BUG: Fix copy semantics in __array__

[seberg]

This should fix the semantics of __array__. While rejecting copy=False is OK even if unnecessary, copy=True should never have been ignored and is dangerous.

Closes gh-57739, gh-59932

• Still needs new tests.
• Added an entry in the latest doc/source/whatsnew/vX.X.X.rst file if fixing a bug or adding a new feature.

---

I have to figure out the tests. I think a test in form of:

arr1 = np.asarray(obj, copy=True)
arr2 = np.asarray(obj, copy=True)
assert not np.may_share_memory(arr1, arr2)
# Check that without copy always works:
assert_array_equal(arr1, np.asarray(obj))

if np_ver < 2:
    return  # copy=False semantics not supported

try:
   arr1 = np.asarray(obj, copy=False)
except ValueError:
   return  # An error is acceptable for `copy=False`

# If no error is given, multiple returns must be views:
arr2 = np.asarray(obj, copy=False)
assert np.may_share_memory(arr1, arr2)

will work nicely. But, right now I am not sure if there is a convenient pattern/parametrization to steal to cover all of the __array__ implementations here.

[seberg]

I did not understand why this is needed. If dtypes match, NumPy should make it a no-op? If dtype is None, it is the same as not passing.

[ajfriend]

Thanks for addressing this! Could this be released as a bug fix for v2.x?

[seberg]

FWIW, if anyone knows how to add decent tests for this, pushing to the PR is appreciated. Otherwise, I may try to figure out where to add at least tests for a few of these paths.