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# flake8: noqa
import torch
import math
a = torch.randn(4)
b = torch.randn(4)
t = torch.tensor([-1, -2, 3], dtype=torch.int8)
# abs/absolute
torch.abs(torch.tensor([-1, -2, 3]))
torch.absolute(torch.tensor([-1, -2, 3]))
# acos/arccos
torch.acos(a)
torch.arccos(a)
# acosh/arccosh
torch.acosh(a.uniform_(1, 2))
# add
torch.add(a, 20)
torch.add(a, torch.randn(4, 1), alpha=10)
# addcdiv
torch.addcdiv(torch.randn(1, 3), torch.randn(3, 1), torch.randn(1, 3), value=0.1)
# addcmul
torch.addcmul(torch.randn(1, 3), torch.randn(3, 1), torch.randn(1, 3), value=0.1)
# angle
torch.angle(torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]))*180/3.14159
# asin/arcsin
torch.asin(a)
torch.arcsin(a)
# asinh/arcsinh
torch.asinh(a)
torch.arcsinh(a)
# atan/arctan
torch.atan(a)
torch.arctan(a)
# atanh/arctanh
torch.atanh(a.uniform_(-1, 1))
torch.arctanh(a.uniform_(-1, 1))
# atan2
torch.atan2(a, a)
# bitwise_not
torch.bitwise_not(t)
# bitwise_and
torch.bitwise_and(t, torch.tensor([1, 0, 3], dtype=torch.int8))
torch.bitwise_and(torch.tensor([True, True, False]), torch.tensor([False, True, False]))
# bitwise_or
torch.bitwise_or(t, torch.tensor([1, 0, 3], dtype=torch.int8))
torch.bitwise_or(torch.tensor([True, True, False]), torch.tensor([False, True, False]))
# bitwise_xor
torch.bitwise_xor(t, torch.tensor([1, 0, 3], dtype=torch.int8))
# ceil
torch.ceil(a)
# clamp/clip
torch.clamp(a, min=-0.5, max=0.5)
torch.clamp(a, min=0.5)
torch.clamp(a, max=0.5)
torch.clip(a, min=-0.5, max=0.5)
# conj
torch.conj(torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]))
# copysign
torch.copysign(a, 1)
torch.copysign(a, b)
# cos
torch.cos(a)
# cosh
torch.cosh(a)
# deg2rad
torch.deg2rad(torch.tensor([[180.0, -180.0], [360.0, -360.0], [90.0, -90.0]]))
# div/divide/true_divide
x = torch.tensor([ 0.3810, 1.2774, -0.2972, -0.3719, 0.4637])
torch.div(x, 0.5)
p = torch.tensor([[-0.3711, -1.9353, -0.4605, -0.2917],
[ 0.1815, -1.0111, 0.9805, -1.5923],
[ 0.1062, 1.4581, 0.7759, -1.2344],
[-0.1830, -0.0313, 1.1908, -1.4757]])
q = torch.tensor([ 0.8032, 0.2930, -0.8113, -0.2308])
torch.div(p, q)
torch.divide(p, q, rounding_mode='trunc')
torch.divide(p, q, rounding_mode='floor')
# digamma
torch.digamma(torch.tensor([1, 0.5]))
# erf
torch.erf(torch.tensor([0, -1., 10.]))
# erfc
torch.erfc(torch.tensor([0, -1., 10.]))
# erfinv
torch.erfinv(torch.tensor([0, 0.5, -1.]))
# exp
torch.exp(torch.tensor([0, math.log(2.)]))
# exp2
torch.exp2(torch.tensor([0, math.log2(2.), 3, 4]))
# expm1
torch.expm1(torch.tensor([0, math.log(2.)]))
# fake_quantize_per_channel_affine
x = torch.randn(2, 2, 2)
scales = (torch.randn(2) + 1) * 0.05
zero_points = torch.zeros(2).to(torch.long)
torch.fake_quantize_per_channel_affine(x, scales, zero_points, 1, 0, 255)
# fake_quantize_per_tensor_affine
torch.fake_quantize_per_tensor_affine(a, 0.1, 0, 0, 255)
# float_power
torch.float_power(torch.randint(10, (4,)), 2)
torch.float_power(torch.arange(1, 5), torch.tensor([2, -3, 4, -5]))
# floor
torch.floor(a)
# floor_divide
torch.floor_divide(torch.tensor([4., 3.]), torch.tensor([2., 2.]))
torch.floor_divide(torch.tensor([4., 3.]), 1.4)
# fmod
torch.fmod(torch.tensor([-3., -2, -1, 1, 2, 3]), 2)
torch.fmod(torch.tensor([1, 2, 3, 4, 5]), 1.5)
# frac
torch.frac(torch.tensor([1, 2.5, -3.2]))
# imag
torch.randn(4, dtype=torch.cfloat).imag
# ldexp
torch.ldexp(torch.tensor([1.]), torch.tensor([1]))
torch.ldexp(torch.tensor([1.0]), torch.tensor([1, 2, 3, 4]))
# lerp
start = torch.arange(1., 5.)
end = torch.empty(4).fill_(10)
torch.lerp(start, end, 0.5)
torch.lerp(start, end, torch.full_like(start, 0.5))
# lgamma
torch.lgamma(torch.arange(0.5, 2, 0.5))
# log
torch.log(torch.arange(5) + 10)
# log10
torch.log10(torch.rand(5))
# log1p
torch.log1p(torch.randn(5))
# log2
torch.log2(torch.rand(5))
# logaddexp
torch.logaddexp(torch.tensor([-1.0]), torch.tensor([-1.0, -2, -3]))
torch.logaddexp(torch.tensor([-100.0, -200, -300]), torch.tensor([-1.0, -2, -3]))
torch.logaddexp(torch.tensor([1.0, 2000, 30000]), torch.tensor([-1.0, -2, -3]))
# logaddexp2
torch.logaddexp2(torch.tensor([-1.0]), torch.tensor([-1.0, -2, -3]))
torch.logaddexp2(torch.tensor([-100.0, -200, -300]), torch.tensor([-1.0, -2, -3]))
torch.logaddexp2(torch.tensor([1.0, 2000, 30000]), torch.tensor([-1.0, -2, -3]))
# logical_and
torch.logical_and(torch.tensor([True, False, True]), torch.tensor([True, False, False]))
r = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
s = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
torch.logical_and(r, s)
torch.logical_and(r.double(), s.double())
torch.logical_and(r.double(), s)
torch.logical_and(r, s, out=torch.empty(4, dtype=torch.bool))
# logical_not
torch.logical_not(torch.tensor([True, False]))
torch.logical_not(torch.tensor([0, 1, -10], dtype=torch.int8))
torch.logical_not(torch.tensor([0., 1.5, -10.], dtype=torch.double))
torch.logical_not(torch.tensor([0., 1., -10.], dtype=torch.double), out=torch.empty(3, dtype=torch.int16))
# logical_or
torch.logical_or(torch.tensor([True, False, True]), torch.tensor([True, False, False]))
torch.logical_or(r, s)
torch.logical_or(r.double(), s.double())
torch.logical_or(r.double(), s)
torch.logical_or(r, s, out=torch.empty(4, dtype=torch.bool))
# logical_xor
torch.logical_xor(torch.tensor([True, False, True]), torch.tensor([True, False, False]))
torch.logical_xor(r, s)
torch.logical_xor(r.double(), s.double())
torch.logical_xor(r.double(), s)
torch.logical_xor(r, s, out=torch.empty(4, dtype=torch.bool))
# logit
torch.logit(torch.rand(5), eps=1e-6)
# hypot
torch.hypot(torch.tensor([4.0]), torch.tensor([3.0, 4.0, 5.0]))
# i0
torch.i0(torch.arange(5, dtype=torch.float32))
# igamma/igammac
a1 = torch.tensor([4.0])
a2 = torch.tensor([3.0, 4.0, 5.0])
torch.igamma(a1, a2)
torch.igammac(a1, a2)
# mul/multiply
torch.mul(torch.randn(3), 100)
torch.multiply(torch.randn(4, 1), torch.randn(1, 4))
# mvlgamma
torch.mvlgamma(torch.empty(2, 3).uniform_(1, 2), 2)
# nan_to_num
w = torch.tensor([float('nan'), float('inf'), -float('inf'), 3.14])
torch.nan_to_num(x)
torch.nan_to_num(x, nan=2.0)
torch.nan_to_num(x, nan=2.0, posinf=1.0)
# neg/negative
torch.neg(torch.randn(5))
# nextafter
eps = torch.finfo(torch.float32).eps
torch.nextafter(torch.tensor([1, 2]), torch.tensor([2, 1])) == torch.tensor([eps + 1, 2 - eps])
# polygamma
torch.polygamma(1, torch.tensor([1, 0.5]))
torch.polygamma(2, torch.tensor([1, 0.5]))
torch.polygamma(3, torch.tensor([1, 0.5]))
torch.polygamma(4, torch.tensor([1, 0.5]))
# pow
torch.pow(a, 2)
torch.pow(torch.arange(1., 5.), torch.arange(1., 5.))
# rad2deg
torch.rad2deg(torch.tensor([[3.142, -3.142], [6.283, -6.283], [1.570, -1.570]]))
# real
torch.randn(4, dtype=torch.cfloat).real
# reciprocal
torch.reciprocal(a)
# remainder
torch.remainder(torch.tensor([-3., -2, -1, 1, 2, 3]), 2)
torch.remainder(torch.tensor([1, 2, 3, 4, 5]), 1.5)
# round
torch.round(a)
# rsqrt
torch.rsqrt(a)
# sigmoid
torch.sigmoid(a)
# sign
torch.sign(torch.tensor([0.7, -1.2, 0., 2.3]))
# sgn
torch.tensor([3+4j, 7-24j, 0, 1+2j]).sgn()
# signbit
torch.signbit(torch.tensor([0.7, -1.2, 0., 2.3]))
# sin
torch.sin(a)
# sinc
torch.sinc(a)
# sinh
torch.sinh(a)
# sqrt
torch.sqrt(a)
# square
torch.square(a)
# sub/subtract
torch.sub(torch.tensor((1, 2)), torch.tensor((0, 1)), alpha=2)
# tan
torch.tan(a)
# tanh
torch.tanh(a)
# trunc/fix
torch.trunc(a)
# xlogy
f = torch.zeros(5,)
g = torch.tensor([-1, 0, 1, float('inf'), float('nan')])
torch.xlogy(f, g)
f = torch.tensor([1, 2, 3])
g = torch.tensor([3, 2, 1])
torch.xlogy(f, g)
torch.xlogy(f, 4)
torch.xlogy(2, g)
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