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Description
Supposedly, mixing array-api-strict arrays with other array types should not be allowed.
Or all of them should be allowed, but then we'd need to specify something like __array_priority__ and that opens quite a Pandora box, so I guess not?
In [5]: import numpy as np
In [6]: import array_api_strict as xp
In [7]: xp.arange(5, dtype=xp.int8) + np.arange(5, dtype=np.complex64)
Out[7]: array([0.+0.j, 2.+0.j, 4.+0.j, 6.+0.j, 8.+0.j], dtype=complex64) # xp + np -> np !
In [8]: import torch
In [10]: xp.arange(5, dtype=xp.int8) + torch.arange(5)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[10], line 1
----> 1 xp.arange(5, dtype=xp.int8) + torch.arange(5)
TypeError: unsupported operand type(s) for +: 'Array' and 'Tensor'
In [11]: import jax.numpy as jnp
In [12]: xp.arange(5, dtype=xp.int8) + jnp.arange(5, dtype=jnp.complex64)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[12], line 1
----> 1 xp.arange(5, dtype=xp.int8) + jnp.arange(5, dtype=jnp.complex64)
TypeError: unsupported operand type(s) for +: 'Array' and 'jaxlib.xla_extension.ArrayImpl'