feat: implement array compatibility/interface protocols for virtual array

[ikrommyd]

This is to fix all the ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() issues we see with the cuda backend with __setitem__ and __getitem__ between virtual arrays with a cupy nplike and regular cupy arrays. We xfail several tests due to that. This will work with cupy 14 once it comes out and causes no errors otherwise. With cupy 14, the tests we xfail actually pass (tried with cupy installation from v14 pre-release) Once cupy 14 is out, we should remove those xfails too. See cupy/cupy#9089 and cupy/cupy#8352 and #3142 for more info.

Also this is to fix a few more cases similar to the cupy issue but with the numpy backend where if you do y[:] = x and y is a numpy array and x a virtual array, this will use __iter__ and iterate over the virtual array like a python object. Adding the interface fixes that.

[codecov]

Codecov Report

❌ Patch coverage is 76.92308% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 82.63%. Comparing base (e4be9fe) to head (90bf00c).

Files with missing lines	Patch %	Lines
src/awkward/_nplikes/virtual.py	76.92%	3 Missing ⚠️

Additional details and impacted files

Files with missing lines	Coverage Δ
src/awkward/_nplikes/virtual.py	91.16% <76.92%> (-0.96%)	⬇️

... and 2 files with indirect coverage changes

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[github-actions]

The documentation preview is ready to be viewed at http://preview.awkward-array.org.s3-website.us-east-1.amazonaws.com/PR3839

[ikrommyd]

@pfackeldey we've been iterating over numpy arrays this whole time in certain edge cases. If you do y[:] = x and y is a numpy array and x a virtual array, this uses __iter__ and iterates over the virtual array like a python object. Adding the interface makes numpy able to convert the virtual array into a numpy array instead of seeing it as an iterable.

[ikrommyd]

This can also be

    def __array__(self, dtype=None, copy=None):
        return ak._nplikes.numpy.Numpy.instance().asarray(self.materialize(), dtype=dtype, copy=copy)

I think and it would still be compatible with numpy1