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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.
+ """
+ # Note: this currently fails as np.argmax does not implement keepdims
+ 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.
+ """
+ # Note: this currently fails as np.argmin does not implement keepdims
+ 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 Array._new(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)
+ return Array._new(np.where(condition._array, x1._array, x2._array))