TST: test binary operators vs. numpy generics

[crusaderky]

In #135, binary operators vs. numpy.int64 became (correctly) disallowed.
Add a test for it.
Also test binary operators vs. numpy.float64 and numpy.complex128, which must be allowed as they are subclasses of float and complex respectively.

• XREF TST/MAINT: signal.symiirorder2: r, omega, precision are floats; use default dtypes scipy/scipy#22783

[ev-br]

Have to admit I'm not a fan of this. This is technically correct, it is a subclass. That said, it is an implementation detail IMO.

I'd think that all instances where numpy returns a numpy scalar, all other libraries return 0D arrays, so I'd think we better always handle them together with arrays not python scalars.

[crusaderky]

Are you saying that

x = xp.asarray(1, dtype=xp.float32)
y = np.float64(2)
z = xp.maximum(x, y)

maximum should crash instead of returning an Array[float32]?

[ev-br]

Definitely not crash :-).

I think the following is correct[ because np.float64(2) is conceptually a np.asarray(2.) not python scalar 2.)

In [4]: xp = array_namespace(np.empty(2))

In [5]: xp.maximum(xp.asarray(1, dtype=xp.float32), 2)
Out[5]: np.float32(2.0)

In [6]: xp.maximum(xp.asarray(1, dtype=xp.float32), np.float64(2))
Out[6]: np.float64(2.0)

Likewise, the following is also correct, for the same reason:

In [12]: xpt = array_namespace(torch.empty(2))

In [13]: xpt.maximum(xpt.asarray(1, dtype=xpt.float32), np.float64(2))
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[13], line 1
----> 1 xpt.maximum(xpt.asarray(1, dtype=xpt.float32), np.float64(2))

File ~/repos/array-api-compat/array_api_compat/torch/_aliases.py:91, in _two_arg.<locals>._f(x1, x2, **kwargs)
     88 @_wraps(f)
     89 def _f(x1, x2, /, **kwargs):
     90     x1, x2 = _fix_promotion(x1, x2)
---> 91     return f(x1, x2, **kwargs)

TypeError: maximum(): argument 'other' (position 2) must be Tensor, not numpy.float64

[crusaderky]

In [6]: xp.maximum(xp.asarray(1, dtype=xp.float32), np.float64(2))
Out[6]: np.float64(2.0)

This looks definitely wrong to me. array-api-strict shouldn't quietly accept a numpy array.
Right now the output is the expected one without explicitly disallowing inheritance:

>>> xp.maximum(xp.asarray(1, dtype=xp.float32), np.float64(2))
Array(2., dtype=array_api_strict.float32)
>>> xp.maximum(xp.asarray(1, dtype=xp.float32), np.asarray(2, dtype=np.float64))
TypeError: Only real numeric dtypes are allowed in maximum(...). Got array_api_strict.float32 and float64.
>>> xp.maximum(xp.asarray(1, dtype=xp.int32), np.int64(2))
TypeError: Only real numeric dtypes are allowed in maximum(...). Got array_api_strict.int32 and int64.

[crusaderky]

nit: the message of the TypeError is a bit misleading; it should call out explicitly something like "only Array and python scalars are accepted; got numpy.ndarray

[ev-br]

This looks definitely wrong to me. array-api-strict shouldn't quietly accept a numpy array.
Right now the output is the expected one without explicitly disallowing inheritance:

Yes, in my example xp is array_api_compat.numpy. As your example shows, array-api-strict does the right thing in main, too. So what is this PR fixing?