lint

Author: ev-br

torch_np/_dtypes_impl.py

@@ -55,8 +55,14 @@ def result_type_impl(*tensors):
 
 SCALAR_TYPES = (int, bool, float, complex)
 
+
 def _dtype_for_scalar(py_type):
-    return {bool: torch.bool, int: torch.int64, float: torch.float64, complex: torch.complex128}[py_type]
+    return {
+        bool: torch.bool,
+        int: torch.int64,
+        float: torch.float64,
+        complex: torch.complex128,
+    }[py_type]
 
 
 categories = [
@@ -86,7 +92,7 @@ def nep50_to_tensors(x1, x2):
         x1 = torch.as_tensor(x1, dtype=_dtype_for_scalar(x1_type))
         x2 = torch.as_tensor(x2, dtype=_dtype_for_scalar(x2_type))
         return x1, x2
-    elif not(x1_is_weak or x2_is_weak):
+    elif not (x1_is_weak or x2_is_weak):
         # two tensors: nothing to do here
         return x1, x2
     else:
@@ -99,11 +105,7 @@ def nep50_to_tensors(x1, x2):
         cat_weak = category(weak_dtype)
         cat_not_weak = category(not_weak.dtype)
 
-        dt = (
-            not_weak.dtype
-            if cat_weak <= cat_not_weak
-            else dtype_for_cat[cat_weak]
-        )
+        dt = not_weak.dtype if cat_weak <= cat_not_weak else dtype_for_cat[cat_weak]
 
         # special-case complex + float32
         if weak_dtype.is_complex and not_weak.dtype == torch.float32:

torch_np/_ndarray.py

@@ -501,9 +501,7 @@ def array(
 
 def asarray(a, dtype=None, order="K", *, like=None):
     # The result of the public `np.asarray(obj)` is not weakly typed.
-    return array(
-        a, dtype=dtype, order=order, like=like, copy=False, ndmin=0
-    )
+    return array(a, dtype=dtype, order=order, like=like, copy=False, ndmin=0)
 
 
 def from_dlpack(x, /):

torch_np/_normalizations.py

@@ -40,7 +40,6 @@
     NotImplementedType = typing.TypeVar("NotImplementedType")
 
 
-
 def normalize_array_like(x, parm=None):
     from ._ndarray import asarray
 

torch_np/_ufuncs.py

@@ -7,11 +7,11 @@
 from . import _binary_ufuncs_impl, _dtypes_impl, _helpers, _unary_ufuncs_impl, _util
 from ._normalizations import (
     ArrayLike,
-    Scalar,
     CastingModes,
     DTypeLike,
     NotImplementedType,
     OutArray,
+    Scalar,
     normalizer,
 )
 
@@ -77,7 +77,7 @@ def wrapped(
 # matmul's signature is _slightly_ different from other ufuncs:
 # - no where=...
 # - additional axis=..., axes=...
-# - no NEP50 scalars in or out 
+# - no NEP50 scalars in or out
 #
 @normalizer
 def matmul(

torch_np/_util.py

@@ -226,5 +226,3 @@ def _coerce_to_tensor(obj, dtype=None, copy=False, ndmin=0):
         tensor = tensor.clone()
 
     return tensor
-
-

torch_np/tests/test_nep50_examples.py

@@ -91,4 +91,3 @@ def test_nep50_exceptions(example):
 
         assert_allclose(result, new, atol=1e-16)
         assert result.dtype == new.dtype
-