Run pre-commit on all files

Author: honno

autogen/_binary_ufuncs.py

@@ -1,9 +1,8 @@
 # this file is autogenerated via gen_ufuncs.py
 # do not edit manually!
 
-import torch
-
 import _util
+import torch
 from _ndarray import asarray_replacer
 
 

autogen/_unary_ufuncs.py

@@ -1,9 +1,8 @@
 # this file is autogenerated via gen_ufuncs.py
 # do not edit manually!
 
-import torch
-
 import _util
+import torch
 from _ndarray import asarray_replacer
 
 

autogen/dump_namespace.py

@@ -1,5 +1,5 @@
-from collections import defaultdict
 import inspect
+from collections import defaultdict
 
 import numpy as np
 

autogen/gen_mapping.py

@@ -3,7 +3,6 @@
 # The default is autopopulated, apply manual tweaks on the result, if needed.
 
 import numpy as np
-
 import wrapper
 
 _all = set(name for name in dir(wrapper) if not name.startswith("_"))

autogen/gen_ufuncs.py

@@ -1,6 +1,5 @@
-from dump_namespace import grab_namespace, get_signature
-
 import numpy as np
+from dump_namespace import get_signature, grab_namespace
 
 namespace = np
 

autogen/gen_ufuncs_2.py

@@ -1,6 +1,5 @@
-from dump_namespace import grab_namespace, get_signature
-
 import numpy as np
+from dump_namespace import get_signature, grab_namespace
 
 namespace = np
 

autogen/test_binary_ufuncs.py

@@ -1,15 +1,11 @@
 # this file is autogenerated via gen_ufuncs.py
 # do not edit manually!
 
-import torch
-
 import _util
-from _ndarray import asarray_replacer
-
 import numpy as np
 import torch
-
 from _binary_ufuncs import *
+from _ndarray import asarray_replacer
 from testing import assert_allclose
 
 

autogen/test_unary_ufuncs.py

@@ -1,14 +1,10 @@
 # this file is autogenerated via gen_ufuncs.py
 # do not edit manually!
 
-import torch
-
 import _util
-from _ndarray import asarray_replacer
-
 import numpy as np
 import torch
-
+from _ndarray import asarray_replacer
 from _unary_ufuncs import *
 from testing import assert_allclose
 

torch_np/__init__.py

@@ -1,15 +1,14 @@
+from ._binary_ufuncs import *
 from ._dtypes import *
+from ._getlimits import errstate, finfo, iinfo
+from ._ndarray import can_cast, newaxis, result_type
 from ._scalar_types import *
+from ._unary_ufuncs import *
+from ._util import AxisError, UFuncTypeError
 from ._wrapper import *
 
 # from . import testing
 
-from ._unary_ufuncs import *
-from ._binary_ufuncs import *
-from ._ndarray import can_cast, result_type, newaxis
-from ._util import AxisError, UFuncTypeError
-from ._getlimits import iinfo, finfo
-from ._getlimits import errstate
 
 inf = float("inf")
 nan = float("nan")

torch_np/_binary_ufuncs.py

@@ -1,13 +1,9 @@
 import functools
-import torch
 
-from . import _util
+import torch
 
+from . import _dtypes, _helpers, _ufunc_impl, _util
 from ._ndarray import asarray
-from . import _dtypes
-from . import _helpers
-
-from . import _ufunc_impl
 
 #
 # Functions in _ufunc_impl receive arrays, implement common tasks with ufunc args

torch_np/_dtypes.py

@@ -6,11 +6,11 @@
 #       3. convert from python types: np.ones(3, dtype=float) etc
 
 import builtins
+
 import torch
 
 from . import _scalar_types
 
-
 __all__ = ["dtype_from_torch", "dtype", "typecodes", "issubdtype"]
 
 

torch_np/_getlimits.py

@@ -1,4 +1,5 @@
 import torch
+
 from . import _dtypes
 
 
@@ -14,6 +15,7 @@ def iinfo(dtyp):
 
 import contextlib
 
+
 # FIXME: this is only a stub
 @contextlib.contextmanager
 def errstate(*args, **kwds):

torch_np/_helpers.py

@@ -1,9 +1,9 @@
 import operator
 
 import torch
-from . import _dtypes
-from ._ndarray import can_cast, ndarray, asarray
-from . import _util
+
+from . import _dtypes, _util
+from ._ndarray import asarray, can_cast, ndarray
 
 
 def cast_and_broadcast(arrays, out, casting):

torch_np/_ndarray.py

@@ -2,10 +2,7 @@
 
 import torch
 
-from . import _util
-from . import _helpers
-from . import _dtypes
-from . import _ufunc_impl
+from . import _dtypes, _helpers, _ufunc_impl, _util
 
 NoValue = None
 newaxis = None

torch_np/_scalar_types.py

@@ -2,6 +2,7 @@
 """
 
 import abc
+
 import torch
 
 
@@ -15,8 +16,7 @@ def __new__(self, value):
         #
         # Yes, a call to np.float32(4) produces a zero-dim array.
         #
-        from . import _dtypes
-        from . import _ndarray
+        from . import _dtypes, _ndarray
 
         torch_dtype = _dtypes.torch_dtype_from(self.name)
 

torch_np/_ufunc_impl.py

@@ -1,7 +1,6 @@
 import torch
 
-from . import _util
-from . import _helpers
+from . import _helpers, _util
 
 
 def deco_binary_ufunc(torch_func):

torch_np/_unary_ufuncs.py

@@ -1,13 +1,9 @@
 import functools
-import torch
 
-from . import _util
+import torch
 
+from . import _dtypes, _helpers, _ufunc_impl, _util
 from ._ndarray import asarray
-from . import _dtypes
-from . import _helpers
-
-from . import _ufunc_impl
 
 __all__ = [
     "abs",

torch_np/_wrapper.py

@@ -7,12 +7,16 @@
 
 import torch
 
-from . import _util
-from . import _dtypes
-from . import _helpers
-from ._ndarray import ndarray, asarray, array, asarray_replacer, newaxis
-from ._ndarray import can_cast, result_type
-
+from . import _dtypes, _helpers, _util
+from ._ndarray import (
+    array,
+    asarray,
+    asarray_replacer,
+    can_cast,
+    ndarray,
+    newaxis,
+    result_type,
+)
 
 # Things to decide on (punt for now)
 #

torch_np/testing/__init__.py

@@ -1,11 +1,11 @@
 from .utils import (
-    assert_equal,
-    assert_array_equal,
-    assert_almost_equal,
-    assert_warns,
+    _gen_alignment_data,
     assert_,
     assert_allclose,
+    assert_almost_equal,
+    assert_array_equal,
+    assert_equal,
+    assert_warns,
 )
-from .utils import _gen_alignment_data
 
 # from .testing import assert_allclose    # FIXME

torch_np/testing/utils.py

@@ -2,28 +2,27 @@
 Utility function to facilitate testing.
 
 """
+import contextlib
+import gc
+import operator
 import os
-import sys
 import platform
+import pprint
 import re
-import gc
-import operator
-import warnings
-from functools import partial, wraps
 import shutil
-import contextlib
+import sys
+import warnings
+from functools import wraps
+from io import StringIO
 from tempfile import mkdtemp, mkstemp
 from warnings import WarningMessage
-import pprint
-
-import torch_np as np
-from torch_np import intp, float32, empty, arange, ndarray, array
-from torch_np import asarray as asanyarray
-
-from io import StringIO
 
 from pytest import raises as assert_raises
 
+import torch_np as np
+from torch_np import arange, array
+from torch_np import asarray as asanyarray
+from torch_np import empty, float32, intp, ndarray
 
 __all__ = [
     "assert_equal",
@@ -201,8 +200,7 @@ def assert_equal(actual, desired, err_msg="", verbose=True):
         for k in range(len(desired)):
             assert_equal(actual[k], desired[k], f"item={k!r}\n{err_msg}", verbose)
         return
-    from torch_np import ndarray, isscalar, signbit
-    from torch_np import iscomplexobj, real, imag
+    from torch_np import imag, iscomplexobj, isscalar, ndarray, real, signbit
 
     if isinstance(actual, ndarray) or isinstance(desired, ndarray):
         return assert_array_equal(actual, desired, err_msg, verbose)
@@ -384,8 +382,7 @@ def assert_almost_equal(actual, desired, decimal=7, err_msg="", verbose=True):
 
     """
     __tracebackhide__ = True  # Hide traceback for py.test
-    from torch_np import ndarray
-    from torch_np import iscomplexobj, real, imag
+    from torch_np import imag, iscomplexobj, ndarray, real
 
     # Handle complex numbers: separate into real/imag to handle
     # nan/inf/negative zero correctly
@@ -555,7 +552,7 @@ def assert_array_compare(
     strict=False,
 ):
     __tracebackhide__ = True  # Hide traceback for py.test
-    from torch_np import asarray, array, isnan, inf, all, max, bool_
+    from torch_np import all, array, asarray, bool_, inf, isnan, max
 
     x = asarray(x)
     y = asarray(y)
@@ -928,9 +925,8 @@ def assert_array_almost_equal(x, y, decimal=6, err_msg="", verbose=True):
 
     """
     __tracebackhide__ = True  # Hide traceback for py.test
-    from torch_np import number, float_, result_type, array
-    from torch_np import issubdtype
     from torch_np import any as npany
+    from torch_np import array, float_, issubdtype, number, result_type
 
     def compare(x, y):
         try:
@@ -1217,6 +1213,7 @@ def _assert_valid_refcount(op):
         return True
 
     import gc
+
     import numpy as np
 
     b = np.arange(100 * 100).reshape(100, 100)
@@ -1758,7 +1755,6 @@ def _gen_alignment_data(dtype=float32, type="binary", max_size=24):
 
 class IgnoreException(Exception):
     "Ignoring this exception due to disabled feature"
-    pass
 
 
 @contextlib.contextmanager

torch_np/tests/test_basic.py

@@ -1,7 +1,7 @@
 import functools
-import pytest
 
 import numpy as np
+import pytest
 import torch
 
 import torch_np as w

torch_np/tests/test_function_base.py

@@ -1,9 +1,8 @@
 import pytest
-
 from pytest import raises as assert_raises
 
 import torch_np as np
-from torch_np.testing import assert_equal, assert_array_equal
+from torch_np.testing import assert_array_equal, assert_equal
 
 
 class TestCopy:

torch_np/tests/test_ndarray_methods.py

@@ -5,7 +5,6 @@
 
 # import numpy as np
 import torch_np as np
-
 from torch_np.testing import assert_equal