seems __buffer__ has been added with 3.12: https://docs.python.org/3/reference/datamodel.html#object.__buffer__
https://docs.python.org/3/library/collections.abc.html#collections.abc.Buffer
https://peps.python.org/pep-0688/

we should probably start to support it as well. Maybe add a TODO to check for __buffer__/use collections.abc.Buffer and then call memoryview if it fails

actually I misunderstood the PEP: this feature is already implemented, we can add a check for Python version and check against __buffer__ or try memoryview only if it's <3.12

Can all that use cases covered converting x to numpy.ndarray?
Do we need so complicated if-logic?

here is CuPy implementation of a similar idea for a reference:
https://github.com/cupy/cupy/blob/main/cupy/_core/_routines_indexing.pyx#L247

can take a similar approach too in some cases (best to leave converting to usm_ndarray to the tensor layer, though, for queue propagation reasons)

I don't think we need to fully follow the same CuPy implementation in _prepare_slice_list, because it seems CuPy mimics faulty NumPy behavior sometimes.

Not sure if I got the comment fully correctly, but in general I meant something like:

    if x is None or x is Ellipsis or isinstance(x, (dpt.usm_ndarray, slice, numpy.ndarray)):
        return x
    if isinstance(x, dpnp_array):
        return x.get_array()

    try:
        x = numpy.asarray(x)
    except ... :
        ...
        raise ...
    ...
    return x

yep, agreed mostly, though problem is, we don't really want to send integers or boolean Python scalars to numpy arrays, which is why we may need something like:

        elif numpy.isscalar(s):
            if not isinstance(s, (bool, numpy.bool_)):
                # keep scalar int
                continue

which is in cupy implementation. We could handle range, list, tuple through numpy though