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�d��ZdS)zEMixin classes for custom array types that don't inherit from ndarray.�)�umath�NDArrayOperatorsMixinc�8�	|jduS#t$rYdSwxYw)z)True when __array_ufunc__ is set to None.NF)�__array_ufunc__�AttributeError)�objs �G/opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/lib/mixins.py�_disables_array_ufuncr
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�c�F���fd�}d�|��|_|S)z>Implement a forward binary method with a ufunc, e.g., __add__.c�H��t|��rtS�||��S�N�r
�NotImplemented��self�other�ufuncs  �r	�funcz_binary_method.<locals>.funcs+��� ��'�'�	"�!�!��u�T�5�!�!�!��__{}__��format�__name__�r�namers`  r	�_binary_methodrs6���"�"�"�"�"��O�O�D�)�)�D�M��Krc�F���fd�}d�|��|_|S)zAImplement a reflected binary method with a ufunc, e.g., __radd__.c�H��t|��rtS�||��Sr
rrs  �r	rz&_reflected_binary_method.<locals>.funcs+��� ��'�'�	"�!�!��u�U�D�!�!�!rz__r{}__rrs`  r	�_reflected_binary_methodrs8���"�"�"�"�"��$�$�T�*�*�D�M��Krc�F���fd�}d�|��|_|S)zAImplement an in-place binary method with a ufunc, e.g., __iadd__.c�"���|||f���S)N)�out�rs  �r	rz$_inplace_binary_method.<locals>.func&s����u�T�5�t�g�.�.�.�.rz__i{}__rrs`  r	�_inplace_binary_methodr$$s6���/�/�/�/�/��$�$�T�*�*�D�M��Krc�`�t||��t||��t||��fS)zEImplement forward, reflected and inplace binary methods with a ufunc.)rrr$)rrs  r	�_numeric_methodsr&,s3���5�$�'�'�$�U�D�1�1�"�5�$�/�/�1�1rc�F���fd�}d�|��|_|S)z.Implement a unary special method with a ufunc.c����|��Sr
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Mixin defining all operator special methods using __array_ufunc__.

    This class implements the special methods for almost all of Python's
    builtin operators defined in the `operator` module, including comparisons
    (``==``, ``>``, etc.) and arithmetic (``+``, ``*``, ``-``, etc.), by
    deferring to the ``__array_ufunc__`` method, which subclasses must
    implement.

    It is useful for writing classes that do not inherit from `numpy.ndarray`,
    but that should support arithmetic and numpy universal functions like
    arrays as described in `A Mechanism for Overriding Ufuncs
    <https://numpy.org/neps/nep-0013-ufunc-overrides.html>`_.

    As an trivial example, consider this implementation of an ``ArrayLike``
    class that simply wraps a NumPy array and ensures that the result of any
    arithmetic operation is also an ``ArrayLike`` object::

        class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin):
            def __init__(self, value):
                self.value = np.asarray(value)

            # One might also consider adding the built-in list type to this
            # list, to support operations like np.add(array_like, list)
            _HANDLED_TYPES = (np.ndarray, numbers.Number)

            def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
                out = kwargs.get('out', ())
                for x in inputs + out:
                    # Only support operations with instances of _HANDLED_TYPES.
                    # Use ArrayLike instead of type(self) for isinstance to
                    # allow subclasses that don't override __array_ufunc__ to
                    # handle ArrayLike objects.
                    if not isinstance(x, self._HANDLED_TYPES + (ArrayLike,)):
                        return NotImplemented

                # Defer to the implementation of the ufunc on unwrapped values.
                inputs = tuple(x.value if isinstance(x, ArrayLike) else x
                               for x in inputs)
                if out:
                    kwargs['out'] = tuple(
                        x.value if isinstance(x, ArrayLike) else x
                        for x in out)
                result = getattr(ufunc, method)(*inputs, **kwargs)

                if type(result) is tuple:
                    # multiple return values
                    return tuple(type(self)(x) for x in result)
                elif method == 'at':
                    # no return value
                    return None
                else:
                    # one return value
                    return type(self)(result)

            def __repr__(self):
                return '%s(%r)' % (type(self).__name__, self.value)

    In interactions between ``ArrayLike`` objects and numbers or numpy arrays,
    the result is always another ``ArrayLike``:

        >>> x = ArrayLike([1, 2, 3])
        >>> x - 1
        ArrayLike(array([0, 1, 2]))
        >>> 1 - x
        ArrayLike(array([ 0, -1, -2]))
        >>> np.arange(3) - x
        ArrayLike(array([-1, -1, -1]))
        >>> x - np.arange(3)
        ArrayLike(array([1, 1, 1]))

    Note that unlike ``numpy.ndarray``, ``ArrayLike`` does not allow operations
    with arbitrary, unrecognized types. This ensures that interactions with
    ArrayLike preserve a well-defined casting hierarchy.

    .. versionadded:: 1.13
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