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Mixin classes for custom array types that don't inherit from ndarray.
�)�umath�NDArrayOperatorsMixinc�>�	|jduS#t$rYywxYw)z)True when __array_ufunc__ is set to None.NF)�__array_ufunc__�AttributeError)�objs �2C:\Python312\Lib\site-packages\numpy/lib/mixins.py�_disables_array_ufuncr

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�	�c�>���fd�}dj|�|_|S)z>Implement a forward binary method with a ufunc, e.g., __add__.c�8��t|�rtS�||�S�N�r
�NotImplemented��self�other�ufuncs  �r	�funcz_binary_method.<locals>.funcs��� ��'�!�!��T�5�!�!��__{}__��format�__name__�r�namers`  r	�_binary_methodrs���"��O�O�D�)�D�M��Krc�>���fd�}dj|�|_|S)zAImplement a reflected binary method with a ufunc, e.g., __radd__.c�8��t|�rtS�||�Sr
rrs  �r	rz&_reflected_binary_method.<locals>.funcs��� ��'�!�!��U�D�!�!rz__r{}__rrs`  r	�_reflected_binary_methodrs!���"��$�$�T�*�D�M��Krc�>���fd�}dj|�|_|S)zAImplement an in-place binary method with a ufunc, e.g., __iadd__.c����|||f��S)N)�out�rs  �r	rz$_inplace_binary_method.<locals>.func(s����T�5�t�g�.�.rz__i{}__rrs`  r	�_inplace_binary_methodr$&s���/��$�$�T�*�D�M��Krc�H�t||�t||�t||�fS)zEImplement forward, reflected and inplace binary methods with a ufunc.)rrr$)rrs  r	�_numeric_methodsr&.s*���5�$�'�$�U�D�1�"�5�$�/�1�1rc�>���fd�}dj|�|_|S)z.Implement a unary special method with a ufunc.c����|�Sr
r#)rrs �r	rz_unary_method.<locals>.func7s
����T�{�rrrrs`  r	�
_unary_methodr)5s�����O�O�D�)�D�M��Krc�2�eZdZdZdZeejd�Zeejd�Z
eejd�Zeejd�Zeejd�Zeej"d�Zeej(d	�\ZZZeej0d
�\ZZZeej8d�\ZZZeej@d�\Z!Z"Z#eejHd
�\Z%Z&Z'eejPd�\Z)Z*Z+eejXd�\Z-Z.Z/eej`d�Z1e2ej`d�Z3eejhd�\Z5Z6Z7eejpd�\Z9Z:Z;eejxd�\Z=Z>Z?eej�d�\ZAZBZCeej�d�\ZEZFZGeej�d�\ZIZJZKeLej�d�ZNeLej�d�ZPeLej�d�ZReLej�d�ZTy)ra 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:

        >>> import numbers
        >>> 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.

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