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