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from __future__ import annotations
from ._array_object import Array
from ._dtypes import _numeric_dtypes, _result_type
from typing import Optional, Sequence, Tuple, Union
import numpy as np
# einsum is not yet implemented in the array API spec.
# def einsum():
# """
# Array API compatible wrapper for :py:func:`np.einsum <numpy.einsum>`.
#
# See its docstring for more information.
# """
# return np.einsum()
def matmul(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.matmul <numpy.matmul>`.
See its docstring for more information.
"""
# Note: the restriction to numeric dtypes only is different from
# np.matmul.
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError('Only numeric dtypes are allowed in matmul')
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
return Array._new(np.matmul(x1._array, x2._array))
# Note: axes must be a tuple, unlike np.tensordot where it can be an array or array-like.
def tensordot(x1: Array, x2: Array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2) -> Array:
# Note: the restriction to numeric dtypes only is different from
# np.tensordot.
if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
raise TypeError('Only numeric dtypes are allowed in tensordot')
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
return Array._new(np.tensordot(x1._array, x2._array, axes=axes))
def transpose(x: Array, /, *, axes: Optional[Tuple[int, ...]] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.transpose <numpy.transpose>`.
See its docstring for more information.
"""
return Array._new(np.transpose(x._array, axes=axes))
# Note: vecdot is not in NumPy
def vecdot(x1: Array, x2: Array, /, *, axis: Optional[int] = None) -> Array:
if axis is None:
axis = -1
return tensordot(x1, x2, axes=((axis,), (axis,)))
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