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from __future__ import annotations
from ._array_object import Array
from ._dtypes import _result_type
from typing import Optional, Tuple
import numpy as np
def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
"""
Array API compatible wrapper for :py:func:`np.argmax <numpy.argmax>`.
See its docstring for more information.
"""
return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims)))
def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
"""
Array API compatible wrapper for :py:func:`np.argmin <numpy.argmin>`.
See its docstring for more information.
"""
return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims)))
def nonzero(x: Array, /) -> Tuple[Array, ...]:
"""
Array API compatible wrapper for :py:func:`np.nonzero <numpy.nonzero>`.
See its docstring for more information.
"""
return tuple(Array._new(i) for i in np.nonzero(x._array))
def where(condition: Array, x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
See its docstring for more information.
"""
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.where(condition._array, x1._array, x2._array))