lint

Author: ev-br

torch_np/_binary_ufuncs.py

@@ -1,12 +1,15 @@
 from ._detail import _binary_ufuncs
 
-__all__ = [name for name in dir(_binary_ufuncs) if not name.startswith("_") and name != "torch"]
+__all__ = [
+    name for name in dir(_binary_ufuncs) if not name.startswith("_") and name != "torch"
+]
 
 
-# TODO: consolidate normalizations
-from ._funcs import normalizer, ArrayLike, SubokLike, DTypeLike
-from ._detail import _util
 from . import _helpers
+from ._detail import _util
+
+# TODO: consolidate normalizations
+from ._funcs import ArrayLike, DTypeLike, SubokLike, normalizer
 
 
 def deco_binary_ufunc(torch_func):
@@ -15,19 +18,20 @@ def deco_binary_ufunc(torch_func):
     Normalize arguments, sort out type casting, broadcasting and delegate to
     the pytorch functions for the actual work.
     """
+
     def wrapped(
-        x1 : ArrayLike,
-        x2 : ArrayLike,
+        x1: ArrayLike,
+        x2: ArrayLike,
         /,
         out=None,
         *,
         where=True,
         casting="same_kind",
         order="K",
-        dtype: DTypeLike=None,
-        subok: SubokLike=False,
+        dtype: DTypeLike = None,
+        subok: SubokLike = False,
         signature=None,
-        extobj=None
+        extobj=None,
     ):
         if order != "K" or not where or signature or extobj:
             raise NotImplementedError
@@ -47,6 +51,7 @@ def wrapped(
 
     return wrapped
 
+
 #
 # For each torch ufunc implementation, decorate and attach the decorated name
 # to this module. Its contents is then exported to the public namespace in __init__.py
@@ -55,6 +60,6 @@ def wrapped(
     ufunc = getattr(_binary_ufuncs, name)
     decorated = normalizer(deco_binary_ufunc(ufunc))
 
-    decorated.__qualname__ = name    # XXX: is this really correct?
+    decorated.__qualname__ = name  # XXX: is this really correct?
     decorated.__name__ = name
     vars()[name] = decorated

torch_np/_decorators.py

@@ -1,5 +1,4 @@
 import functools
-import operator
 
 import torch
 
@@ -9,7 +8,6 @@
 NoValue = None
 
 
-
 def out_shape_dtype(func):
     """Handle out=... kwarg for ufuncs.
 
@@ -26,5 +24,3 @@ def wrapped(*args, out=None, **kwds):
         return _helpers.result_or_out(result_tensor, out)
 
     return wrapped
-
-

torch_np/_detail/_binary_ufuncs.py

@@ -4,19 +4,43 @@
 
 import torch
 
-from . import _dtypes_impl, _util
-
-
-from torch import (add, arctan2, bitwise_and, bitwise_or, bitwise_xor, copysign, divide, 
-    float_power, floor_divide, fmax, fmin, fmod, gcd, greater, greater_equal, heaviside,
-    hypot, lcm, ldexp, less, less_equal, logaddexp, logaddexp2, logical_and,
-    logical_or, logical_xor, maximum, minimum, remainder, multiply, nextafter, not_equal,
-    remainder, subtract, divide)
-
-
 # renames
-from torch import (eq as equal, pow as power, bitwise_right_shift as right_shift,
-        bitwise_left_shift as left_shift,)
+from torch import add, arctan2, bitwise_and
+from torch import bitwise_left_shift as left_shift
+from torch import bitwise_or
+from torch import bitwise_right_shift as right_shift
+from torch import bitwise_xor, copysign, divide
+from torch import eq as equal
+from torch import (
+    float_power,
+    floor_divide,
+    fmax,
+    fmin,
+    fmod,
+    gcd,
+    greater,
+    greater_equal,
+    heaviside,
+    hypot,
+    lcm,
+    ldexp,
+    less,
+    less_equal,
+    logaddexp,
+    logaddexp2,
+    logical_and,
+    logical_or,
+    logical_xor,
+    maximum,
+    minimum,
+    multiply,
+    nextafter,
+    not_equal,
+)
+from torch import pow as power
+from torch import remainder, subtract
+
+from . import _dtypes_impl, _util
 
 
 # work around torch limitations w.r.t. numpy
@@ -27,4 +51,3 @@ def matmul(x, y):
     y = _util.cast_if_needed(y, dtype)
     result = torch.matmul(x, y)
     return result
-

torch_np/_detail/_reductions.py

@@ -13,13 +13,13 @@
 
 import functools
 
-
 ############# XXX
 ### From _util.axis_expand_func
 
 
 def deco_axis_expand(func):
     """Generically handle axis arguments in reductions."""
+
     @functools.wraps(func)
     def wrapped(tensor, axis, *args, **kwds):
 
@@ -46,11 +46,13 @@ def wrapped(tensor, axis=None, keepdims=NoValue, *args, **kwds):
         if keepdims:
             result = _util.apply_keepdims(result, axis, tensor.ndim)
         return result
+
     return wrapped
 
 
 def deco_axis_ravel(func):
     """Generically handle 'axis=None ravels' behavior."""
+
     @functools.wraps(func)
     def wrapped(tensor, axis, *args, **kwds):
         if axis is not None:
@@ -61,6 +63,7 @@ def wrapped(tensor, axis, *args, **kwds):
 
         result = func(tensor, axis=axis, *args, **kwds)
         return result
+
     return wrapped
 
 
@@ -292,7 +295,6 @@ def cumsum(tensor, axis, dtype=None):
     return result
 
 
-
 def average(a, axis, weights, returned=False, keepdims=False):
     if weights is None:
         result, wsum = average_noweights(a, axis, keepdims=keepdims)
@@ -384,7 +386,6 @@ def quantile(a_tensor, q_tensor, axis, method, keepdims=False):
 
     q_tensor = _util.cast_if_needed(q_tensor, a_tensor.dtype)
 
-
     # axis=None ravels, so store the originals to reuse with keepdims=True below
     ax, ndim = axis, a_tensor.ndim
     (a_tensor, q_tensor), axis = _util.axis_none_ravel(a_tensor, q_tensor, axis=axis)

torch_np/_detail/_unary_ufuncs.py

@@ -4,15 +4,36 @@
 
 import torch
 
-from torch import (arccos, arccosh, arcsin, arcsinh, arctan, arctanh, ceil,
-    cos, cosh, deg2rad, exp, exp2, expm1,
-    floor, isfinite, isinf, isnan, log, log10, log1p, log2, logical_not,
-    negative, rad2deg, reciprocal, sign, signbit,
-    sin, sinh, sqrt, square, tan, tanh, trunc)
-
 # renames
-from torch import (conj_physical as conjugate, round as rint, bitwise_not as invert, rad2deg as degrees,
-    deg2rad as radians, absolute as fabs, )
+from torch import absolute as fabs
+from torch import arccos, arccosh, arcsin, arcsinh, arctan, arctanh
+from torch import bitwise_not as invert
+from torch import ceil
+from torch import conj_physical as conjugate
+from torch import cos, cosh
+from torch import deg2rad
+from torch import deg2rad as radians
+from torch import (
+    exp,
+    exp2,
+    expm1,
+    floor,
+    isfinite,
+    isinf,
+    isnan,
+    log,
+    log1p,
+    log2,
+    log10,
+    logical_not,
+    negative,
+)
+from torch import rad2deg
+from torch import rad2deg as degrees
+from torch import reciprocal
+from torch import round as rint
+from torch import sign, signbit, sin, sinh, sqrt, square, tan, tanh, trunc
+
 
 # special cases: torch does not export these names
 def cbrt(x):
@@ -32,4 +53,3 @@ def absolute(x):
 
 abs = absolute
 conj = conjugate
-