Implement support of tuple key in __getitem__ and __setitem__

[antonwolfy]

The PR is about to close #1361.

It introduced support of key as a tuple in dpnp.__getitem__() and dpnp.__setitem__() functions.

Previously dpnp was falling back on dpctl call in case of tuple as a key, but it is not always correct.
It requires to unwrap dpnp array into USM ndarray for each dpnp array in tuple key before invoking dpctl functions.

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[oleksandr-pavlyk]

By the way, using test_equal may not be the most efficient way of testing array equality:

In [13]: import dpnp, numpy as np

In [14]: x = dpnp.arange(6)

In [15]: np.asarray(x)
Out[15]:
array([array(0), array(1), array(2), array(3), array(4), array(5)],
      dtype=object)

In [16]: x = dpnp.arange(6).reshape((2,3))

In [17]: np.asarray(x)
Out[17]:
array([[array(0), array(1), array(2)],
       [array(3), array(4), array(5)]], dtype=object)

I can not quite put my finger on what property of the container makes NumPy behave like this.

I have tried to create a mock indexable array, but it behaves different:

In [24]: class Indexable:
    ...:     def __init__(self, n):
    ...:         self.data_ = list(range(n))
    ...:     def __getitem__(self, key):
    ...:         return self.data[key]
    ...:     def __setitem__(self, key, val):
    ...:         self.data_[key] = val
    ...:     @property
    ...:     def shape(self):
    ...:         return (len(self.data_),)
    ...:     @property
    ...:     def dtype(self):
    ...:         return np.int_
    ...:

In [25]: np.asarray(Indexable(6))
Out[25]: array(<__main__.Indexable object at 0x7f5d742c7790>, dtype=object)

[antonwolfy]

By the way, using test_equal may not be the most efficient way of testing array equality:

In [13]: import dpnp, numpy as np

In [14]: x = dpnp.arange(6)

In [15]: np.asarray(x)
Out[15]:
array([array(0), array(1), array(2), array(3), array(4), array(5)],
      dtype=object)

In [16]: x = dpnp.arange(6).reshape((2,3))

In [17]: np.asarray(x)
Out[17]:
array([[array(0), array(1), array(2)],
       [array(3), array(4), array(5)]], dtype=object)

I can not quite put my finger on what property of the container makes NumPy behave like this.

I have tried to create a mock indexable array, but it behaves different:

In [24]: class Indexable:
    ...:     def __init__(self, n):
    ...:         self.data_ = list(range(n))
    ...:     def __getitem__(self, key):
    ...:         return self.data[key]
    ...:     def __setitem__(self, key, val):
    ...:         self.data_[key] = val
    ...:     @property
    ...:     def shape(self):
    ...:         return (len(self.data_),)
    ...:     @property
    ...:     def dtype(self):
    ...:         return np.int_
    ...:

In [25]: np.asarray(Indexable(6))
Out[25]: array(<__main__.Indexable object at 0x7f5d742c7790>, dtype=object)

There will be dpnp.dpnp_utils.convert_item function called to convert to numpy array, before invoking numpy.testing.assert_equal. Thus dtype will be correct one.