revert cupy/cp back to numpy/np for easier patch application

cupy/array_api/_array_object.py

@@ -34,19 +34,19 @@
 if TYPE_CHECKING:
     from ._typing import PyCapsule, Device, Dtype
 
-import cupy as cp
+import cupy as np
 
-from cupyx import array_api
+from cupy import array_api
 
 
 class Array:
     """
     n-d array object for the array API namespace.
 
-    See the docstring of :py:obj:`cp.ndarray <cupy.ndarray>` for more
+    See the docstring of :py:obj:`np.ndarray <numpy.ndarray>` for more
     information.
 
-    This is a wrapper around cupy.ndarray that restricts the usage to only
+    This is a wrapper around numpy.ndarray that restricts the usage to only
     those things that are required by the array API namespace. Note,
     attributes on this object that start with a single underscore are not part
     of the API specification and should only be used internally. This object
@@ -70,9 +70,9 @@ def _new(cls, x, /):
         """
         obj = super().__new__(cls)
         # Note: The spec does not have array scalars, only 0-D arrays.
-        if isinstance(x, cp.generic):
+        if isinstance(x, np.generic):
             # Convert the array scalar to a 0-D array
-            x = cp.asarray(x)
+            x = np.asarray(x)
         if x.dtype not in _all_dtypes:
             raise TypeError(
                 f"The array_api namespace does not support the dtype '{x.dtype}'"
@@ -135,8 +135,8 @@ def _check_allowed_dtypes(self, other, dtype_category, op):
             # the type promoted operator does not match the left-hand side
             # operand. For example,
 
-            # >>> a = cp.array(1, dtype=cp.int8)
-            # >>> a += cp.array(1, dtype=cp.int16)
+            # >>> a = np.array(1, dtype=np.int8)
+            # >>> a += np.array(1, dtype=np.int16)
 
             # The spec explicitly disallows this.
             if res_dtype != self.dtype:
@@ -178,7 +178,7 @@ def _promote_scalar(self, scalar):
         # behavior for integers within the bounds of the integer dtype.
         # Outside of those bounds we use the default NumPy behavior (either
         # cast or raise OverflowError).
-        return Array._new(cp.array(scalar, self.dtype))
+        return Array._new(np.array(scalar, self.dtype))
 
     @staticmethod
     def _normalize_two_args(x1, x2):
@@ -188,10 +188,10 @@ def _normalize_two_args(x1, x2):
         NumPy deviates from the spec type promotion rules in cases where one
         argument is 0-dimensional and the other is not. For example:
 
-        >>> import cupy as cp
-        >>> a = cp.array([1.0], dtype=cp.float32)
-        >>> b = cp.array(1.0, dtype=cp.float64)
-        >>> cp.add(a, b) # The spec says this should be float64
+        >>> import numpy as np
+        >>> a = np.array([1.0], dtype=np.float32)
+        >>> b = np.array(1.0, dtype=np.float64)
+        >>> np.add(a, b) # The spec says this should be float64
         array([2.], dtype=float32)
 
         To fix this, we add a dimension to the 0-dimension array before passing it
@@ -294,9 +294,9 @@ def _validate_index(key, shape):
 
             for idx in key:
                 if (
-                    isinstance(idx, cp.ndarray)
+                    isinstance(idx, np.ndarray)
                     and idx.dtype in _boolean_dtypes
-                    or isinstance(idx, (bool, cp.bool_))
+                    or isinstance(idx, (bool, np.bool_))
                 ):
                     if len(key) == 1:
                         return key
@@ -979,7 +979,7 @@ def __rxor__(self: Array, other: Union[int, bool, Array], /) -> Array:
     @property
     def dtype(self) -> Dtype:
         """
-        Array API compatible wrapper for :py:meth:`cp.ndarray.dtype <cupy.ndarray.dtype>`.
+        Array API compatible wrapper for :py:meth:`np.ndarray.dtype <numpy.ndarray.dtype>`.
 
         See its docstring for more information.
         """
@@ -992,7 +992,7 @@ def device(self) -> Device:
     @property
     def ndim(self) -> int:
         """
-        Array API compatible wrapper for :py:meth:`cp.ndarray.ndim <cupy.ndarray.ndim>`.
+        Array API compatible wrapper for :py:meth:`np.ndarray.ndim <numpy.ndarray.ndim>`.
 
         See its docstring for more information.
         """
@@ -1001,7 +1001,7 @@ def ndim(self) -> int:
     @property
     def shape(self) -> Tuple[int, ...]:
         """
-        Array API compatible wrapper for :py:meth:`cp.ndarray.shape <cupy.ndarray.shape>`.
+        Array API compatible wrapper for :py:meth:`np.ndarray.shape <numpy.ndarray.shape>`.
 
         See its docstring for more information.
         """
@@ -1010,7 +1010,7 @@ def shape(self) -> Tuple[int, ...]:
     @property
     def size(self) -> int:
         """
-        Array API compatible wrapper for :py:meth:`cp.ndarray.size <cupy.ndarray.size>`.
+        Array API compatible wrapper for :py:meth:`np.ndarray.size <numpy.ndarray.size>`.
 
         See its docstring for more information.
         """
@@ -1019,7 +1019,7 @@ def size(self) -> int:
     @property
     def T(self) -> Array:
         """
-        Array API compatible wrapper for :py:meth:`cp.ndarray.T <cupy.ndarray.T>`.
+        Array API compatible wrapper for :py:meth:`np.ndarray.T <numpy.ndarray.T>`.
 
         See its docstring for more information.
         """

cupy/array_api/_constants.py

@@ -1,6 +1,6 @@
-import cupy as cp
+import cupy as np
 
-e = cp.e
-inf = cp.inf
-nan = cp.nan
-pi = cp.pi
+e = np.e
+inf = np.inf
+nan = np.nan
+pi = np.pi