Structure the copy flag in cupy.asarray better

This way it is more future-proof for when cupy changes the meaning of
copy=False.

Author: asmeurer

array_api_compat/cupy/_aliases.py

@@ -61,6 +61,8 @@
 matrix_transpose = get_xp(cp)(_aliases.matrix_transpose)
 tensordot = get_xp(cp)(_aliases.tensordot)
 
+_copy_default = object()
+
 # asarray also adds the copy keyword, which is not present in numpy 1.0.
 def asarray(
     obj: Union[
@@ -75,7 +77,7 @@ def asarray(
     *,
     dtype: Optional[Dtype] = None,
     device: Optional[Device] = None,
-    copy: Optional[bool] = None,
+    copy: Optional[bool] = _copy_default,
     **kwargs,
 ) -> ndarray:
     """
@@ -90,12 +92,23 @@ def asarray(
     with cp.cuda.Device(device):
         # cupy is like NumPy 1.26 (except without _CopyMode). See the comments
         # in asarray in numpy/_aliases.py.
-        if copy is None:
-            copy = False
-        elif copy is False:
-            raise NotImplementedError("asarray(copy=False) is not yet supported in cupy")
+        if copy is not _copy_default:
+            # A future version of CuPy will change the meaning of copy=False
+            # to mean no-copy. We don't know for certain what version it will
+            # be yet, so to avoid breaking that version, we use a different
+            # default value for copy so asarray(obj) with no copy kwarg will
+            # always do the copy-if-needed behavior.
+
+            # This will still need to be updated to remove the
+            # NotImplementedError for copy=False, but at least this won't
+            # break the default or existing behavior.
+            if copy is None:
+                copy = False
+            elif copy is False:
+                raise NotImplementedError("asarray(copy=False) is not yet supported in cupy")
+            kwargs['copy'] = copy
 
-        return cp.array(obj, copy=copy, dtype=dtype, **kwargs)
+        return cp.array(obj, dtype=dtype, **kwargs)
 
 # These functions are completely new here. If the library already has them
 # (i.e., numpy 2.0), use the library version instead of our wrapper.