = t8.runWebGPUProgram(u, a, a[0].dtype, c);
a.forEach((f) => t8.disposeData(f.dataId));
let d = pe({ inputs: { x: m }, backend: t8, attrs: { shape: i } });
return t8.disposeData(m.dataId), d;
}
function cue(r, e, t8) {
let o = w.computeOutShape(r.map((s) => s.shape), e);
return { tensors2D: r.map((s) => pe({ inputs: { x: s }, backend: t8, attrs: { shape: [y.sizeFromShape(s.shape.slice(0, e)), y.sizeFromShape(s.shape.slice(e))] } })), outShape: o };
}
function Yv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape);
w.assertParamsConsistent(a, s);
let i = w.computeOutShape(e.map((u) => u.shape), s);
if (y.sizeFromShape(i) === 0)
return t8.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: t8 }) : Yc(p, s, t8);
}
var mV = { kernelName: ea, backendName: "webgpu", kernelFunc: Yv };
function lue(r, e, t8, 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;`, C = 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 && t8 ? `
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 ? C : S}
}
fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} {
${r ? S : C}
}
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}
${Qr(n, s)}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
}
}`;
}
var dx = class {
constructor(e, t8, 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 = um(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = pm(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = K(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 = t8 % 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 ? Rp(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner) : Dp(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner, false, null, this.sequentialAccessByThreads), t8 = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1];
return `
${lue(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, t8[0], t8[1], t8[2])}
${e}
`;
}
};
var fx = class {
constructor(e, t8 = false, o = null, n = false) {
this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec2<i32>, pads: vec2<i32>, strides: vec2<i32>, dilations: vec2<i32>,", this.workgroupSize = [4, 4, 8], this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [2], y: [1], z: [0, 3] } : { x: [3], y: [2], z: [0, 1] }, this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t8, this.activation = o, this.hasPreluActivationWeights = n, t8 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.shaderKey = `conv2dnaive_${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;
${Qr(this.addBias, this.activation)}
setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);
}
}
${H("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) {
${this.isChannelsLast ? "let v = readInp(batch, xRow, xCol, xChannel);" : "let v = readInp(batch, xChannel, xRow, xCol);"}
let f = readFilt(row, col, xChannel, outChannel);
acc = acc + v * f;
}
}
}
writeResult(batch, outRow, outCol, outChannel, acc);
}
`;
}
};
var hx = class {
constructor(e, t8) {
this.variableNames = ["x"], this.uniforms = `pads : vec2<i32>, strides : vec2<i32>, dilations : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
inChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = t8, this.shaderKey = `im2col_${this.isChannelsLast}`;
}
getUserCode() {
let e = this.isChannelsLast ? 1 : 2, t8 = 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 `
${H("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[${t8}] && xCol >= 0) {
value = ${s};
}
}
setOutputAtIndex(index, value);
}
}
`;
}
};
function gx(r, e) {
let t8 = r.length;
return t8 >= 3 ? e ? [...r.slice(0, -3), r[t8 - 3] * r[t8 - 2], r[t8 - 1]] : [...r.slice(0, -3), r[t8 - 3], r[t8 - 2] * r[t8 - 1]] : !e && t8 === 1 && r[0] > 1 ? [r[0], 1] : null;
}
function mue({ x: r, filter: e, convInfo: t8, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
let p = t8.dataFormat === "channelsLast", u = !p, c = false, l = p && t8.filterHeight === t8.inHeight && t8.filterWidth === t8.inWidth && t8.padInfo.type === "VALID", m = [], d, f;
if (l) {
let x = t8.inHeight * t8.inWidth * t8.inChannels;
d = pe({ inputs: { x: r }, backend: o, attrs: { shape: [1, t8.batchSize, x] } }), f = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, x, t8.outChannels] } });
} else
d = pe({ inputs: { x: r }, backend: o, attrs: { shape: p ? [t8.batchSize, t8.inHeight * t8.inWidth, t8.inChannels] : [t8.batchSize, t8.inChannels, t8.inHeight * t8.inWidth] } }), f = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, t8.inChannels, t8.outChannels] } });
if (m.push(d), m.push(f), s != null) {
let x = gx(s.shape, p);
x != null && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: x } }), m.push(s));
}
if (n != null) {
let x = gx(n.shape, p);
x != null && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: x } }), m.push(n));
}
let h = Ap({ 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: t8.outShape } });
m.push(h);
for (let x of m)
o.disposeData(x.dataId);
return g;
}
function due({ x: r, filter: e, convInfo: t8, 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 } = t8, C = b === "channelsLast", S = p * u * c, k = h * f, _ = C ? [t8.batchSize, k, S] : [t8.batchSize, S, k], E = new hx(_, C), 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), P = [];
P.push(D);
let O = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, S, -1] } });
if (P.push(O), s != null) {
let U = gx(s.shape, C);
U != null && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: U } }), P.push(s));
}
if (n != null) {
let U = gx(n.shape, C);
U != null && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: U } }), P.push(n));
}
let B = Ap({ a: C ? D : O, b: C ? O : D, transposeA: !C, transposeB: false, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), z = pe({ inputs: { x: B }, backend: o, attrs: { shape: t8.outShape } });
P.push(B);
for (let U of P)
o.disposeData(U.dataId);
return z;
}
function xx({ x: r, filter: e, convInfo: t8, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
let p = n != null, u = s != null, c = t8.dataFormat === "channelsLast", l = c && t8.filterHeight === t8.inHeight && t8.filterWidth === t8.inWidth && t8.padInfo.type === "VALID", m = A().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");
if (!m && (l || t8.filterHeight === 1 && t8.filterWidth === 1 && t8.dilationHeight === 1 && t8.dilationWidth === 1 && t8.strideHeight === 1 && t8.strideWidth === 1 && (t8.padInfo.type === "SAME" || t8.padInfo.type === "VALID")))
return mue({ x: r, filter: e, convInfo: t8, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a });
let d = A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), f = d > 0 ? d : o.thresholdToIncreaseWorkgroups, h = t8.batchSize * Math.ceil(t8.outHeight * t8.outWidth / 32) * Math.ceil(t8.outChannels / 32);
if (A().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") || h <= f)
return due({ x: r, filter: e, convInfo: t8, backend: o, bias: n, preluActivationWeights: s, leakyreluAlpha: a, activation: i });
let g, x = [t8.padInfo.top, t8.padInfo.left], b = [{ type: "int32", data: [t8.filterHeight, t8.filterWidth] }, { type: "int32", data: [...x] }, { type: "int32", data: [t8.strideHeight, t8.strideWidth] }, { type: "int32", data: [t8.dilationHeight, t8.dilationWidth] }];
if (m)
g = new fx(t8, p, i, u);
else {
let _ = c ? t8.outHeight * t8.outWidth : t8.outChannels, E = c ? t8.outChannels : t8.outHeight * t8.outWidth, R = t8.filterHeight * t8.filterWidth * t8.inChannels;
b.push({ type: "int32", data: [_] }, { type: "int32", data: [E] }, { type: "int32", data: [R] });
let D = o.adapterInfo.isIntel();
g = new dx(t8, _, E, R, p, i, u, D);
}
let C = [], S = [r, e];
p && (!c && n.shape.length === 1 && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: [n.shape[0], 1, 1] } }), C.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] } }), C.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 C)
o.disposeData(_.dataId);
return k;
}
function fue(r) {
let { inputs: e, attrs: t8, backend: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = t8, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l);
return xx({ x: n, filter: s, convInfo: m, backend: o });
}
var dV = { kernelName: tn, backendName: "webgpu", kernelFunc: fue };
var yx = 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 = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [4, this.workPerThread, 1])) : (this.size = true, this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize)), this.shaderKey = `conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`;
}
getUserCode() {
let e = this.isChannelsLast ? 1 : 2, t8 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? 3 : 1, n = `
${H()} {
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}
` : `
${H("index")} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d1 = coords[${o}];
let dyCorner = vec2<i32>(coords[${e}], coords[${t8}]) - 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 = ${this.isChannelsLast ? "getDy(batch, idyR, idyC, d2)" : "getDy(batch, d2, idyR, idyC)"};
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
var bx = class {
constructor(e) {
this.variableNames = ["x", "dy"], this.uniforms = "pads : vec2<i32>, strides : vec2<i32>, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerFilter_${this.isChannelsLast}`;
}
getUserCode() {
return `
${H("index")} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let wR = coords[0];
let wC = coords[1];
let d1 = coords[2];
let d2 = coords[3];
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
for (var b = 0; b < uniforms.batchSize; b = b + 1) {
for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {
let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];
if (xR < 0 || xR >= uniforms.inHeight) {
continue;
}
for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {
let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];
if (xC < 0 || xC >= uniforms.inWidth) {
continue;
}
if (${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 Cx = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3DDerFilter";
}
getUserCode() {
return `
${H("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 wx = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3DDerInput";
}
getUserCode() {
return `
${H("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 hue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new bx(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 t8.runWebGPUProgram(d, [n, s], n.dtype, f);
}
var fV = { kernelName: Ai, backendName: "webgpu", kernelFunc: hue };
function gue(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 Sx = 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 = um(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = pm(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = K(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 ? Rp(this.elementsPerThread, this.workgroupSize) : Dp(this.elementsPerThread, this.workgroupSize);
return `
${gue(this.isVec4 ? 4 : 1)}
${e}
`;
}
};
function xue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(u), m = w.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 (A().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE") || m.dataFormat !== "channelsLast")
f = new yx(m);
else {
f = new Sx(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 t8.runWebGPUProgram(f, [n, s], "float32", d);
}
var hV = { kernelName: rn, backendName: "webgpu", kernelFunc: xue };
var Ix = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3dnaive";
}
getUserCode() {
return `
${H("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 yue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = w.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 Ix(u), d = dt(n.dtype, s.dtype);
return t8.runWebGPUProgram(m, [n, s], d, l);
}
var gV = { kernelName: on, backendName: "webgpu", kernelFunc: yue };
function bue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = w.computeConv3DInfo(n.shape, p, a, 1, i), c = new Cx(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 t8.runWebGPUProgram(c, [n, s], s.dtype, l);
}
var xV = { kernelName: Ka, backendName: "webgpu", kernelFunc: bue };
function Cue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, pad: i, inputShape: p } = o, u = w.computeConv3DInfo(p, s.shape, a, 1, i), c = new wx(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 t8.runWebGPUProgram(c, [n, s], n.dtype, l);
}
var yV = { kernelName: nn, backendName: "webgpu", kernelFunc: Cue };
var wue = ye({ opType: Z.COS });
var bV = { kernelName: sn, backendName: "webgpu", kernelFunc: wue };
var Sue = ye({ opType: Z.COSH });
var CV = { kernelName: an, backendName: "webgpu", kernelFunc: Sue };
var vx = class {
constructor(e, t8, o, n) {
this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true;
let [s] = t8;
this.outputShape = [s, o[0], o[1], e], this.dispatchLayout = X(this.outputShape), this.dispatch = K(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, t8] = ["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) * ${e}`], [a, i, p] = this.cropWidthBiggerThan1 ? [`(${t8} / f32(uniforms.outShape[2] - 1))`, "(x2-x1) * width_ratio", `x1*${t8} + f32(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${t8}`];
return `
${H("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let height_ratio = f32(${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 > ${t8} ) {
setOutputAtIndex(index, uniforms.extrapolationValue);
return;
}
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
if(${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 Iue = (r) => {
let { inputs: e, backend: t8, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new vx(n.shape[3], s.shape, i, p), l = [{ type: "float32", data: [u] }];
return t8.runWebGPUProgram(c, [n, s, a], "float32", l);
};
var wV = { kernelName: cn, backendName: "webgpu", kernelFunc: Iue };
var Pp;
(function(r) {
r.Prod = "*", r.Sum = "+";
})(Pp || (Pp = {}));
var fm = class {
constructor(e, t8, o, n) {
this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true, this.workgroupSize = [128, 1, 1], this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.exclusive = o, this.reverse = n, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;
}
getUserCode() {
let e = this.outputShape.length, t8 = this.op === Pp.Prod ? "1.0" : "0.0", o = this.exclusive ? t8 : `getX(${SV(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"), `
${H("index")} {
if (index < uniforms.size) {
var coords = getCoordsFromIndex(index);
let end = ${IV(e, "coords", this.op)};
var val = ${o};
let pow2 = i32(pow(2.0, uniforms.index));
if (${s}) {
let idx = ${a};
${IV(e, "coords", this.op)} = idx;
val ${this.op}= getX(${SV(e, "coords", this.op)});
}
setOutputAtIndex(index, val);
}
}
`;
}
};
function SV(r, e, t8) {
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 ${t8} for rank ${r} is not yet supported`);
}
function IV(r, e, t8) {
if (r === 1)
return `${e}`;
if (r === 2)
return `${e}.y`;
if (r === 3)
return `${e}.z`;
if (r === 4)
return `${e}.w`;
throw Error(`Cumulative ${t8} for rank ${r} is not yet supported`);
}
function kx(r, e, t8, o, n, s) {
let a = e.shape.length, i = w.getAxesPermutation([o], a), p = e;
i != null && (p = yr({ inputs: { x: e }, backend: t8, attrs: { perm: i } }));
let u = w.getInnerMostAxes(1, a)[0];
if (u !== a - 1)
throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`);
let c = p.shape[u], l = At({ inputs: { x: p }, backend: t8 });
for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) {
let d = new fm(r, p.shape, false, s), f = l, h = [{ type: "float32", data: [m] }];
l = t8.runWebGPUProgram(d, [l], l.dtype, h), t8.disposeData(f.dataId);
}
if (n) {
let m = new fm(r, p.shape, n, s), d = l, f = [{ type: "float32", data: [0] }];
l = t8.runWebGPUProgram(m, [l], l.dtype, f), t8.disposeData(d.dataId);
}
if (i != null) {
let m = w.getUndoAxesPermutation(i), d = yr({ inputs: { x: l }, backend: t8, attrs: { perm: m } });
return t8.disposeData(l.dataId), t8.disposeData(p.dataId), d;
}
return l;
}
function vue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
return kx(Pp.Prod, n, t8, s, a, i);
}
var vV = { kernelName: un, backendName: "webgpu", kernelFunc: vue };
function kue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
return kx(Pp.Sum, n, t8, s, a, i);
}
var kV = { kernelName: pn, backendName: "webgpu", kernelFunc: kue };
function Nue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o, p = n.shape.length === 1, c = y.sizeFromShape(s.shape) > 0, l = s.dtype, m = p ? [n.shape[0]] : [n.shape[0], n.shape[1]], d = p ? [a] : [n.shape[0], a], f = Vt({ backend: t8, attrs: { shape: d, value: 0, dtype: l } }), h = new Xc(m, c, i), g = [{ type: "int32", data: [a] }], x = c ? [n, s] : [n];
return t8.runWebGPUProgram(h, x, l, g, f);
}
var NV = { kernelName: ta, backendName: "webgpu", kernelFunc: Nue };
var Nx = class {
constructor(e, t8) {
this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `depthToSpace_${t8}`, this.dataFormat = t8;
}
getUserCode() {
return `
${H("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 = ${this.getInputSamplingString()};
setOutputAtIndex(index, rlt);
}
}`;
}
getHeightCoordString() {
return this.dataFormat === "NHWC" ? "coords[1]" : "coords[2]";
}
getWidthCoordString() {
return this.dataFormat === "NHWC" ? "coords[2]" : "coords[3]";
}
getDepthCoordString() {
return this.dataFormat === "NHWC" ? "coords[3]" : "coords[1]";
}
getOutputDepthSize() {
return this.dataFormat === "NHWC" ? "uniforms.outShape[3]" : "uniforms.outShape[1]";
}
getInputSamplingString() {
return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
}
};
function Tue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = [{ type: "int32", data: [s] }], g = new Nx(f, a);
return t8.runWebGPUProgram(g, [n], n.dtype, h);
}
var TV = { kernelName: ln, backendName: "webgpu", kernelFunc: Tue };
var Tx = class {
constructor(e, t8, o, n = false, s = null, a = false) {
this.variableNames = ["x", "W"], this.uniforms = "pads : vec2<i32>, inDims : vec2<i32>,", this.workgroupSize = [16, 16, 1], this.outputShape = e, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), n && this.variableNames.push("bias"), a && this.variableNames.push("preluActivationWeights"), this.addBias = n, this.activation = s, this.hasPreluActivation = a, this.filterHeight = t8, this.filterWidth = o, this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`;
}
getUserCode() {
let e = this.filterWidth * this.filterHeight, t8 = 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;
}
${H()} {
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 < t8 ? `if (wIndex < ${e})` : `for(; wIndex < ${e}; wIndex = wIndex + ${t8})`}
{
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);
}
}
${Qr(this.addBias, this.activation)}
if (coordsInBounds4D(coords, uniforms.outShape)) {
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
}
}
`;
}
};
var Qc = class {
constructor(e, t8 = false, o = null, n = false) {
this.variableNames = ["x", "W"], this.uniforms = "pads : vec2<i32>, inDims : vec2<i32>, virtualWidth : i32,", this.workgroupSize = [64, 1, 1], this.workPerThread = 4, this.outputComponent = 4, this.outputShape = e.outShape, this.virtualWidth = Math.ceil(this.outputShape[2] / this.workPerThread) * this.workPerThread;
let s = [this.outputShape[0], this.outputShape[1], this.virtualWidth, this.outputShape[3]];
this.dispatchLayout = X(s), this.dispatch = K(this.dispatchLayout, s, this.workgroupSize, [this.outputComponent * this.workPerThread, 1, 1]), y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t8 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t8, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwiseVec4_${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, t8 = 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;
}
${H("index")} {
let width0 = uniforms.outShape[3] / ${this.outputComponent};
let d1 = (index % width0) * ${this.outputComponent};
var index1 = index / width0;
let width1 = uniforms.virtualWidth / ${this.workPerThread};
let c = (index1 % width1) * ${this.workPerThread};
index1 = index1 / width1;
let r = index1 % uniforms.outShape[1];
let batch = index1 / uniforms.outShape[1];
let xRCCorner = vec2<i32>(r, c) * vec2<i32>(${t8}, ${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];
${Qr(this.addBias, this.activation)}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
}
}
}
`;
}
};
var Zc = class {
constructor(e, t8 = false, o = null, n = false) {
this.variableNames = ["x", "W"], this.uniforms = `pads : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,
filterWidth : i32, strides : vec2<i32>, dilations : vec2<i32>,`, this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", t8 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t8, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwise_${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)}
${H("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.${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;
}
}
}
${Qr(this.addBias, this.activation)}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
}
}
`;
}
};
function _ue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(p), m = u;
m == null && (m = [1, 1]);
let d = w.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 Tx(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 Qc(d), f.push({ type: "int32", data: [g.virtualWidth] })) : (g = new Zc(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] })), t8.runWebGPUProgram(g, [n, s], n.dtype, f);
}
var _V = { kernelName: mn, backendName: "webgpu", kernelFunc: _ue };
var _x = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "depthwise_conv2d_backprop_filter";
}
getUserCode() {
return `
${H("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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "depthwise_conv2d_backprop_input";
}
getUserCode() {
return `
${H("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 $ue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = w.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new _x(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 t8.runWebGPUProgram(m, [n, s], "float32", d);
}
var $V = { kernelName: Fi, backendName: "webgpu", kernelFunc: $ue };
function Eue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = w.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 t8.runWebGPUProgram(m, [n, s], n.dtype, d);
}
var EV = { kernelName: Pi, backendName: "webgpu", kernelFunc: Eue };
var Ex = class {
constructor(e) {
this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, e], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "diag";
}
getUserCode() {
return `
${H("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);
setOutputAtIndex(index, value);
}
}
`;
}
};
function Rue(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = pe({ inputs: { x: o }, backend: t8, attrs: { shape: [s] } }), i = new Ex(s), p = t8.runWebGPUProgram(i, [a], a.dtype), u = pe({ inputs: { x: p }, backend: t8, attrs: { shape: n } });
return t8.disposeData(a.dataId), t8.disposeData(p.dataId), u;
}
var RV = { kernelName: ra, backendName: "webgpu", kernelFunc: Rue };
var Rx = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "dilation2d";
}
getUserCode() {
return `
${H("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 Due(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = w.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 Rx(u);
return t8.runWebGPUProgram(m, [n, s], n.dtype, l);
}
var DV = { kernelName: dn, backendName: "webgpu", kernelFunc: Due };
var Dx = class {
constructor(e, t8) {
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 = X(e.outShape), this.dispatch = K(this.dispatchLayout, e.outShape, this.workgroupSize), t8 !== "float32" && t8 !== "int32")
throw new Error(`Dilation2DBackpropInput only supports float32 and int32
types, does not support ${t8} type.`);
this.type = t8, this.shaderKey = "dilation2DBackpropInput";
}
getUserCode() {
return `
${H("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);
${Us("&result[flatIndexIn]", "value", this.type)}
}
}
`;
}
};
var Ax = class {
constructor(e, t8, 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 = X(e.outShape), this.dispatch = K(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 `
${H("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);
${Us("&result[flatIndexIn]", "value", this.type)}
}
}
`;
}
};
function Aue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o, c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = s.dtype, m = new Ax(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: t8, attrs: { shape: s.shape, value: 0, dtype: l } });
return t8.runWebGPUProgram(m, [n, s, a], l, d, f);
}
var AV = { kernelName: Mi, backendName: "webgpu", kernelFunc: Aue };
function Fue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o, c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = n.dtype, m = new Dx(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: t8, attrs: { shape: c.inShape, value: 0, dtype: l } });
return t8.runWebGPUProgram(m, [n, s, a], l, d, f);
}
var FV = { kernelName: Oi, backendName: "webgpu", kernelFunc: Fue };
var Qv = et({ opType: fe.MUL, cpuKernelImpl: gz, supportsComplex: true });
var PV = { kernelName: Xn, backendName: "webgpu", kernelFunc: Qv };
function Zv(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
return Jr(n, s, a, "sum", t8);
}
var OV = { kernelName: Ss, backendName: "webgpu", kernelFunc: Zv };
function Pue(r) {
let { inputs: e, backend: t8, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length);
w.checkEinsumDimSizes(a.length, p, s);
let { path: u, steps: c } = w.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 } = w.getEinsumPermutation(d, p[g]), C;
w.isIdentityPermutation(x) ? C = s[g] : (C = yr({ inputs: { x: s[g] }, backend: t8, attrs: { perm: x } }), f.push(C));
let S = C.shape.slice();
for (let k = 0; k < b.length; ++k)
S.splice(b[k], 0, 1);
y.arraysEqual(C.shape, S) || (C = pe({ inputs: { x: C }, backend: t8, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = Qv({ inputs: { a: C, b: m }, backend: t8 }), f.push(m));
}
h < l - 1 && (u[h] >= 0 && (m = Zv({ inputs: { x: m }, backend: t8, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--);
}
for (let h of f)
h !== m && t8.disposeData(h.dataId);
return m;
}
var MV = { kernelName: Li, backendName: "webgpu", kernelFunc: Pue };
var Oue = ye({ opType: Z.ELU });
var LV = { kernelName: hn, backendName: "webgpu", kernelFunc: Oue };
var Mue = (r) => {
let { inputs: e, backend: t8 } = r, { dy: o, y: n } = e, s = new Si(fe.ELU_DER, o.shape, n.shape);
return t8.runWebGPUProgram(s, [o, n], o.dtype);
};
var BV = { kernelName: qa, backendName: "webgpu", kernelFunc: Mue };
var Lue = et({ opType: fe.EQUAL, dtype: "bool", cpuKernelImpl: tz });
var zV = { kernelName: xn, backendName: "webgpu", kernelFunc: Lue };
var Bue = ye({ opType: Z.ERF });
var VV = { kernelName: gn, backendName: "webgpu", kernelFunc: Bue };
var zue = ye({ opType: Z.EXP, cpuKernelImpl: rz, dtype: "float32" });
var WV = { kernelName: yn, backendName: "webgpu", kernelFunc: zue };
function Fx(r) {
let { inputs: e, attrs: t8, backend: o } = r, { dim: n } = t8, { 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)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), pe({ inputs: { x: s }, backend: o, attrs: { shape: i } });
}
var UV = { kernelName: oa, backendName: "webgpu", kernelFunc: Fx };
var Vue = ye({ opType: Z.EXPM1, cpuKernelImpl: oz });
var GV = { kernelName: bn, backendName: "webgpu", kernelFunc: Vue };
var hm = class {
constructor(e, t8) {
this.variableNames = ["real", "imag"], this.outputShape = [], this.uniforms = "exponentMultiplier : f32, denominator: f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.component = e, this.shaderKey = `fft_${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;
}
${H("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));
}
}
`;
}
};
function Px(r, e, t8) {
let o = t8.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: t8, attrs: { shape: [a, s] } });
i.push(p);
let u = p.shape, c = new hm("real", u), l = new hm("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 = t8.runWebGPUProgram(c, m, "float32", h);
i.push(g);
let x = t8.runWebGPUProgram(l, m, "float32", h);
i.push(x);
let b = xo({ inputs: { real: g, imag: x }, backend: t8 });
i.push(b);
let C = pe({ inputs: { x: b }, backend: t8, attrs: { shape: r.shape } });
return i.forEach((S) => t8.disposeData(S.dataId)), C;
}
function Wue(r) {
let { inputs: e, backend: t8 } = r, { input: o } = e;
return Px(o, false, t8);
}
var HV = { kernelName: Bi, backendName: "webgpu", kernelFunc: Wue };
var Ox = class {
constructor(e) {
this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "flipLeftRight";
}
getUserCode() {
return `
${H("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let coordX = uniforms.xShape[2] - coords[2] - 1;
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
setOutputAtIndex(index, outputValue);
}
}
`;
}
};
var KV = { kernelName: Cn, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => {
let { image: t8 } = r, o = e, n = new Ox(t8.shape);
return o.runWebGPUProgram(n, [t8], t8.dtype);
} };
var Uue = ye({ opType: Z.FLOOR, cpuKernelImpl: nz });
var qV = { kernelName: wn, backendName: "webgpu", kernelFunc: Uue };
var Gue = et({ opType: fe.INT_DIV, cpuKernelImpl: sz, dtype: "int32" });
var jV = { kernelName: Sn, backendName: "webgpu", kernelFunc: Gue };
var Mx = class {
constructor(e, t8, o = false) {
this.isFromPixels = true, this.outputShape = [0], this.variableNames = [], this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [t8, 1, 1]), this.importVideo = o, this.shaderKey = `fromPixels_${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>"};
${H("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 XV = { kernelName: Au, backendName: "webgpu", kernelFunc: Hue };
var Jc;
var Jv = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
function Hue(r) {
let { inputs: e, backend: t8, 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 = t8.device.importExternalTexture({ source: n });
else {
if (f) {
let L = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
(Jc == null || L !== Jv) && (Jv = L, Jc = document.createElement("canvas").getContext("2d", { willReadFrequently: Jv })), Jc.canvas.width = c, Jc.canvas.height = l, Jc.drawImage(n, 0, 0, c, l), n = Jc.canvas;
}
let P = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, O = "rgba8unorm", M = t8.textureManager.acquireTexture(m[1], m[0], O, P);
t8.queue.copyExternalImageToTexture({ source: n }, { texture: M }, [m[1], m[0]]), b = M;
}
let C = y.sizeFromShape(m), S = y.computeStrides(m), k = new Mx(m, s, d), _ = [{ type: "uint32", data: [C] }, { type: "uint32", data: [s] }, { type: "uint32", data: [...S] }], E = t8.makeTensorInfo([l, c], "int32"), R = t8.tensorMap.get(E.dataId);
R.resource = b;
let D = t8.runWebGPUProgram(k, [E], "int32", _);
return t8.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, C = 0;
for (let S = 0; S < b; S++)
S % 4 < s && (g[C++] = h[S]);
}
let x = t8.makeTensorInfo(m, "int32", new Int32Array(g));
return t8.uploadToGPU(x.dataId), x;
}
var Lx = class {
constructor(e, t8, o, n, s) {
this.uniforms = "varianceEpsilon : f32,", this.workgroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], w.assertAndGetBroadcastShape(e, t8), w.assertAndGetBroadcastShape(e, o), this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), n != null && (w.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset")), s != null && (w.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 t8 = "1.0";
return this.scaleShape != null && (t8 = "getScaleByOutputIndex(index)"), `
${H("index")} {
if (index < uniforms.size)
{
let xValue = getXByOutputIndex(index);
let meanValue = getMeanByOutputIndex(index);
let varianValue = getVarianceByOutputIndex(index);
let offsetValue = ${e};
let scaleValue = ${t8};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
}
`;
}
};
var YV = { kernelName: In, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => {
let { x: o, scale: n, offset: s, mean: a, variance: i } = r, { varianceEpsilon: p } = e, u = t8, 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 Lx(o.shape, a.shape, i.shape, l, m), f = [{ type: "float32", data: [p] }];
return u.runWebGPUProgram(d, c, o.dtype, f);
} };
function Kue(r) {
let { inputs: e, backend: t8, 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 = w.convertConv2DDataFormat(c), g = w.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h);
return xx({ x: n, filter: s, convInfo: g, backend: t8, bias: a, preluActivationWeights: i, leakyreluAlpha: f, activation: d });
}
var QV = { kernelName: Io, backendName: "webgpu", kernelFunc: Kue };
function que(r) {
let { inputs: e, backend: t8, 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(w.eitherStridesOrDilationsAreOne(p, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);
let h = w.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 C = [{ 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 Qc(h, x, m, b), C.push({ type: "int32", data: [S.virtualWidth] })) : (S = new Zc(h, x, m, b), C.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" && (C.push({ type: "float32", data: [d] }), S.uniforms += " alpha : f32,"), t8.runWebGPUProgram(S, g, "float32", C);
}
var ZV = { kernelName: vo, backendName: "webgpu", kernelFunc: que };
var Bx = class {
constructor(e, t8) {
this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${ht(e)},`;
}
getUserCode() {
let e;
return this.sliceDim > 1 ? e = "uniforms.strides[j]" : e = "uniforms.strides", `
${H("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 jue(r) {
let { inputs: e, backend: t8 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = w.prepareAndValidate(o, n), m = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [u, a] } }), d = pe({ inputs: { x: o }, backend: t8, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } });
if (t8.shouldExecuteOnCPU([o, n]) || o.dtype === "string") {
let b = t8.readSync(n.dataId), C = t8.bufferSync(o), S = az(b, C, o.dtype, u, a, c, l, o.shape, i);
return t8.makeTensorInfo(p, o.dtype, S.values);
}
let f = new Bx(a, [u, c]), h = [{ type: "int32", data: [a] }, { type: "int32", data: l }], g = t8.runWebGPUProgram(f, [d, m], d.dtype, h), x = pe({ inputs: { x: g }, backend: t8, attrs: { shape: p } });
return t8.disposeData(m.dataId), t8.disposeData(d.dataId), t8.disposeData(g.dataId), x;
}
var JV = { kernelName: vn, backendName: "webgpu", kernelFunc: jue };
var zx = class {
constructor(e, t8) {
this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "gather";
}
getUserCode() {
let e = Xue(this.aShape);
return `
${H("index")} {
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let indexZ = i32(getIndices(resRC.x, resRC.z));
let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
setOutputAtIndex(index, inBounds * getA(${e}));
}
}
`;
}
};
function Xue(r) {
let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], t8 = [];
for (let o = 0; o < r.length; o++)
o === 2 ? t8.push("indexZ") : t8.push(`${e[o]}`);
return t8.join();
}
function e0(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = w.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = pe({ inputs: { x: s }, backend: t8, 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 (t8.shouldExecuteOnCPU([n, s])) {
let C = t8.tensorMap.get(d.dataId).values, S = me(d.shape, d.dtype, C), _ = t8.tensorMap.get(m.dataId).values, E = me(m.shape, m.dtype, _), R = iz(E, S, f);
return l.forEach((D) => t8.disposeData(D.dataId)), t8.makeTensorInfo(u.outputShape, R.dtype, R.values);
}
let h = new zx(m.shape, f), g = t8.runWebGPUProgram(h, [m, d], m.dtype);
l.push(g);
let x = pe({ inputs: { x: g }, backend: t8, attrs: { shape: u.outputShape } });
return l.forEach((b) => t8.disposeData(b.dataId)), x;
}
var eW = { kernelName: sa, backendName: "webgpu", kernelFunc: e0 };
var Yue = et({ opType: fe.GREATER, cpuKernelImpl: pz, dtype: "bool" });
var tW = { kernelName: kn, backendName: "webgpu", kernelFunc: Yue };
var Que = et({ opType: fe.GREATER_EQUAL, dtype: "bool", cpuKernelImpl: uz });
var rW = { kernelName: Nn, backendName: "webgpu", kernelFunc: Que };
function Zue(r) {
let { inputs: e, backend: t8 } = r, { input: o } = e;
return Px(o, true, t8);
}
var oW = { kernelName: zi, backendName: "webgpu", kernelFunc: Zue };
var Jue = ye({ opType: Z.IS_FINITE, dtype: "bool" });
var nW = { kernelName: Tn, backendName: "webgpu", kernelFunc: Jue };
var epe = ye({ opType: Z.IS_INF, dtype: "bool" });
var sW = { kernelName: _n, backendName: "webgpu", kernelFunc: epe };
var tpe = ye({ opType: Z.IS_NAN, dtype: "bool" });
var aW = { kernelName: $n, backendName: "webgpu", kernelFunc: tpe };
function rpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = [{ type: "float32", data: [s] }], i = new Zr(n.shape, Z.LEAKYRELU, "alpha : f32,");
return t8.runWebGPUProgram(i, [n], "float32", a);
}
var iW = { kernelName: En, backendName: "webgpu", kernelFunc: rpe };
var ope = et({ opType: fe.LESS, dtype: "bool", cpuKernelImpl: lz });
var uW = { kernelName: Rn, backendName: "webgpu", kernelFunc: ope };
var npe = et({ opType: fe.LESS_EQUAL, dtype: "bool", cpuKernelImpl: cz });
var pW = { kernelName: Dn, backendName: "webgpu", kernelFunc: npe };
var Vx = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "linSpace";
}
getUserCode() {
return `
${H("index")} {
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);
}
}
`;
}
};
function spe(r) {
let { backend: e, attrs: t8 } = r, { start: o, stop: n, num: s } = t8, a = (n - o) / (s - 1), i = new Vx(s), p = [{ type: "float32", data: [o] }, { type: "float32", data: [a] }];
return e.runWebGPUProgram(i, [], "float32", p);
}
var cW = { kernelName: An, backendName: "webgpu", kernelFunc: spe };
var ape = ye({ opType: Z.LOG, cpuKernelImpl: mz });
var lW = { kernelName: Fn, backendName: "webgpu", kernelFunc: ape };
var ipe = ye({ opType: Z.LOG1P });
var mW = { kernelName: Pn, backendName: "webgpu", kernelFunc: ipe };
var upe = et({ opType: fe.LOGICAL_AND, dtype: "bool" });
var dW = { kernelName: On, backendName: "webgpu", kernelFunc: upe };
var ppe = ye({ opType: Z.LOGICAL_NOT });
var fW = { kernelName: Mn, backendName: "webgpu", kernelFunc: ppe };
var cpe = et({ opType: fe.LOGICAL_OR });
var hW = { kernelName: Ln, backendName: "webgpu", kernelFunc: cpe };
var gW = `
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 Wx = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn";
}
getUserCode() {
return `
${H("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;
}
}
${gW}
setOutputAtIndex(index, x * powValue);
}
}
`;
}
};
var Ux = class {
constructor(e, t8) {
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(t8 <= this.maxAllowRadius, () => `Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t8}`), this.outputShape = e, this.elementsPerWorkgroup = this.workgroupSize[0] - 2 * this.maxAllowRadius, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = K(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};
${H()} {
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;
}
${gW}
setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);
}
} `;
}
};
function lpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u;
s > 16 ? u = new Wx(n.shape) : u = new Ux(n.shape, s);
let c = [{ type: "int32", data: [s] }, { type: "float32", data: [a] }, { type: "float32", data: [i] }, { type: "float32", data: [p] }];
return t8.runWebGPUProgram(u, [n], n.dtype, c);
}
var xW = { kernelName: Bn, backendName: "webgpu", kernelFunc: lpe };
var Gx = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn_grad";
}
getUserCode() {
return `
${H("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 mpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new Gx(n.shape), m = [{ type: "int32", data: [i] }, { type: "float32", data: [p] }, { type: "float32", data: [u] }, { type: "float32", data: [c] }];
return t8.runWebGPUProgram(l, [n, s, a], n.dtype, m);
}
var yW = { kernelName: ja, backendName: "webgpu", kernelFunc: mpe };
var dpe = et({ opType: fe.MAX, cpuKernelImpl: fz });
var bW = { kernelName: Vn, backendName: "webgpu", kernelFunc: dpe };
function fpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = w.computePool2DInfo(n.shape, s, a, u, i, p);
return nx(n, c, "max", t8);
}
var CW = { kernelName: Wn, backendName: "webgpu", kernelFunc: fpe };
function hpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new ku(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 t8.runWebGPUProgram(m, [n], n.dtype, d);
}
var wW = { kernelName: aa, backendName: "webgpu", kernelFunc: hpe };
var Hx = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "maxPool2DBackprop";
}
getUserCode() {
return `
${H("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 Kx = 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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "maxPool3DBackprop";
}
getUserCode() {
return `
${H("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 gpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new ku(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 = t8.runWebGPUProgram(d, [a], "int32", f), g = new Kx(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 = t8.runWebGPUProgram(g, [n, h], a.dtype, f);
return t8.disposeData(h.dataId), x;
}
var SW = { kernelName: Ui, backendName: "webgpu", kernelFunc: gpe };
function xpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s;
lm([s, a], "maxPoolGrad");
let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = new Ma(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 = t8.runWebGPUProgram(d, [i], "int32", f), g = new Hx(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 = t8.runWebGPUProgram(g, [n, h], i.dtype, f);
return t8.disposeData(h.dataId), x;
}
var IW = { kernelName: Wi, backendName: "webgpu", kernelFunc: xpe };
function ype(r) {
let { inputs: e, backend: t8, 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(w.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);
let c = w.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 Ma(c, "max", false), d = t8.runWebGPUProgram(m, [p], p.dtype, l);
m = new Ma(c, "max", true, true, i);
let f = t8.runWebGPUProgram(m, [p], "int32", l);
return [d, f];
}
var vW = { kernelName: ia, backendName: "webgpu", kernelFunc: ype };
function bpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
return Jr(n, s, a, "min", t8);
}
var kW = { kernelName: Gn, backendName: "webgpu", kernelFunc: bpe };
var Cpe = et({ opType: fe.MIN, cpuKernelImpl: hz });
var NW = { kernelName: Hn, backendName: "webgpu", kernelFunc: Cpe };
var qx = class {
constructor(e, t8, o) {
this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = e, t8.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, t8 = 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 = ht(e), p = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
return `
${H("index")} {
if (index < uniforms.size) {
let start = ${i}(${t8});
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 TW = { kernelName: Kn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => {
let { x: o } = r, { paddings: n, mode: s } = e, a = t8, i = n.map((c) => ({ type: "int32", data: [c[0], c[1]] })), p = new qx(o.shape, n, s);
return a.runWebGPUProgram(p, [o], o.dtype, i);
} };
var wpe = et({ opType: fe.MOD });
var _W = { kernelName: qn, backendName: "webgpu", kernelFunc: wpe };
var jx = class {
constructor(e, t8) {
this.variableNames = ["probs"], this.outputShape = [], this.uniforms = "seed : f32, numOutcomes: i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t8], this.dispatchLayout = X(this.outputShape), this.dispatch = K(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);
}
${H("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 Xx = class {
constructor(e) {
this.variableNames = ["logits"], this.outputShape = e, this.dispatchLayout = X(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]};
${H("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 t0(r) {
let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [y.sizeFromShape(n.shape) / n.shape[s], n.shape[s]] } }), i = new Xx(a.shape), p = t8.runWebGPUProgram(i, [a], n.dtype), u = pe({ inputs: { x: p }, backend: t8, attrs: { shape: n.shape } });
return t8.disposeData(a.dataId), t8.disposeData(p.dataId), u;
}
var $W = { kernelName: Is, backendName: "webgpu", kernelFunc: t0 };
function Spe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : t0({ inputs: { logits: n }, backend: t8, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new jx(u, s), m = [{ type: "float32", data: [a] }, { type: "int32", data: [c] }], d = t8.runWebGPUProgram(l, [p], "int32", m);
return i || t8.disposeData(p.dataId), d;
}
var EW = { kernelName: jn, backendName: "webgpu", kernelFunc: Spe };
function Ipe(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
if (t8.shouldExecuteOnCPU([o])) {
let s = t8.tensorMap.get(o.dataId), [a, i] = xz(s.values, o.shape, o.dtype);
return t8.makeTensorInfo(i, o.dtype, a);
}
let n = new Zr(o.shape, Z.NEG);
return t8.runWebGPUProgram(n, [o], o.dtype);
}
var RW = { kernelName: ua, backendName: "webgpu", kernelFunc: Ipe };
function vpe(r) {
console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t8.readSync(n.dataId), c = t8.readSync(s.dataId), { selectedIndices: l } = Wt.nonMaxSuppressionV3Impl(u, c, a, i, p);
return t8.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var DW = { kernelName: Qn, backendName: "webgpu", kernelFunc: vpe };
function kpe(r) {
console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
let { inputs: e, backend: t8, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t8.readSync(n.dataId), l = t8.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Wt.nonMaxSuppressionV5Impl(c, l, m, d, f, h);
return [t8.makeTensorInfo([g.length], "int32", new Int32Array(g)), t8.makeTensorInfo([x.length], "float32", new Float32Array(x))];
}
var AW = { kernelName: Zn, backendName: "webgpu", kernelFunc: kpe };
var Yx = class {
constructor(e, t8) {
this.variableNames = ["x"], this.uniforms = "onValue : f32, offValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t8], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "onehot";
}
getUserCode() {
return `
${H("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 Npe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new Yx(u, a), l = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [u] } }), m = [{ type: "float32", data: [i] }, { type: "float32", data: [p] }], d = t8.runWebGPUProgram(c, [l], s, m);
t8.disposeData(l.dataId);
let f = [...n.shape, a], h = pe({ inputs: { x: d }, backend: t8, attrs: { shape: f } });
return t8.disposeData(d.dataId), h;
}
var FW = { kernelName: Jn, backendName: "webgpu", kernelFunc: Npe };
function gm(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
if (o.dtype === "complex64") {
let n = Ii({ inputs: { input: o }, backend: t8 }), s = gm({ inputs: { x: n }, backend: t8 }), a = Fp({ inputs: { input: o }, backend: t8 }), i = gm({ inputs: { x: a }, backend: t8 }), p = xo({ inputs: { real: s, imag: i }, backend: t8 });
return t8.disposeData(n.dataId), t8.disposeData(s.dataId), t8.disposeData(a.dataId), t8.disposeData(i.dataId), p;
} else
return Vt({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t8 });
}
var PW = { kernelName: ba, backendName: "webgpu", kernelFunc: gm };
function OW(r) {
let { inputs: e, backend: t8 } = r, { x: o } = e;
if (o.dtype === "string")
throw new Error("onesLike is not supported under string dtype");
if (o.dtype === "complex64") {
let n = Ii({ inputs: { input: o }, backend: t8 }), s = OW({ inputs: { x: n }, backend: t8 }), a = Fp({ inputs: { input: o }, backend: t8 }), i = gm({ inputs: { x: a }, backend: t8 }), p = xo({ inputs: { real: s, imag: i }, backend: t8 });
return t8.disposeData(n.dataId), t8.disposeData(s.dataId), t8.disposeData(a.dataId), t8.disposeData(i.dataId), p;
} else
return Vt({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t8 });
}
var MW = { kernelName: pa, backendName: "webgpu", kernelFunc: OW };
function Tpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { axis: n } = o;
if (e.length === 1)
return Fx({ inputs: { input: e[0] }, backend: t8, 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 = Fx({ inputs: { input: c }, backend: t8, attrs: { dim: n } });
return i.push(l), l;
}), u = Yv({ inputs: p, backend: t8, attrs: { axis: n } });
return i.forEach((c) => t8.disposeData(c.dataId)), u;
}
var LW = { kernelName: ca, backendName: "webgpu", kernelFunc: Tpe };
function r0(r, e = false) {
let t8 = r.length, o = ht(t8), n = r.map((l, m) => `uniforms.pad${m}[0]`).join(","), s = r.map((l, m) => `uniforms.pad${m}[0] + uniforms.xShape${t8 > 1 ? `[${m}]` : ""}`).join(","), a = t8 > 1 ? `${o}(${n})` : `${n}`, i = t8 > 1 ? `${o}(${s})` : `${s}`, p = t8 > 1 ? "any(paddedCoords < start)" : "paddedCoords < start", u = t8 > 1 ? "any(paddedCoords >= end)" : "paddedCoords >= end", c = t8 > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, t8) : "coords";
return `
let start = ${a};
let end = ${i};
if (${p} || ${u}) {
setOutputAtIndex(index, ${e ? 0 : "uniforms.constantValue"});
} else {
let coords = paddedCoords - start;
setOutputAtIndex(index, getX(${c}));
}
`;
}
var Qx = class {
constructor(e, t8) {
this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.map((o, n) => o[0] + e[n] + o[1]), this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), t8.map((o, n) => {
this.uniforms += ` pad${n} : vec2<i32>,`;
}), this.xShape = e, this.shaderKey = "pad";
}
getUserCode() {
return `
${H("index")} {
if (index < uniforms.size) {
let paddedCoords = getCoordsFromIndex(index);
${r0(this.xShape)}
}
}
`;
}
};
var _pe = (r) => {
let { inputs: e, backend: t8, 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: t8 });
if (y.sizeFromShape(n.shape) === 0) {
let u = s.map((c, l) => c[0] + n.shape[l] + c[1]);
return Vt({ backend: t8, 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 Qx(n.shape, s);
return t8.runWebGPUProgram(p, [n], n.dtype, i);
};
var BW = { kernelName: es, backendName: "webgpu", kernelFunc: _pe };
var $pe = et({ opType: fe.POW });
var zW = { kernelName: ts, backendName: "webgpu", kernelFunc: $pe };
function Epe(r) {
let { inputs: e, backend: t8 } = r, { x: o, alpha: n } = e, s = new Si(fe.PRELU, o.shape, n.shape);
return t8.runWebGPUProgram(s, [o, n], "float32");
}
var VW = { kernelName: rs, backendName: "webgpu", kernelFunc: Epe };
function Rpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
return Jr(n, s, a, "prod", t8);
}
var WW = { kernelName: os, backendName: "webgpu", kernelFunc: Rpe };
var Dpe = (r) => {
let { backend: e, attrs: t8 } = r, { start: o, stop: n, step: s, dtype: a } = t8, i = Cz(o, n, s, a);
return e.makeTensorInfo([i.length], a, i);
};
var UW = { kernelName: la, backendName: "webgpu", kernelFunc: Dpe };
var Ape = et({ opType: fe.DIV });
var GW = { kernelName: fn, backendName: "webgpu", kernelFunc: Ape };
var Fpe = ye({ opType: Z.RECIPROCAL });
var HW = { kernelName: ns, backendName: "webgpu", kernelFunc: Fpe };
var Ppe = ye({ opType: Z.RELU });
var KW = { kernelName: ss, backendName: "webgpu", kernelFunc: Ppe };
var Ope = ye({ opType: Z.RELU6 });
var qW = { kernelName: us, backendName: "webgpu", kernelFunc: Ope };
var Zx = class {
constructor(e, t8, o) {
this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t8, o, e[3]], this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "resizeBilinear";
}
getUserCode() {
return `
${H("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 Mpe(r) {
let { inputs: e, backend: t8, 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 Zx(n.shape, p, u);
return t8.runWebGPUProgram(f, [n], "float32", d);
}
var jW = { kernelName: is, backendName: "webgpu", kernelFunc: Mpe };
var Jx = class {
constructor(e, t8) {
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 = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.alignCorners = t8, this.shaderKey = `resizeBilinearBackprop_${t8}`;
}
getUserCode() {
return `
${H("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 Lpe(r) {
let { inputs: e, backend: t8, 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, C = new Jx(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 t8.runWebGPUProgram(C, [s], s.dtype, S);
}
var XW = { kernelName: Qa, backendName: "webgpu", kernelFunc: Lpe };
var ey = class {
constructor(e, t8, o, n) {
this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, roundBase : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t8, o, e[3]], this.dispatchLayout = X(this.outputShape), this.dispatch = K(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", `
${H("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 = ${e};
// Compute the coordinators of nearest neighbor point.
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
let sourceNearestRC = vec2<i32>(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutputAtIndex(index, newValue);
}
}
`;
}
};
function Bpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [s ? 0.5 : 0] }], f = new ey(n.shape, p, u, a);
return t8.runWebGPUProgram(f, [n], n.dtype, d);
}
var YW = { kernelName: as, backendName: "webgpu", kernelFunc: Bpe };
var ty = class {
constructor(e, t8) {
this.variableNames = ["dy"], this.uniforms = `effectiveXSize : vec2<i32>, effectiveYSize : vec2<i32>, invHeightScale : f32, invWidthScale : f32,
winHeight : i32, winWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.alignCorners = t8, this.shaderKey = `resizeNearestNeigborBackprop_${t8}`;
}
getUserCode() {
return `
${H("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 zpe(r) {
let { inputs: e, backend: t8, 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, C = new ty(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 t8.runWebGPUProgram(C, [s], s.dtype, S);
}
var QW = { kernelName: Ya, backendName: "webgpu", kernelFunc: zpe };
var ry = class {
constructor(e) {
this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(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;
}
${H("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 Vpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length;
if (a === 0)
return At({ inputs: { x: n }, backend: t8 });
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: t8, attrs: { shape: p } }), d = new ry(p), f = t8.runWebGPUProgram(d, [m], m.dtype, l);
t8.disposeData(m.dataId);
let h = pe({ inputs: { x: f }, backend: t8, attrs: { shape: i } });
return t8.disposeData(f.dataId), h;
}
var ZW = { kernelName: ps, backendName: "webgpu", kernelFunc: Vpe };
var oy = class {
constructor(e, t8) {
this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t8 == "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 `
${H("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));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputAtIndex(index, outputValue);
}
}
`;
}
};
var JW = { kernelName: Ds, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t8 }) => {
let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t8, p = new oy(o.shape, s), [u, c] = w.getImageCenter(a, o.shape[1], o.shape[2]), l = [{ type: "float32", data: [u] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(n)] }, { type: "float32", data: [Math.cos(n)] }];
return typeof s == "number" ? l.push({ type: "float32", data: [Number.parseFloat(s.toFixed(2))] }) : l.push({ type: "float32", data: s }), i.runWebGPUProgram(p, [o], o.dtype, l);
} };
var Wpe = ye({ opType: Z.ROUND });
var eU = { kernelName: cs, backendName: "webgpu", kernelFunc: Wpe };
var Upe = ye({ opType: Z.RSQRT, cpuKernelImpl: wz });
var tU = { kernelName: ls, backendName: "webgpu", kernelFunc: Upe };
var La = class {
constructor(e, t8, o, n, s, a, i, p = true) {
this.variableNames = ["updates", "indices"], this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.sumDupeIndices = p, this.dispatchLayout = X(e), this.dispatch = K(this.dispatchLayout, e, this.workgroupSize), this.sliceDimGreaterThanOne = t8 > 1, this.shaderKey = `scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;
let u = ht(s.length);
this.uniforms = `sliceDim : i32, strides: ${u}, updatesSize: i32,`, this.updatesRank = n, this.indicesRank = o;
}
getUserCode() {
let e = "";
this.indicesRank === 1 ? e = "coords[0]" : this.indicesRank === 2 && (e = "coords[0], j");
let t8 = `getIndices(${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}
${H("index")} {
if (index < uniforms.updatesSize) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t8}));
flattenedIndex = flattenedIndex + indexInside * ${o};
}
let updateValue =
${$p(this.type)}(${i});
let flatIndex = getOutputIndexFromCoords(${n});
${this.sumDupeIndices ? Us("&result[flatIndex]", "updateValue", this.type) : "atomicStore(&result[flatIndex], bitcast<i32>(updateValue));"}
}
}`;
}
};
function Gpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = [l / u, u];
if (l === 0)
return t8.makeTensorInfo(a, n.dtype);
let d = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [p, i] } }), f = pe({ inputs: { x: s }, backend: t8, attrs: { shape: [p, u] } }), h = f.dtype, g = Vt({ backend: t8, attrs: { shape: m, value: 0, dtype: h } }), x = y.sizeFromShape(f.shape), b = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [x] }], C = new La(f.shape, i, d.shape.length, f.shape.length, c, m, h), S = t8.runWebGPUProgram(C, [f, d], h, b, g), k = pe({ inputs: { x: S }, backend: t8, attrs: { shape: a } });
return t8.disposeData(d.dataId), t8.disposeData(f.dataId), t8.disposeData(S.dataId), k;
}
var rU = { kernelName: ms, backendName: "webgpu", kernelFunc: Gpe };
var ny = class {
constructor(e, t8) {
this.outputShape = [], this.variableNames = ["sortedSequence", "values"], this.uniforms = "numInputs : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.side = t8, this.shaderKey = `search_sorted_${t8}`;
}
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;
}
${H("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let value = getValuesByOutputIndex(index);
setOutputAtIndexI32(index, findBound(coords[0], value));
}
}
`;
}
};
function Hpe(r) {
let { inputs: e, backend: t8, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new ny([s.shape[0], s.shape[1]], a), p = [{ type: "int32", data: [n.shape[1]] }];
return t8.runWebGPUProgram(i, [n, s], "int32", p);
}
var oU = { kernelName: fs, backendName: "webgpu", kernelFunc: Hpe };
var sy = class {
constructor(e, t8, o) {
this.variableNames = ["c", "a", "b"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.cRank = e, this.rank = o, this.shaderKey = "select";
}
getUserCode() {
let e, t8;
if (this.rank > 4)
throw Error(`Where for rank ${this.rank} is not yet supported`);
if (this.rank === 1)
t8 = "resRC", e = "resRC";
else {
let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [], a = [];
for (let i = 0; i < this.outputShape.length; i++)
a.push(`${n[i]}`), i < this.cRank && s.push(`${n[i]}`);
e = s.join(), t8 = a.join();
}
return `
${H("index")} {
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputAtIndex(index, getA(${t8}));
} else {
setOutputAtIndex(index, getB(${t8}));
}
}
}
`;
}
};
function Kpe(r) {
let { inputs: e, backend: t8 } = r, { condition: o, t: n, e: s } = e, a = new sy(o.shape.length, n.shape, n.shape.length);
return t8.runWebGPUProgram(a, [o, n, s], dt(n.dtype, s.dtype));
}
var nU = { kernelName: da, backendName: "webgpu", kernelFunc: Kpe };
var qpe = ye({ opType: Z.SELU });
var sU = { kernelName: hs, backendName: "webgpu", kernelFunc: qpe };
var jpe = ye({ opType: Z.SIGMOID });
var aU = { kernelName: bs, backendName: "webgpu", kernelFunc: jpe };
var Xpe = ye({ opType: Z.SIGN });
var iU = { kernelName: ys, backendName: "webgpu", kernelFunc: Xpe };
var Ype = ye({ opType: Z.SIN });
var uU = { kernelName: gs, backendName: "webgpu", kernelFunc: Ype };
var Qpe = ye({ opType: Z.SINH });
var pU = { kernelName: xs, backendName: "webgpu", kernelFunc: Qpe };
var Zpe = ye({ opType: Z.SOFTPLUS });
var cU = { kernelName: Cs, backendName: "webgpu", kernelFunc: Zpe };
var ay = class {
constructor(e, t8, o, n, s, a) {
this.variableNames = ["x"], this.outputShape = [], this.uniforms = "", this.workgroupSize = [64, 1, 1], this.size = true;
let i = new Array(n.length);
for (let p = 0; p < i.length; p++)
i[p] = n[s[p]];
this.outputShape = i, this.newDim = s, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = e, this.paddedXShape = t8, this.uniforms += `reshapedPaddedXShape : ${ht(n.length)}, paddedXShapeStrides : ${ht(a)}, `, o.map((p, u) => {
this.uniforms += ` pad${u} : vec2<i32>,`;
}), this.shaderKey = `spaceToBatchND_${s}`;
}
getUserCode() {
let e = ht(this.outputShape.length), t8 = Gv(this.newDim);
return `
${im(this.paddedXShape, "PaddedX")}
${H("index")} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t8}), uniforms.reshapedPaddedXShape);
let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex);
${r0(this.xShape, true)}
}
}
`;
}
};
var Jpe = (r) => {
let { inputs: e, backend: t8, 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((b, C) => b * C), p = [[0, 0]];
p.push(...a);
for (let b = 1 + s.length; b < n.shape.length; ++b)
p.push([0, 0]);
let u = p.map((b, C) => b[0] + n.shape[C] + b[1]), c = w.getReshaped(u, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u, s, i, false), d = y.computeStrides(u), f = new ay(n.shape, u, p, c, l, d.length), h = [{ type: "int32", data: c }, { type: "int32", data: d }];
p.map((b) => h.push({ type: "int32", data: [b[0], b[1]] }));
let g = t8.runWebGPUProgram(f, [n], n.dtype, h), x = pe({ inputs: { x: g }, backend: t8, attrs: { shape: m } });
return t8.disposeData(g.dataId), x;
};
var lU = { kernelName: ha, backendName: "webgpu", kernelFunc: Jpe };
var iy = class {
constructor(e, t8) {
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] * t8[n];
this.outputShape = o, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile";
}
getUserCode() {
let e = ece(this.rank, "uniforms.");
return `
${H("index")} {
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`;
}
};
function ece(r, e = "") {
if (r >= 5)
throw Error(`Tile for rank ${r} is not yet supported`);
if (r === 1)
return `(resRC % ${e}aShape)`;
let t8 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = [];
for (let n = 0; n < r; n++)
o.push(`(${t8[n]} % ${e}aShape[${n}])`);
return o.join();
}
function xm(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { reps: s } = o;
if (t8.shouldExecuteOnCPU([n]) || n.dtype === "string" || n.shape.length >= 5) {
let p = t8.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = _z(c, s);
return t8.makeTensorInfo(l.shape, l.dtype, l.values);
}
let a = new iy(n.shape, s);
return t8.runWebGPUProgram(a, [n], n.dtype);
}
var mU = { kernelName: uo, backendName: "webgpu", kernelFunc: xm };
function tce(r) {
let { inputs: e, backend: t8, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false;
if (s.dtype === "string") {
let R = t8.bufferSync(n), D = t8.bufferSync(s), P = y.decodeString(t8.readSync(a.dataId)[0]), O = Sz(R, D, i, m, c, u, p, l, P, d);
return t8.makeTensorInfo(i, O.dtype, O.values);
}
let f = [m / c, c], h = pe({ inputs: { x: n }, backend: t8, attrs: { shape: [u, p] } }), g = s.shape.length ? pe({ inputs: { x: s }, backend: t8, attrs: { shape: [u, c] } }) : At({ inputs: { x: s }, backend: t8 }), x = g.dtype, b = t8.makeTensorInfo([], x, y.makeZerosTypedArray(1, x)), C = pe({ inputs: { x: a }, backend: t8, attrs: { shape: Array(f.length).fill(1) } }), S = xm({ inputs: { x: C }, backend: t8, 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 La([u, c], p, h.shape.length, g.shape.length, l, f, x, d);
t8.runWebGPUProgram(R, [g, h], x, _, S);
}
break;
default:
{
let R = new La([u, c], p, h.shape.length, b.shape.length, l, f, x, d);
t8.runWebGPUProgram(R, [b, h], x, _, S);
}
{
let R = new La([u, c], p, h.shape.length, g.shape.length, l, f, x);
t8.runWebGPUProgram(R, [g, h], x, _, S);
}
}
let E = pe({ inputs: { x: S }, backend: t8, attrs: { shape: i } });
return t8.disposeData(h.dataId), t8.disposeData(g.dataId), t8.disposeData(C.dataId), t8.disposeData(b.dataId), t8.disposeData(S.dataId), E;
}
var dU = { kernelName: vs, backendName: "webgpu", kernelFunc: tce };
function rce(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.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 = Gs({ inputs: { x: n }, backend: t8, attrs: { begin: c, size: d } });
return c[i] += m, f;
});
}
var fU = { kernelName: ga, backendName: "webgpu", kernelFunc: rce };
var oce = ye({ opType: Z.SQRT });
var hU = { kernelName: ws, backendName: "webgpu", kernelFunc: oce };
var gU = { kernelName: ji, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => {
let { x: t8 } = r, o = e, n = new Zr(t8.shape, Z.SQUARE);
return o.runWebGPUProgram(n, [t8], t8.dtype);
} };
var nce = et({ opType: fe.SQUARED_DIFFERENCE });
var xU = { kernelName: ks, backendName: "webgpu", kernelFunc: nce };
function sce({ inputs: r, attrs: e, backend: t8 }) {
let { x: o } = r, n = new Zr(o.shape, Z.STEP, "stepAlpha : f32,"), s = [{ type: "float32", data: [e.alpha] }];
return t8.runWebGPUProgram(n, [o], o.dtype, s);
}
var yU = { kernelName: wo, backendName: "webgpu", kernelFunc: sce };
var uy = class {
constructor(e) {
this.variableNames = ["x"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]);
let t8 = ht(this.outputShape.length);
this.uniforms = `begin : ${t8}, strides : ${t8}, `, this.shaderKey = "stridedSlice";
}
getUserCode() {
let e = this.outputShape.length, t8 = "";
if (e === 1)
t8 = "coords * uniforms.strides + uniforms.begin";
else {
let n = 0;
t8 = this.outputShape.map((s, a) => (n++, this.outputShape.length === 1 ? `coords * uniforms.strides[${a}] + uniforms.begin[${a}]` : `coords[${n - 1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",");
}
return `
${H("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
setOutputAtIndex(index, getX(${t8}));
}
}
`;
}
};
function ace(r) {
let { inputs: e, backend: t8, 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: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
if (h)
k = pe({ inputs: { x: n }, backend: t8, 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, C, S), E = Gs({ inputs: { x: n }, backend: t8, attrs: { begin: b, size: _ } });
k = pe({ inputs: { x: E }, backend: t8, attrs: { shape: f } }), t8.disposeData(E.dataId);
} else if (t8.shouldExecuteOnCPU([n])) {
let E = t8.readSync(n.dataId), R = me(n.shape, n.dtype, E), D = kz(d, R, S, b);
k = t8.makeTensorInfo(f, n.dtype, D.values);
} else {
let E = new uy(d), R = [{ type: "int32", data: b }, { type: "int32", data: S }], D = t8.runWebGPUProgram(E, [n], n.dtype, R);
k = pe({ inputs: { x: D }, backend: t8, attrs: { shape: f } }), t8.disposeData(D.dataId);
}
return k;
}
var bU = { kernelName: Ns, backendName: "webgpu", kernelFunc: ace };
function ice(r) {
let { inputs: e, backend: t8, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t8.readSync(c.dataId), d = t8.readSync(l.dataId), [f, h] = Nz(m, d, n, s, a, i, p, u);
return [t8.makeTensorInfo([f.length], "string", f), t8.makeTensorInfo(l.shape, "int32", h)];
}
var CU = { kernelName: xa, backendName: "webgpu", kernelFunc: ice };
var uce = et({ opType: fe.SUB, cpuKernelImpl: Tz, supportsComplex: true });
var wU = { kernelName: Ts, backendName: "webgpu", kernelFunc: uce };
var pce = ye({ opType: Z.TAN });
var SU = { kernelName: _s, backendName: "webgpu", kernelFunc: pce };
var cce = ye({ opType: Z.TANH });
var IU = { kernelName: $s, backendName: "webgpu", kernelFunc: cce };
function lce(r) {
let { inputs: e, backend: t8, attrs: o } = r, { tensor: n, indices: s, updates: a } = e, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(a, s, n.shape), m = [l / u, u];
if (l === 0)
return t8.makeTensorInfo(n.shape, s.dtype);
let d = [], f = pe({ inputs: { x: s }, backend: t8, attrs: { shape: [p, i] } });
d.push(f);
let h = pe({ inputs: { x: a }, backend: t8, attrs: { shape: [p, u] } });
d.push(h);
let g = pe({ inputs: { x: n }, backend: t8, attrs: { shape: m } });
d.push(g);
let x = xm({ inputs: { x: g }, backend: t8, attrs: { reps: Array(m.length).fill(1) } }), b = new La([p, u], i, f.shape.length, h.shape.length, c, m, n.dtype, false), C = y.sizeFromShape([p, u]), S = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [C] }], k = t8.runWebGPUProgram(b, [h, f], g.dtype, S, x);
d.push(k);
let _ = pe({ inputs: { x: k }, backend: t8, attrs: { shape: n.shape } });
return d.forEach((E) => t8.disposeData(E.dataId)), _;
}
var vU = { kernelName: ds, backendName: "webgpu", kernelFunc: lce };
var py = class {
constructor(e) {
this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
dir : i32, inc : i32,`, this.shaderKey = "swap";
}
getUserCode() {
return `
${H("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 cy = class {
constructor(e) {
this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = K(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge";
}
getUserCode() {
return `
${H("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 el(r, e) {
e !== null && r.disposeData(e.dataId);
}
function kU(r) {
let e = 1;
for (; e < r; )
e *= 2;
return e;
}
function mce(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = n.shape, p = i[i.length - 1];
if (t8.shouldExecuteOnCPU([n])) {
let k = t8.readSync(n.dataId), [_, E] = $z(k, i, n.dtype, s, a);
return [t8.makeTensorInfo(_.shape, _.dtype, _.values), t8.makeTensorInfo(E.shape, E.dtype, E.values)];
}
if (s === 0)
return i[i.length - 1] = 0, [t8.makeTensorInfo(i, n.dtype, []), t8.makeTensorInfo(i, "int32", [])];
if (p === 1)
return [n, Vt({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: t8 })];
let c = y.sizeFromShape(i) / p, l = pe({ inputs: { x: n }, attrs: { shape: [c, p] }, backend: t8 }), m = kU(s), d = kU(p), f = null, h = () => f === null ? [l, l] : [l, f], g = (k, _, E) => {
let R = h(), D = new py(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 = t8.runWebGPUProgram(D, R, "int32", O), el(t8, 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 cy([c, k / 2]), D = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [m] }], P = f;
f = t8.runWebGPUProgram(E, _, "int32", D), el(t8, P);
let O = m / 2, M = O * 2;
for (let L = O; L >= 1; L /= 2)
g(M, L, f.shape);
}
let x = f;
f = Gs({ inputs: { x: f }, backend: t8, attrs: { begin: 0, size: [c, s] } }), el(t8, x);
let b = e0({ inputs: { x: l, indices: f }, backend: t8, attrs: { axis: 1, batchDims: 1 } });
el(t8, l);
let C = i.slice(0, -1);
C.push(s), x = f, f = pe({ inputs: { x: f }, attrs: { shape: C }, backend: t8 }), el(t8, x);
let S = b;
return b = pe({ inputs: { x: b }, attrs: { shape: C }, backend: t8 }), el(t8, S), [b, f];
}
var NU = { kernelName: Es, backendName: "webgpu", kernelFunc: mce };
var ly = 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 = X(this.outputShape), this.dispatch = K(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;
}
${H("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 dce(r) {
let { inputs: e, backend: t8, 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 ly(g), b = a === "nearest" ? 1 : 2, C;
switch (i) {
case "constant":
C = 1;
break;
case "reflect":
C = 2;
break;
case "wrap":
C = 3;
break;
case "nearest":
C = 4;
break;
default:
C = 1;
break;
}
let S = [{ type: "int32", data: [b] }, { type: "int32", data: [C] }, { type: "float32", data: [p] }];
return t8.runWebGPUProgram(x, [n, s], "float32", S);
}
var TU = { kernelName: Rs, backendName: "webgpu", kernelFunc: dce };
function fce(r) {
let { inputs: e, backend: t8, 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 = Gs({ inputs: { x: a }, backend: t8, attrs: { begin: m, size: d } }), x = pe({ inputs: { x: g }, backend: t8, attrs: { shape: u } });
f[h] = x, l.push(g);
}
return l.forEach((h) => t8.disposeData(h.dataId)), f;
}
var _U = { kernelName: ya, backendName: "webgpu", kernelFunc: fce };
var my = class {
constructor(e, t8, o) {
if (this.outputShape = [], this.variableNames = ["x", "segmentIds"], this.uniforms = "numSegments : i32, xSize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = t8, this.dispatchLayout = X(e), this.dispatch = K(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 `
${H("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);
${Us("&result[flatIndex]", "value", this.type)}
}
}
}
`;
}
};
function hce(r) {
let { inputs: e, backend: t8, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = w.getAxesPermutation([u], i), l = n;
c != null && (l = yr({ inputs: { x: n }, backend: t8, attrs: { perm: c } }), p.push(l), u = w.getInnerMostAxes(1, i)[0]);
let m = w.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = pe({ inputs: { x: l }, backend: t8, attrs: { shape: [-1, d] } });
p.push(f);
let h = n.dtype, g = [f.shape[0], a], x = Vt({ backend: t8, attrs: { shape: g, value: 0, dtype: h } }), b = new my(f.shape, g, h), C = [{ type: "int32", data: [a] }, { type: "int32", data: [y.sizeFromShape(f.shape)] }], S = t8.runWebGPUProgram(b, [f, s], h, C, x), k = pe({ inputs: { x: S }, backend: t8, attrs: { shape: m } });
p.push(S);
let _ = k;
if (c != null) {
p.push(k);
let E = w.getUndoAxesPermutation(c);
_ = yr({ inputs: { x: _ }, backend: t8, attrs: { perm: E } });
}
return p.forEach((E) => t8.disposeData(E.dataId)), _;
}
var $U = { kernelName: Zi, backendName: "webgpu", kernelFunc: hce };
var gce = [jB, Rz, Dz, Az, Fz, Pz, Mz, Lz, Bz, zz, Vz, Wz, Uz, Gz, Hz, jz, Xz, Yz, Qz, Zz, eV, tV, rV, aV, iV, uV, YB, cV, mV, dV, fV, hV, gV, xV, yV, bV, CV, wV, vV, kV, NV, TV, $V, EV, _V, RV, DV, AV, FV, MV, LV, BV, zV, VV, WV, UV, GV, HV, KB, KV, XV, qV, jV, YV, QV, ZV, JV, eW, tW, rW, XB, oW, lV, nW, sW, aW, iW, uW, pW, cW, mW, lW, dW, fW, hW, xW, yW, Kz, bW, CW, IW, wW, SW, vW, qz, kW, NW, TW, _W, EW, PV, RW, DW, AW, oV, FW, MW, LW, BW, zW, VW, WW, UW, nV, GW, HW, KW, qW, qB, jW, XW, YW, QW, ZW, JW, eU, tU, rU, oU, nU, sU, aU, iU, uU, pU, Jz, yU, bU, CU, $W, cU, lU, dU, fU, hU, gU, xU, wU, OV, SU, IU, vU, mU, NU, TU, Oz, _U, $U, PW];
for (let r of gce)
Ja(r);
var EU = "4.7.0";
var xce = "4.7.0";
var yce = "4.7.0";
var bce = "4.7.0";
var Cce = "4.7.0";
var wce = "4.7.0";
var Sce = { tfjs: EU, "tfjs-core": EU, "tfjs-converter": xce, "tfjs-backend-cpu": yce, "tfjs-backend-webgl": bce, "tfjs-backend-wasm": Cce, "tfjs-backend-webgpu": wce };
// src/util/util.ts
function log(...msg) {
const dt2 = /* @__PURE__ */ new Date();
const ts2 = `${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 y10 = (x - 1) * -0.5;
filter.colorMatrix([
x,
y10,
y10,
0,
0,
y10,
x,
y10,
0,
0,
y10,
y10,
x,
0,
0,
0,
0,
0,
1,
0
]);
},
desaturate: () => {
filter.saturation(-1);
},
contrast: (amount) => {
const v10 = (amount || 0) + 1;
const o = -128 * (v10 - 1);
filter.colorMatrix([
v10,
0,
0,
0,
o,
0,
v10,
0,
0,
o,
0,
0,
v10,
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 ? mc(inputImage) : inputImage;
const rgb3 = pi(squeeze, 3, 2);
const min = [vl(rgb3[0]), vl(rgb3[1]), vl(rgb3[2])];
const max = [_a(rgb3[0]), _a(rgb3[1]), _a(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 = kr([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 = ai(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 ut) && !(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 ut) {
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 = ai(input, 0);
} else if (input.shape[2] === 4) {
const rgb3 = N1(input, [0, 0, 0], [-1, -1, 3]);
tensor2 = ai(rgb3, 0);
Ot(rgb3);
}
} else if (input.shape.length === 4) {
if (input.shape[3] === 3) {
tensor2 = Ur(input);
} else if (input.shape[3] === 4) {
tensor2 = T1(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 = qe(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 && XN) {
pixels = XN ? XN.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 (XN && env.browser) {
if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
pixels = XN.fromPixels(outCanvas);
} else {
tmpCanvas = copy(outCanvas);
pixels = XN.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 = N1(pixels, [0, 0, 0], [-1, -1, 3]);
Ot(pixels);
pixels = rgb3;
}
if (!pixels)
throw new Error("input error: cannot create tensor");
const casted = qe(pixels, "float32");
const tensor = config3.filter.equalization ? await histogramEqualization(casted) : ai(casted, 0);
Ot([pixels, casted]);
if (config3.filter.autoBrightness) {
const max = _a(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 = Ur(input);
} else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
Ot(last.inputTensor);
last.inputTensor = Ur(input);
} else {
const t8 = {};
t8.diff = Te(input, last.inputTensor);
t8.squared = se(t8.diff, t8.diff);
t8.sum = ot(t8.squared);
const diffSum = await t8.sum.data();
const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
Ot([last.inputTensor, t8.diff, t8.squared, t8.sum]);
last.inputTensor = Ur(input);
skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
}
return skipFrame;
}
async function compare(config3, input1, input2) {
const t8 = {};
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;
}
t8.input1 = Ur(input1);
t8.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? Fj.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : Ur(input2);
t8.diff = Te(t8.input1, t8.input2);
t8.squared = se(t8.diff, t8.diff);
t8.sum = ot(t8.squared);
const diffSum = await t8.sum.data();
const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
Ot([t8.input1, t8.input2, t8.diff, t8.squared, t8.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: Sce["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: dde()["binding"] ? dde()["binding"].TF_Version : void 0,
gpu: dde()["binding"] ? dde()["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 A().getAsync("WASM_HAS_SIMD_SUPPORT");
this.wasm.multithread = await A().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 = jm(pde()).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 el2 = document.getElementById(webcamConfig.element);
if (el2 && el2 instanceof HTMLVideoElement) {
this.element = el2;
} else {
if (this.config.debug)
log("webcam", "cannot get dom element", webcamConfig.element);
return `webcam error: cannot get dom element: ${webcamConfig.element}`;
}
} else if (webcamConfig.element instanceof HTMLVideoElement) {
this.element = webcamConfig.element;
} else {
if (this.config.debug)
log("webcam", "unknown dom element", webcamConfig.element);
return `webcam error: unknown dom element: ${webcamConfig.element}`;
}
} 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 error", "no devices");
return "webcam error: no devices";
}
try {
this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints);
} catch (err) {
log("webcam", err);
return `webcam error: ${err}`;
}
if (!this.stream) {
if (this.config.debug)
log("webcam error", "no stream");
return "webcam error no stream";
}
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
});
}
return `webcam: ${this.label}`;
});
/** 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 mi.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 h8(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 = "3.0.7";
// 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 (!lde(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 {
gI(2, config2.gl);
} catch (err) {
log("humangl error: cannot set webgl context:", err);
return;
}
try {
const ctx = new wp(config2.gl);
nu(config2.name, () => new bu(ctx), config2.priority);
} catch (err) {
log("humangl error: cannot register webgl backend:", err);
return;
}
try {
const kernels = jm("webgl");
kernels.forEach((kernelConfig) => {
const newKernelConfig = { ...kernelConfig, backendName: config2.name };
Ja(newKernelConfig);
});
} catch (err) {
log("humangl error: cannot update webgl backend registration:", err);
return;
}
try {
if (A().flagRegistry.WEBGL_VERSION)
A().set("WEBGL_VERSION", 2);
} catch (err) {
log("humangl error: cannot set WebGL backend flags:", err);
return;
}
extensions();
const backend = dde();
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: pde(),
kernelFunc: (op2) => De(() => Te(op2.inputs.a, se(je(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
};
Ja(kernelMod);
env.kernels.push("mod");
newKernels.push("mod");
}
if (!env.kernels.includes("floormod")) {
const kernelFloorMod = {
kernelName: "FloorMod",
backendName: pde(),
kernelFunc: (op2) => De(() => Ce(se(ld(op2.inputs.a, op2.inputs.b), op2.inputs.b), T2(op2.inputs.a, op2.inputs.b)))
};
Ja(kernelFloorMod);
env.kernels.push("floormod");
newKernels.push("floormod");
}
if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) {
const kernelRotateWithOffset = {
kernelName: "RotateWithOffset",
backendName: pde(),
kernelFunc: (op2) => De(() => {
const backend = pde();
ide("cpu");
const t8 = Fj.rotateWithOffset(op2.inputs.image, op2.attrs.radians, op2.attrs.fillValue, op2.attrs.center);
ide(backend);
return t8;
})
};
Ja(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 && pde() !== 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 (A().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY)
A().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
if (instance.config.debug)
log("wasm path:", instance.config.wasmPath);
if (typeof Ose !== "undefined")
Ose(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 A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
simd = await A().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 ide(instance.config.backend);
await ude();
} catch (err) {
log("error: cannot set backend:", instance.config.backend, err);
return false;
}
if (instance.config.debug)
defaultFlags = JSON.parse(JSON.stringify(A().flags));
}
if (pde() === "humangl" || pde() === "webgl") {
if (A().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
A().set("WEBGL_USE_SHAPES_UNIFORMS", true);
if (A().flagRegistry.WEBGL_EXP_CONV)
A().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);
A().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
}
}
if (pde() === "webgpu") {
}
if (instance.config.debug) {
const newFlags = A().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:", pde(), "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)) {
A().set(key, val);
}
}
ede();
init();
instance.performance.initBackend = Math.trunc(now() - timeStamp);
instance.config.backend = pde();
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); },
};
Ja(kernelConfig);
}
env.kernels = jm(pde()).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", { willReadFrequently: true });
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 y10 = i * localOptions2.lineHeight + startY;
if (localOptions2.shadowColor && localOptions2.shadowColor !== "") {
ctx.fillStyle = localOptions2.shadowColor;
ctx.fillText(line[i], x + 5, y10 + 16);
}
ctx.fillStyle = localOptions2.labelColor;
ctx.fillText(line[i], x + 4, y10 + 15);
}
}
function point(ctx, x, y10, z, localOptions2) {
ctx.fillStyle = colorDepth(z, localOptions2);
ctx.beginPath();
ctx.arc(x, y10, localOptions2.pointSize, 0, 2 * Math.PI);
ctx.fill();
}
function rect(ctx, x, y10, width, height, localOptions2) {
ctx.beginPath();
ctx.lineWidth = localOptions2.lineWidth;
if (localOptions2.useCurves) {
const cx2 = (x + x + width) / 2;
const cy2 = (y10 + y10 + height) / 2;
ctx.ellipse(cx2, cy2, width / 2, height / 2, 0, 0, 2 * Math.PI);
} else {
ctx.moveTo(x + localOptions2.roundRect, y10);
ctx.lineTo(x + width - localOptions2.roundRect, y10);
ctx.quadraticCurveTo(x + width, y10, x + width, y10 + localOptions2.roundRect);
ctx.lineTo(x + width, y10 + height - localOptions2.roundRect);
ctx.quadraticCurveTo(x + width, y10 + height, x + width - localOptions2.roundRect, y10 + height);
ctx.lineTo(x + localOptions2.roundRect, y10 + height);
ctx.quadraticCurveTo(x, y10 + height, x, y10 + height - localOptions2.roundRect);
ctx.lineTo(x, y10 + localOptions2.roundRect);
ctx.quadraticCurveTo(x, y10, x + localOptions2.roundRect, y10);
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 y10;
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];
y10 = radius * Math.sin(angle) + to[1];
ctx.moveTo(x, y10);
angle += 1 / 3 * (2 * Math.PI);
x = radius * Math.cos(angle) + to[0];
y10 = radius * Math.sin(angle) + to[1];
ctx.lineTo(x, y10);
angle += 1 / 3 * (2 * Math.PI);
x = radius * Math.cos(angle) + to[0];
y10 = radius * Math.sin(angle) + to[1];
ctx.lineTo(x, y10);
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
];
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104,
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151,
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48,
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196,
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92,
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2,
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0,
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169,
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32,
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125,
237,
179,
86,
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179,
181,
84,
180,
182,
83,
181,
194,
201,
182,
177,
137,
132,
184,
76,
183,
185,
61,
184,
186,
57,
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216,
212,
186,
192,
214,
187,
139,
34,
156,
218,
79,
237,
147,
123,
177,
45,
44,
4,
208,
201,
32,
98,
64,
129,
192,
213,
138,
235,
59,
219,
141,
242,
97,
97,
2,
141,
240,
75,
235,
229,
24,
228,
31,
25,
226,
230,
23,
229,
231,
22,
230,
232,
26,
231,
233,
112,
232,
244,
189,
243,
189,
221,
190,
222,
28,
221,
223,
27,
222,
224,
29,
223,
225,
30,
224,
113,
247,
225,
99,
60,
240,
213,
147,
215,
60,
20,
166,
192,
187,
213,
243,
112,
244,
244,
233,
245,
245,
128,
188,
188,
114,
174,
134,
131,
220,
174,
217,
236,
236,
198,
134,
215,
177,
58,
156,
143,
124,
25,
110,
7,
31,
228,
25,
264,
356,
368,
0,
11,
267,
451,
452,
349,
267,
302,
269,
350,
357,
277,
350,
452,
357,
299,
333,
297,
396,
175,
377,
381,
384,
382,
280,
347,
330,
269,
303,
270,
151,
9,
337,
344,
278,
360,
424,
418,
431,
270,
304,
409,
272,
310,
407,
322,
270,
410,
449,
450,
347,
432,
422,
434,
18,
313,
17,
291,
306,
375,
259,
387,
260,
424,
335,
418,
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364,
416,
391,
423,
327,
301,
251,
298,
275,
281,
4,
254,
373,
253,
375,
307,
321,
280,
425,
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200,
421,
18,
335,
321,
406,
321,
320,
405,
314,
315,
17,
423,
426,
266,
396,
377,
369,
270,
322,
269,
413,
417,
464,
385,
386,
258,
248,
456,
419,
298,
284,
333,
168,
417,
8,
448,
346,
261,
417,
413,
285,
326,
327,
328,
277,
355,
329,
309,
392,
438,
381,
382,
256,
279,
429,
360,
365,
364,
379,
355,
277,
437,
282,
443,
283,
281,
275,
363,
395,
431,
369,
299,
297,
337,
335,
273,
321,
348,
450,
349,
359,
446,
467,
283,
293,
282,
250,
458,
462,
300,
276,
383,
292,
308,
325,
283,
276,
293,
264,
372,
447,
346,
352,
340,
354,
274,
19,
363,
456,
281,
426,
436,
425,
380,
381,
252,
267,
269,
393,
421,
200,
428,
371,
266,
329,
432,
287,
422,
290,
250,
328,
385,
258,
384,
446,
265,
342,
386,
387,
257,
422,
424,
430,
445,
342,
276,
422,
273,
424,
306,
292,
307,
352,
366,
345,
268,
271,
302,
358,
423,
371,
327,
294,
460,
331,
279,
294,
303,
271,
304,
436,
432,
427,
304,
272,
408,
395,
394,
431,
378,
395,
400,
296,
334,
299,
6,
351,
168,
376,
352,
411,
307,
325,
320,
285,
295,
336,
320,
319,
404,
329,
330,
349,
334,
293,
333,
366,
323,
447,
316,
15,
315,
331,
358,
279,
317,
14,
316,
8,
285,
9,
277,
329,
350,
253,
374,
252,
319,
318,
403,
351,
6,
419,
324,
318,
325,
397,
367,
365,
288,
435,
397,
278,
344,
439,
310,
272,
311,
248,
195,
281,
375,
273,
291,
175,
396,
199,
312,
311,
268,
276,
283,
445,
390,
373,
339,
295,
282,
296,
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449,
346,
356,
264,
454,
337,
336,
299,
337,
338,
151,
294,
278,
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415,
429,
358,
355,
265,
340,
372,
388,
390,
466,
352,
346,
280,
295,
442,
282,
354,
19,
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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,
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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,
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366,
352,
401,
1,
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418,
421,
262,
331,
294,
358,
435,
433,
367,
392,
289,
439,
328,
462,
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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,
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343,
357,
412,
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343,
399,
344,
360,
440,
420,
437,
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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, v10] of Object.entries((f == null ? void 0 : f.annotations) || {})) {
if (!(v10 == null ? void 0 : v10[0]))
continue;
const pt2 = v10[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 y10 = 0; y10 < featureMapHeight; ++y10) {
for (let x = 0; x < featureMapWidth; ++x) {
for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y10 + 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 = pi(boxesTensor, 12, 1);
let xCenter = mc(split[0]);
let yCenter = mc(split[1]);
let width = mc(split[2]);
let height = mc(split[3]);
xCenter = Ce(je(xCenter, inputSize), anchor.x);
yCenter = Ce(je(yCenter, inputSize), anchor.y);
width = se(je(width, inputSize), cropFactor[0]);
height = se(je(height, inputSize), cropFactor[1]);
const xMin = Te(xCenter, je(width, 2));
const yMin = Te(yCenter, je(height, 2));
const xMax = Ce(xMin, width);
const yMax = Ce(yMin, height);
const boxes = kr([xMin, yMin, xMax, yMax], 1);
return boxes;
});
}
async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) {
var _a2, _b;
const detectedBoxes = [];
const t8 = {};
t8.boxes = decodeBoxes(boxesTensor, anchorTensor);
t8.scores = Na(logitsTensor);
t8.nms = await Fj.nonMaxSuppressionAsync(t8.boxes, t8.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 t8.nms.data();
const scores = await t8.scores.data();
const boxes = await t8.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(t8).forEach((tensor) => Ot(t8[tensor]));
return detectedBoxes;
}
async function detectBoxes(input, config3, outputSize2) {
const t8 = {};
t8.res = model == null ? void 0 : model.execute(input, ["Identity"]);
t8.logitsRaw = Xe(t8.res, [0, 0, 0], [1, -1, 1]);
t8.boxesRaw = Xe(t8.res, [0, 0, 1], [1, -1, -1]);
t8.logits = mc(t8.logitsRaw);
t8.boxes = mc(t8.boxesRaw);
const boxes = await decodeResults(t8.boxes, t8.logits, config3, outputSize2);
Object.keys(t8).forEach((tensor) => Ot(t8[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 t8 = {};
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) {
t8.cropped = Fj.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
];
t8.pad = Ea(t8.cropped || input, padding);
t8.resize = Fj.resizeBilinear(t8.pad, [size2, size2]);
final = je(t8.resize, constants.tf255);
} else if (input.shape[1] !== size2) {
t8.resize = Fj.resizeBilinear(t8.cropped || input, [size2, size2]);
final = je(t8.resize, constants.tf255);
} else {
final = je(t8.cropped || input, constants.tf255);
}
Object.keys(t8).forEach((tensor) => Ot(t8[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 t8 = {};
[
t8.ld,
t8.segmentation,
t8.heatmap,
t8.world,
t8.poseflag
/* 1,1 */
] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks);
const poseScore = (await t8.poseflag.data())[0];
const points = await t8.ld.data();
const distances = await t8.world.data();
Object.keys(t8).forEach((tensor) => Ot(t8[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 t8 = {};
const results = [];
const detections = await res.array();
t8.squeeze = mc(res);
const arr = pi(t8.squeeze, 6, 1);
t8.stack = kr([arr[1], arr[0], arr[3], arr[2]], 1);
t8.boxes = mc(t8.stack);
t8.scores = mc(arr[4]);
t8.classes = mc(arr[5]);
Ot([res, ...arr]);
t8.nms = await Fj.nonMaxSuppressionAsync(t8.boxes, t8.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0);
const nms = await t8.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, y10] = [
detections[0][id2][0] / inputSize3,
detections[0][id2][1] / inputSize3
];
const boxRaw = [
x,
y10,
detections[0][id2][2] / inputSize3 - x,
detections[0][id2][3] / inputSize3 - y10
];
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(t8).forEach((tensor) => Ot(t8[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 = Fj.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 = _a(reshaped, 0);
const newScore = (await max.data())[0];
if (newScore > minScore) {
const coordinates = Sk(reshaped, 0);
const mod = T2(coordinates, width);
const x = (await mod.data())[0];
const div = je(coordinates, width);
const y10 = (await div.data())[0];
Ot([reshaped, max, coordinates, mod, div]);
return [x, y10, 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 = Fj.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 = mc(resT);
Ot(resT);
const stack = mo(squeeze, 2);
Ot(squeeze);
for (let id2 = 0; id2 < stack.length; id2++) {
const [x5, y11, 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],
y11 / 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] * y11 / 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 y10 = cache2.keypoints.map((a) => a.position[1]);
cache2.box = [
Math.min(...x),
Math.min(...y10),
Math.max(...x) - Math.min(...x),
Math.max(...y10) - Math.min(...y10)
];
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 w10 = image.shape[2];
const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w10, box.endPoint[1] / h, box.endPoint[0] / w10];
const crop = Fj.cropAndResize(image, [cutBox], [0], cropSize);
const norm = je(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 y10 = landmarks.map((d) => d[1]);
return {
startPoint: [Math.min(...x), Math.min(...y10)],
endPoint: [Math.max(...x), Math.max(...y10)],
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, y10) => [[1, 0, x], [0, 1, y10], [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 = Fj.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 y10 = mesh.map((m) => m[1]);
return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y10) + (Math.max(...y10) - Math.min(...y10)) / 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 = mu(generateAnchors(inputSize4));
return model5;
}
function decodeBoxes2(boxOutputs) {
if (!anchors || !inputSizeT)
return Gr([0, 0]);
const t8 = {};
t8.boxStarts = Xe(boxOutputs, [0, 1], [-1, 2]);
t8.centers = Ce(t8.boxStarts, anchors);
t8.boxSizes = Xe(boxOutputs, [0, 3], [-1, 2]);
t8.boxSizesNormalized = je(t8.boxSizes, inputSizeT);
t8.centersNormalized = je(t8.centers, inputSizeT);
t8.halfBoxSize = je(t8.boxSizesNormalized, constants.tf2);
t8.starts = Te(t8.centersNormalized, t8.halfBoxSize);
t8.ends = Ce(t8.centersNormalized, t8.halfBoxSize);
t8.startNormalized = se(t8.starts, inputSizeT);
t8.endNormalized = se(t8.ends, inputSizeT);
const boxes = Wk([t8.startNormalized, t8.endNormalized], 1);
Object.keys(t8).forEach((tensor) => Ot(t8[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 t8 = {};
t8.resized = Fj.resizeBilinear(inputImage, [inputSize4, inputSize4]);
t8.div = je(t8.resized, constants.tf127);
t8.normalized = Te(t8.div, constants.tf05);
const res = model5 == null ? void 0 : model5.execute(t8.normalized);
if (Array.isArray(res) && res.length > 2) {
const sorted = res.sort((a, b) => a.size - b.size);
t8.concat384 = bt([sorted[0], sorted[2]], 2);
t8.concat512 = bt([sorted[1], sorted[3]], 2);
t8.concat = bt([t8.concat512, t8.concat384], 1);
t8.batch = mc(t8.concat, [0]);
} else if (Array.isArray(res)) {
t8.batch = mc(res[0]);
} else {
t8.batch = mc(res);
}
Ot(res);
t8.boxes = decodeBoxes2(t8.batch);
t8.logits = Xe(t8.batch, [0, 0], [-1, 1]);
t8.sigmoid = Na(t8.logits);
t8.scores = mc(t8.sigmoid);
t8.nms = await Fj.nonMaxSuppressionAsync(t8.boxes, t8.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 t8.nms.array();
const boxes = [];
const scores = await t8.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 = Xe(t8.boxes, [nms[i], 0], [1, -1]);
b.slice = Xe(t8.batch, [nms[i], keypointsCount - 1], [1, -1]);
b.squeeze = mc(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 = Xe(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(t8).forEach((tensor) => Ot(t8[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}${key}`];
if (!keys || keys.includes(key)) {
for (let j = 0; j < indices.length; j++) {
const index2 = indices[j];
rawCoords[originalIndices[j]] = [
newCoords[index2][0],
newCoords[index2][1],
(newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2
];
}
}
}
}
var getLeftToRightEyeDepthDifference = (rawCoords) => {
const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
return leftEyeZ - rightEyeZ;
};
var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
const boxSize = getBoxSize(box);
let crop = Fj.cropAndResize(face4, [[
box.startPoint[1] / meshSize,
box.startPoint[0] / meshSize,
box.endPoint[1] / meshSize,
box.endPoint[0] / meshSize
]], [0], [inputSize5, inputSize5]);
if (flip && env.kernels.includes("flipleftright")) {
const flipped = Fj.flipLeftRight(crop);
Ot(crop);
crop = flipped;
}
return { box, boxSize, crop };
};
var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
const eyeRawCoords = [];
for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
const x = eyeData[i * 3];
const y10 = eyeData[i * 3 + 1];
const z = eyeData[i * 3 + 2];
eyeRawCoords.push([
(flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
y10 / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
z
]);
}
return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
};
var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
const upperCenterZ = rawCoords[meshAnnotations[`${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 = bt([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 t8 = {
// 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(t8)) {
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 < t8.irisL.length / 2; i++)
rawCoords.push([t8.irisL[2 * i + 0], t8.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 < t8.irisR.length / 2; i++)
rawCoords.push([t8.irisR[2 * i + 0], t8.irisR[2 * i + 1], irisRDepth]);
for (let i = 0; i < t8.eyeL.length / 2; i++)
rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t8.eyeL[2 * i + 0], t8.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];
for (let i = 0; i < t8.eyeR.length / 2; i++)
rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t8.eyeR[2 * i + 0], t8.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];
for (let i = 0; i < t8.lips.length / 2; i++)
rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t8.lips[2 * i + 0], t8.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((t8) => t8.shape[t8.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.size = box.size;
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((t8) => t8.shape[t8.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);
face4.size = calculatedBox.size;
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 t8 = {};
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]];
t8.resize = Fj.cropAndResize(image, box, [0], [inputSize10, inputSize10]);
} else {
t8.resize = Fj.resizeBilinear(image, [inputSize10, inputSize10], false);
}
if (rgb) {
t8.mul = se(t8.resize, 255);
t8.normalize = Te(t8.mul, [103.939, 116.779, 123.68]);
t8.emotion = model8 == null ? void 0 : model8.execute(t8.normalize);
} else {
t8.channels = se(t8.resize, constants.rgb);
t8.grayscale = ot(t8.channels, 3, true);
t8.grayscaleSub = Te(t8.grayscale, constants.tf05);
t8.grayscaleMul = se(t8.grayscaleSub, constants.tf2);
t8.emotion = model8 == null ? void 0 : model8.execute(t8.grayscaleMul);
}
lastTime4 = now();
const data = await t8.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(t8).forEach((tensor) => Ot(t8[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 = Fj.cropAndResize(tensor, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]);
} else {
crop = Fj.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((t8) => t8.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 = Sk(resT.find((t8) => t8.shape[1] === 100), 1);
const ageIdx = (await argmax.data())[0];
Ot(argmax);
const ageT = resT.find((t8) => t8.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((t8) => t8.shape[1] === 1024);
const descriptor = desc ? await desc.data() : [];
obj.descriptor = Array.from(descriptor);
resT.forEach((t8) => Ot(t8));
}
last4[idx] = obj;
lastCount2 = count2;
resolve(obj);
});
}
// src/face/mask.ts
var expandFact = 0.1;
var alpha = 0.5;
function insidePoly(x, y10, polygon) {
let inside = false;
let j = polygon.length - 1;
for (let i = 0; i < polygon.length; j = i++) {
if (polygon[i].y > y10 !== polygon[j].y > y10 && x < (polygon[j].x - polygon[i].x) * (y10 - 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 y10 = 0; y10 < height; y10++) {
const inside = insidePoly(x / width, y10 / width, silhouette);
if (!inside) {
buffer.set(alpha * buffer.get(0, y10, x, 0), 0, y10, x, 0);
buffer.set(alpha * buffer.get(0, y10, x, 1), 0, y10, x, 1);
buffer.set(alpha * buffer.get(0, y10, x, 2), 0, y10, 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 = Fj.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 = Fj.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 t8 = {};
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]];
}
t8.resize = Fj.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)
[t8.age, t8.gender, t8.race] = model12.execute(t8.resize, ["age_output", "gender_output", "race_output"]);
const gender2 = await t8.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 t8.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 t8.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(t8).forEach((tensor) => Ot(t8[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 t8 = {};
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]];
t8.resize = Fj.cropAndResize(image, box, [0], [model13.inputs[0].shape[2], model13.inputs[0].shape[1]]);
} else {
t8.resize = Fj.resizeBilinear(image, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
}
t8.enhance = se(t8.resize, constants.tf255);
const obj = { age: 0 };
if ((_c3 = config3.face["ssrnet"]) == null ? void 0 : _c3.enabled)
t8.age = model13.execute(t8.enhance);
if (t8.age) {
const data = await t8.age.data();
obj.age = Math.trunc(10 * data[0]) / 10;
}
Object.keys(t8).forEach((tensor) => Ot(t8[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 t8 = {};
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]];
t8.resize = Fj.cropAndResize(image, box, [0], [model14.inputs[0].shape[2], model14.inputs[0].shape[1]]);
} else {
t8.resize = Fj.resizeBilinear(image, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false);
}
t8.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] = pi(t8.resize, 3, 3);
const redNorm = se(red, rgb2[0]);
const greenNorm = se(green, rgb2[1]);
const blueNorm = se(blue, rgb2[2]);
const grayscale = bk([redNorm, greenNorm, blueNorm]);
normalize2 = se(Te(grayscale, constants.tf05), 2);
} else {
normalize2 = se(Te(t8.resize, constants.tf05), 2);
}
return normalize2;
});
const obj = { gender: "unknown", genderScore: 0 };
if ((_c3 = config3.face["ssrnet"]) == null ? void 0 : _c3.enabled)
t8.gender = model14.execute(t8.enhance);
const data = await t8.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(t8).forEach((tensor) => Ot(t8[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 t8 = {};
t8.crop = Fj.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false);
t8.data = model15.execute(t8.crop);
const output = await t8.data.data();
data = Array.from(output);
Object.keys(t8).forEach((tensor) => Ot(t8[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 t8 = {};
t8.crop = Fj.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false);
t8.data = model16.execute(t8.crop);
const output = await t8.data.data();
data = Array.from(output);
Object.keys(t8).forEach((tensor) => Ot(t8[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 = (v10) => {
const length = Math.sqrt(v10[0] * v10[0] + v10[1] * v10[1] + v10[2] * v10[2]);
v10[0] /= length;
v10[1] /= length;
v10[2] /= length;
return v10;
};
const subVectors = (a, b) => {
const x = a[0] - b[0];
const y10 = a[1] - b[1];
const z = a[2] - b[2];
return [x, y10, z];
};
const crossVectors = (a, b) => {
const x = a[1] * b[2] - a[2] * b[1];
const y10 = a[2] * b[0] - a[0] * b[2];
const z = a[0] * b[1] - a[1] * b[0];
return [x, y10, 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, _u, _v2, _w2;
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 (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.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 = ((_w2 = instance.config.face.detector) == null ? void 0 : _w2.return) ? mc(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((el2) => el2 * 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 (rightIrisCenterX > 0.04)
gestures.push({ iris: i, gesture: "looking right" });
} else {
if (leftIrisCenterX > 0.04)
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 w10 = image.shape[2];
const boxes = [[
box.startPoint[1] / h,
box.startPoint[0] / w10,
box.endPoint[1] / h,
box.endPoint[0] / w10
]];
return Fj.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, y10) => [[1, 0, x], [0, 1, y10], [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 },
{ x: 0.796875, y: 0.078125 },
{ x: 0.796875, y: 0.078125 },
{ x: 0.828125, y: 0.078125 },
{ x: 0.828125, y: 0.078125 },
{ x: 0.859375, y: 0.078125 },
{ x: 0.859375, y: 0.078125 },
{ x: 0.890625, y: 0.078125 },
{ x: 0.890625, y: 0.078125 },
{ x: 0.921875, y: 0.078125 },
{ x: 0.921875, y: 0.078125 },
{ x: 0.953125, y: 0.078125 },
{ x: 0.953125, y: 0.078125 },
{ x: 0.984375, y: 0.078125 },
{ x: 0.984375, y: 0.078125 },
{ x: 0.015625, y: 0.109375 },
{ x: 0.015625, y: 0.109375 },
{ x: 0.046875, y: 0.109375 },
{ x: 0.046875, y: 0.109375 },
{ x: 0.078125, y: 0.109375 },
{ x: 0.078125, y: 0.109375 },
{ x: 0.109375, y: 0.109375 },
{ x: 0.109375, y: 0.109375 },
{ x: 0.140625, y: 0.109375 },
{ x: 0.140625, y: 0.109375 },
{ x: 0.171875, y: 0.109375 },
{ x: 0.171875, y: 0.109375 },
{ x: 0.203125, y: 0.109375 },
{ x: 0.203125, y: 0.109375 },
{ x: 0.234375, y: 0.109375 },
{ x: 0.234375, y: 0.109375 },
{ x: 0.265625, y: 0.109375 },
{ x: 0.265625, y: 0.109375 },
{ x: 0.296875, y: 0.109375 },
{ x: 0.296875, y: 0.109375 },
{ x: 0.328125, y: 0.109375 },
{ x: 0.328125, y: 0.109375 },
{ x: 0.359375, y: 0.109375 },
{ x: 0.359375, y: 0.109375 },
{ x: 0.390625, y: 0.109375 },
{ x: 0.390625, y: 0.109375 },
{ x: 0.421875, y: 0.109375 },
{ x: 0.421875, y: 0.109375 },
{ x: 0.453125, y: 0.109375 },
{ x: 0.453125, y: 0.109375 },
{ x: 0.484375, y: 0.109375 },
{ x: 0.484375, y: 0.109375 },
{ x: 0.515625, y: 0.109375 },
{ x: 0.515625, y: 0.109375 },
{ x: 0.546875, y: 0.109375 },
{ x: 0.546875, y: 0.109375 },
{ x: 0.578125, y: 0.109375 },
{ x: 0.578125, y: 0.109375 },
{ x: 0.609375, y: 0.109375 },
{ x: 0.609375, y: 0.109375 },
{ x: 0.640625, y: 0.109375 },
{ x: 0.640625, y: 0.109375 },
{ x: 0.671875, y: 0.109375 },
{ x: 0.671875, y: 0.109375 },
{ x: 0.703125, y: 0.109375 },
{ x: 0.703125, y: 0.109375 },
{ x: 0.734375, y: 0.109375 },
{ x: 0.734375, y: 0.109375 },
{ x: 0.765625, y: 0.109375 },
{ x: 0.765625, y: 0.109375 },
{ x: 0.796875, y: 0.109375 },
{ x: 0.796875, y: 0.109375 },
{ x: 0.828125, y: 0.109375 },
{ x: 0.828125, y: 0.109375 },
{ x: 0.859375, y: 0.109375 },
{ x: 0.859375, y: 0.109375 },
{ x: 0.890625, y: 0.109375 },
{ x: 0.890625, y: 0.109375 },
{ x: 0.921875, y: 0.109375 },
{ x: 0.921875, y: 0.109375 },
{ x: 0.953125, y: 0.109375 },
{ x: 0.953125, y: 0.109375 },
{ x: 0.984375, y: 0.109375 },
{ x: 0.984375, y: 0.109375 },
{ x: 0.015625, y: 0.140625 },
{ x: 0.015625, y: 0.140625 },
{ x: 0.046875, y: 0.140625 },
{ x: 0.046875, y: 0.140625 },
{ x: 0.078125, y: 0.140625 },
{ x: 0.078125, y: 0.140625 },
{ x: 0.109375, y: 0.140625 },
{ x: 0.109375, y: 0.140625 },
{ x: 0.140625, y: 0.140625 },
{ x: 0.140625, y: 0.140625 },
{ x: 0.171875, y: 0.140625 },
{ x: 0.171875, y: 0.140625 },
{ x: 0.203125, y: 0.140625 },
{ x: 0.203125, y: 0.140625 },
{ x: 0.234375, y: 0.140625 },
{ x: 0.234375, y: 0.140625 },
{ x: 0.265625, y: 0.140625 },
{ x: 0.265625, y: 0.140625 },
{ x: 0.296875, y: 0.140625 },
{ x: 0.296875, y: 0.140625 },
{ x: 0.328125, y: 0.140625 },
{ x: 0.328125, y: 0.140625 },
{ x: 0.359375, y: 0.140625 },
{ x: 0.359375, y: 0.140625 },
{ x: 0.390625, y: 0.140625 },
{ x: 0.390625, y: 0.140625 },
{ x: 0.421875, y: 0.140625 },
{ x: 0.421875, y: 0.140625 },
{ x: 0.453125, y: 0.140625 },
{ x: 0.453125, y: 0.140625 },
{ x: 0.484375, y: 0.140625 },
{ x: 0.484375, y: 0.140625 },
{ x: 0.515625, y: 0.140625 },
{ x: 0.515625, y: 0.140625 },
{ x: 0.546875, y: 0.140625 },
{ x: 0.546875, y: 0.140625 },
{ x: 0.578125, y: 0.140625 },
{ x: 0.578125, y: 0.140625 },
{ x: 0.609375, y: 0.140625 },
{ x: 0.609375, y: 0.140625 },
{ x: 0.640625, y: 0.140625 },
{ x: 0.640625, y: 0.140625 },
{ x: 0.671875, y: 0.140625 },
{ x: 0.671875, y: 0.140625 },
{ x: 0.703125, y: 0.140625 },
{ x: 0.703125, y: 0.140625 },
{ x: 0.734375, y: 0.140625 },
{ x: 0.734375, y: 0.140625 },
{ x: 0.765625, y: 0.140625 },
{ x: 0.765625, y: 0.140625 },
{ x: 0.796875, y: 0.140625 },
{ x: 0.796875, y: 0.140625 },
{ x: 0.828125, y: 0.140625 },
{ x: 0.828125, y: 0.140625 },
{ x: 0.859375, y: 0.140625 },
{ x: 0.859375, y: 0.140625 },
{ x: 0.890625, y: 0.140625 },
{ x: 0.890625, y: 0.140625 },
{ x: 0.921875, y: 0.140625 },
{ x: 0.921875, y: 0.140625 },
{ x: 0.953125, y: 0.140625 },
{ x: 0.953125, y: 0.140625 },
{ x: 0.984375, y: 0.140625 },
{ x: 0.984375, y: 0.140625 },
{ x: 0.015625, y: 0.171875 },
{ x: 0.015625, y: 0.171875 },
{ x: 0.046875, y: 0.171875 },
{ x: 0.046875, y: 0.171875 },
{ x: 0.078125, y: 0.171875 },
{ x: 0.078125, y: 0.171875 },
{ x: 0.109375, y: 0.171875 },
{ x: 0.109375, y: 0.171875 },
{ x: 0.140625, y: 0.171875 },
{ x: 0.140625, y: 0.171875 },
{ x: 0.171875, y: 0.171875 },
{ x: 0.171875, y: 0.171875 },
{ x: 0.203125, y: 0.171875 },
{ x: 0.203125, y: 0.171875 },
{ x: 0.234375, y: 0.171875 },
{ x: 0.234375, y: 0.171875 },
{ x: 0.265625, y: 0.171875 },
{ x: 0.265625, y: 0.171875 },
{ x: 0.296875, y: 0.171875 },
{ x: 0.296875, y: 0.171875 },
{ x: 0.328125, y: 0.171875 },
{ x: 0.328125, y: 0.171875 },
{ x: 0.359375, y: 0.171875 },
{ x: 0.359375, y: 0.171875 },
{ x: 0.390625, y: 0.171875 },
{ x: 0.390625, y: 0.171875 },
{ x: 0.421875, y: 0.171875 },
{ x: 0.421875, y: 0.171875 },
{ x: 0.453125, y: 0.171875 },
{ x: 0.453125, y: 0.171875 },
{ x: 0.484375, y: 0.171875 },
{ x: 0.484375, y: 0.171875 },
{ x: 0.515625, y: 0.171875 },
{ x: 0.515625, y: 0.171875 },
{ x: 0.546875, y: 0.171875 },
{ x: 0.546875, y: 0.171875 },
{ x: 0.578125, y: 0.171875 },
{ x: 0.578125, y: 0.171875 },
{ x: 0.609375, y: 0.171875 },
{ x: 0.609375, y: 0.171875 },
{ x: 0.640625, y: 0.171875 },
{ x: 0.640625, y: 0.171875 },
{ x: 0.671875, y: 0.171875 },
{ x: 0.671875, y: 0.171875 },
{ x: 0.703125, y: 0.171875 },
{ x: 0.703125, y: 0.171875 },
{ x: 0.734375, y: 0.171875 },
{ x: 0.734375, y: 0.171875 },
{ x: 0.765625, y: 0.171875 },
{ x: 0.765625, y: 0.171875 },
{ x: 0.796875, y: 0.171875 },
{ x: 0.796875, y: 0.171875 },
{ x: 0.828125, y: 0.171875 },
{ x: 0.828125, y: 0.171875 },
{ x: 0.859375, y: 0.171875 },
{ x: 0.859375, y: 0.171875 },
{ x: 0.890625, y: 0.171875 },
{ x: 0.890625, y: 0.171875 },
{ x: 0.921875, y: 0.171875 },
{ x: 0.921875, y: 0.171875 },
{ x: 0.953125, y: 0.171875 },
{ x: 0.953125, y: 0.171875 },
{ x: 0.984375, y: 0.171875 },
{ x: 0.984375, y: 0.171875 },
{ x: 0.015625, y: 0.203125 },
{ x: 0.015625, y: 0.203125 },
{ x: 0.046875, y: 0.203125 },
{ x: 0.046875, y: 0.203125 },
{ x: 0.078125, y: 0.203125 },
{ x: 0.078125, y: 0.203125 },
{ x: 0.109375, y: 0.203125 },
{ x: 0.109375, y: 0.203125 },
{ x: 0.140625, y: 0.203125 },
{ x: 0.140625, y: 0.203125 },
{ x: 0.171875, y: 0.203125 },
{ x: 0.171875, y: 0.203125 },
{ x: 0.203125, y: 0.203125 },
{ x: 0.203125, y: 0.203125 },
{ x: 0.234375, y: 0.203125 },
{ x: 0.234375, y: 0.203125 },
{ x: 0.265625, y: 0.203125 },
{ x: 0.265625, y: 0.203125 },
{ x: 0.296875, y: 0.203125 },
{ x: 0.296875, y: 0.203125 },
{ x: 0.328125, y: 0.203125 },
{ x: 0.328125, y: 0.203125 },
{ x: 0.359375, y: 0.203125 },
{ x: 0.359375, y: 0.203125 },
{ x: 0.390625, y: 0.203125 },
{ x: 0.390625, y: 0.203125 },
{ x: 0.421875, y: 0.203125 },
{ x: 0.421875, y: 0.203125 },
{ x: 0.453125, y: 0.203125 },
{ x: 0.453125, y: 0.203125 },
{ x: 0.484375, y: 0.203125 },
{ x: 0.484375, y: 0.203125 },
{ x: 0.515625, y: 0.203125 },
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{ x: 0.8125, y: 0.3125 },
{ x: 0.8125, y: 0.3125 },
{ x: 0.8125, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.9375, y: 0.3125 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.0625, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.1875, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.3125, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.4375, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.5625, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.6875, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.8125, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.9375, y: 0.4375 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.0625, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.1875, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.3125, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.4375, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.5625, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.6875, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.8125, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.9375, y: 0.5625 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.0625, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.1875, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.3125, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.4375, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.5625, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.6875, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.8125, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.9375, y: 0.6875 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.0625, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.1875, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.3125, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.4375, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.5625, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.6875, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.8125, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.9375, y: 0.8125 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.0625, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.1875, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.3125, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ 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 = mu(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 t8 = {};
t8.boxOffsets = Xe(boxes, [0, 0], [-1, 2]);
t8.boxSizes = Xe(boxes, [0, 2], [-1, 2]);
t8.div = je(t8.boxOffsets, this.inputSizeTensor);
t8.boxCenterPoints = Ce(t8.div, this.anchorsTensor);
t8.halfBoxSizes = je(t8.boxSizes, this.doubleInputSizeTensor);
t8.sub = Te(t8.boxCenterPoints, t8.halfBoxSizes);
t8.startPoints = se(t8.sub, this.inputSizeTensor);
t8.add = Ce(t8.boxCenterPoints, t8.halfBoxSizes);
t8.endPoints = se(t8.add, this.inputSizeTensor);
const res = Wk([t8.startPoints, t8.endPoints], 1);
Object.keys(t8).forEach((tensor) => Ot(t8[tensor]));
return res;
}
normalizeLandmarks(rawPalmLandmarks, index2) {
const t8 = {};
t8.reshape = W(rawPalmLandmarks, [-1, 7, 2]);
t8.div = je(t8.reshape, this.inputSizeTensor);
t8.landmarks = Ce(t8.div, this.anchors[index2] ? this.anchors[index2] : 0);
const res = se(t8.landmarks, this.inputSizeTensor);
Object.keys(t8).forEach((tensor) => Ot(t8[tensor]));
return res;
}
async predict(input, config3) {
var _a2;
const t8 = {};
t8.resize = Fj.resizeBilinear(input, [this.inputSize, this.inputSize]);
t8.div = je(t8.resize, constants.tf127);
t8.image = Te(t8.div, constants.tf1);
t8.batched = this.model.execute(t8.image);
t8.predictions = mc(t8.batched);
t8.slice = Xe(t8.predictions, [0, 0], [-1, 1]);
t8.sigmoid = Na(t8.slice);
t8.scores = mc(t8.sigmoid);
const scores = await t8.scores.data();
t8.boxes = Xe(t8.predictions, [0, 1], [-1, 4]);
t8.norm = this.normalizeBoxes(t8.boxes);
t8.nms = await Fj.nonMaxSuppressionAsync(t8.norm, t8.scores, 3 * (((_a2 = config3.hand) == null ? void 0 : _a2.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence);
const nms = await t8.nms.array();
const hands = [];
for (const index2 of nms) {
const p = {};
p.box = Xe(t8.norm, [index2, 0], [1, -1]);
p.slice = Xe(t8.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(t8).forEach((tensor) => Ot(t8[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) {
this.skipped++;
} else {
boxes = await this.handDetector.predict(image, config3);
this.skipped = 0;
}
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") ? Fj.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 = je(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;
function initPipeline() {
const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0;
if (handDetector && handPoseModel)
handPipeline = new HandPipeline(handDetector, handPoseModel);
}
async function predict14(input, config3) {
if (!handPipeline)
initPipeline();
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 loadDetect2(config3) {
var _a2;
if (env.initial)
handDetectorModel = null;
if (!handDetectorModel)
handDetectorModel = await loadModel((_a2 = config3.hand.detector) == null ? void 0 : _a2.modelPath);
else if (config3.debug)
log("cached model:", handDetectorModel["modelUrl"]);
return handDetectorModel;
}
async function loadSkeleton(config3) {
var _a2;
if (env.initial)
handPoseModel = null;
if (!handPoseModel)
handPoseModel = await loadModel((_a2 = config3.hand.skeleton) == null ? void 0 : _a2.modelPath);
else if (config3.debug)
log("cached model:", handPoseModel["modelUrl"]);
return 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 loadDetect3(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 loadSkeleton2(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 t8 = {};
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;
t8.resize = Fj.resizeBilinear(input, [height, width]);
t8.cast = qe(t8.resize, "int32");
[t8.rawScores, t8.rawBoxes] = await models2[0].executeAsync(t8.cast, modelOutputNodes);
t8.boxes = mc(t8.rawBoxes, [0, 2]);
t8.scores = mc(t8.rawScores, [0]);
const classScores = mo(t8.scores, 1);
Ot(classScores[faceIndex]);
classScores.splice(faceIndex, 1);
t8.filtered = kr(classScores, 1);
Ot(classScores);
t8.max = _a(t8.filtered, 1);
t8.argmax = Sk(t8.filtered, 1);
let id2 = 0;
t8.nms = await Fj.nonMaxSuppressionAsync(t8.boxes, t8.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
const nms = await t8.nms.data();
const scores = await t8.max.data();
const classNum = await t8.argmax.data();
for (const nmsIndex of Array.from(nms)) {
const boxSlice = Xe(t8.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(t8).forEach((tensor) => Ot(t8[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 t8 = {};
const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
t8.crop = Fj.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
t8.div = je(t8.crop, constants.tf255);
[t8.score, t8.keypoints] = models2[1].execute(t8.div, ["Identity_1", "Identity"]);
const rawScore = (await t8.score.data())[0];
const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
if (score >= (config3.hand.minConfidence || 0)) {
hand3.fingerScore = score;
t8.reshaped = W(t8.keypoints, [-1, 3]);
const coordsData = await t8.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(t8).forEach((tensor) => Ot(t8[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, _u, _v2, _w2, _x2, _y, _z2;
const t02 = 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 = (_u = newResult.face[i].rotation) == null ? void 0 : _u.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) * (((_w2 = bufferedResult.face[i].rotation) == null ? void 0 : _w2.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 - t02) : Math.round(t12 - t02);
if (newResult.performance)
bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };
return bufferedResult;
}
// src/segmentation/meet.ts
var model17;
async function load15(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 load15(config3);
if (!(model17 == null ? void 0 : model17["executor"]) || !((_a2 = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a2[0].shape))
return null;
const t8 = {};
t8.resize = Fj.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false);
t8.norm = je(t8.resize, constants.tf255);
t8.res = model17.execute(t8.norm);
t8.squeeze = mc(t8.res, [0]);
[t8.bgRaw, t8.fgRaw] = mo(t8.squeeze, 2);
t8.fg = _1(t8.fgRaw);
t8.mul = se(t8.fg, constants.tf255);
t8.expand = ai(t8.mul, 2);
t8.output = Fj.resizeBilinear(t8.expand, [input.shape[1] || 0, input.shape[2] || 0]);
let rgba;
switch (config3.segmentation.mode || "default") {
case "default":
t8.input = mc(input);
t8.concat = bt([t8.input, t8.output], -1);
rgba = qe(t8.concat, "int32");
break;
case "alpha":
rgba = qe(t8.output, "int32");
break;
default:
rgba = ir(0);
}
Object.keys(t8).forEach((tensor) => Ot(t8[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 t8 = {};
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
];
t8.pad = Ea(input, cache5.padding);
t8.resize = Fj.resizeBilinear(t8.pad, [inputSize10, inputSize10]);
const final = qe(t8.resize, "int32");
Object.keys(t8).forEach((tensor) => Ot(t8[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 load16(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 (A().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
A().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 t8 = {};
skipped14 = 0;
t8.input = padInput(input, inputSize8);
t8.res = model18 == null ? void 0 : model18.execute(t8.input);
cache6.last = now();
const res = await t8.res.array();
cache6.bodies = t8.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(t8).forEach((tensor) => Ot(t8[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 load17(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 = mc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length));
const scores = await scoresT.array();
const featuresT = mc(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 = Sk(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, y10] = [
cx2 - scaleBox / strideSize * boxOffset[0],
cy2 - scaleBox / strideSize * boxOffset[1]
];
const [w10, h] = [
cx2 + scaleBox / strideSize * boxOffset[2] - x,
cy2 + scaleBox / strideSize * boxOffset[3] - y10
];
let boxRaw = [x, y10, w10, 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 Fj.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 = Fj.resizeBilinear(image, [inputSize9, inputSize9], false);
const normT = je(resizeT, constants.tf255);
const transposeT = fc(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: y10 } }) => ({
maxX: Math.max(maxX, x),
maxY: Math.max(maxY, y10),
minX: Math.min(minX, x),
minY: Math.min(minY, y10)
}), {
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 t8 = this.priorityQueue[i];
this.priorityQueue[i] = this.priorityQueue[j];
this.priorityQueue[j] = t8;
}
};
function getOffsetPoint(y10, x, keypoint, offsets) {
return {
y: offsets.get(y10, x, keypoint),
x: offsets.get(y10, x, keypoint + count)
};
}
function getImageCoords(part, outputStride2, offsets) {
const { heatmapY, heatmapX, id: keypoint } = part;
const { y: y10, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
return {
x: part.heatmapX * outputStride2 + x,
y: part.heatmapY * outputStride2 + y10
};
}
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: y10 }, keypointId) {
return poses.some(({ keypoints }) => {
var _a2;
const correspondingKeypoint = (_a2 = keypoints[keypointId]) == null ? void 0 : _a2.position;
if (!correspondingKeypoint)
return false;
return squaredDistance(y10, 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 = Fj.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]);
const normalized = Te(je(qe(resized, "float32"), 127.5), 1);
const results = model20.execute(normalized, poseNetOutputs);
const results3d = results.map((y10) => mc(y10, [0]));
results3d[1] = Na(results3d[1]);
return results3d;
});
const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));
for (const t8 of res)
Ot(t8);
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 load18(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 load19(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 = mc(r, [0]);
const mul = se(squeeze, constants.tf255);
const cast = qe(mul, "int32");
return cast;
});
function getRGBA(fgr, pha) {
const rgb3 = fgr ? normalize(fgr) : Ta([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
const a = pha ? normalize(pha) : Ta([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
const rgba = bt([rgb3, a], -1);
Ot([rgb3, a]);
return rgba;
}
function getState(state) {
return De(() => {
const r = {};
r.unstack = mo(state, -1);
r.concat = bt(r.unstack, 1);
r.split = pi(r.concat, 4, 1);
r.stack = bt(r.split, 2);
r.squeeze = mc(r.stack, [0]);
r.expand = ai(r.squeeze, -1);
r.add = Ce(r.expand, 1);
r.mul = se(r.add, 127.5);
r.cast = qe(r.mul, "int32");
r.tile = uu(r.cast, [1, 1, 3]);
r.alpha = Ta([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
return bt([r.tile, r.alpha], -1);
});
}
async function predict20(input, config3) {
if (!model21)
model21 = await load19(config3);
if (!(model21 == null ? void 0 : model21["executor"]))
return null;
t.src = je(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 load20(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 load20(config3);
if (!(model22 == null ? void 0 : model22["executor"]) || !((_a2 = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a2[0].shape))
return null;
const t8 = {};
t8.resize = Fj.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false);
t8.norm = je(t8.resize, constants.tf255);
t8.res = model22.execute(t8.norm);
t8.squeeze = mc(t8.res, [0]);
t8.alpha = Fj.resizeBilinear(t8.squeeze, [input.shape[1] || 0, input.shape[2] || 0]);
t8.mul = se(t8.alpha, constants.tf255);
let rgba;
switch (config3.segmentation.mode || "default") {
case "default":
t8.input = mc(input);
t8.concat = bt([t8.input, t8.mul], -1);
rgba = qe(t8.concat, "int32");
break;
case "alpha":
rgba = qe(t8.mul, "int32");
break;
default:
rgba = ir(0);
}
Object.keys(t8).forEach((tensor) => Ot(t8[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, _u, _v2, _w2, _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")) ? load16(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")) ? load18(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")) ? loadDetect3(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")) ? loadSkeleton2(this.instance.config) : null;
if (this.instance.config.hand.enabled && !this.models.handdetect && ((_v2 = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v2.includes("handdetect"))) {
m.handdetect = loadDetect2(this.instance.config);
m.handskeleton = loadSkeleton(this.instance.config);
}
m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w2 = this.instance.config.object.modelPath) == null ? void 0 : _w2.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")) ? load17(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")) ? load20(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")) ? load15(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")) ? load19(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 y10 = [];
const extractXY = (box) => {
if (box && box.length === 4) {
x.push(box[0], box[0] + box[2]);
y10.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(...y10);
person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y10) - 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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// 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 && pde() === "tensorflow") {
const data = (void 0).decodeJpeg(img);
const expanded = ai(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 (!A().flagRegistry.ENGINE_COMPILE_ONLY)
return;
const backendType = pde();
const webGLBackend = dde();
if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) {
return;
}
A().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 = Gr(shape, dtype);
try {
const res = model23.execute(tensor);
compiledModels.push(modelName);
if (Array.isArray(res))
res.forEach((t8) => Ot(t8));
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 });
A().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 t02 = 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 - t02), "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 ut))
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 = (Sce.tfjs || fX).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 ude();
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 t02 = now();
const res = await warmup(this, userConfig);
const t12 = now();
this.performance.warmup = Math.trunc(t12 - t02);
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, _u;
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 = ((_u = this.process.tensor) == null ? void 0 : _u.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