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Z
 	 ddedee   d	ee   d
efdZ	 	 	 ddedee   ded   d	ee   d
ef
dZy)    )Optional)Tensor)Literal)_check_input_reduce_distance_matrix)_safe_matmulNxyzero_diagonalreturnc                 f    t        | ||      \  } }}t        | |      }|r|j                  d       |S )zCalculate the pairwise linear similarity matrix.

    Args:
        x: tensor of shape ``[N,d]``
        y: tensor of shape ``[M,d]``
        zero_diagonal: determines if the diagonal of the distance matrix should be set to zero

    r   )r   r   fill_diagonal_)r	   r
   r   distances       |/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/functional/pairwise/linear.py"_pairwise_linear_similarity_updater      s;     'q!];Aq-Aq!H"O    	reduction)meansumnoneNc                 4    t        | ||      }t        ||      S )a  Calculate pairwise linear similarity.

    .. math::
        s_{lin}(x,y) = <x,y> = \sum_{d=1}^D x_d \cdot y_d

    If both :math:`x` and :math:`y` are passed in, the calculation will be performed pairwise between
    the rows of :math:`x` and :math:`y`.
    If only :math:`x` is passed in, the calculation will be performed between the rows of :math:`x`.

    Args:
        x: Tensor with shape ``[N, d]``
        y: Tensor with shape ``[M, d]``, optional
        reduction: reduction to apply along the last dimension. Choose between `'mean'`, `'sum'`
            (applied along column dimension) or  `'none'`, `None` for no reduction
        zero_diagonal: if the diagonal of the distance matrix should be set to 0. If only `x` is given
            this defaults to `True` else if `y` is also given it defaults to `False`

    Returns:
        A ``[N,N]`` matrix of distances if only ``x`` is given, else a ``[N,M]`` matrix

    Example:
        >>> import torch
        >>> from torchmetrics.functional.pairwise import pairwise_linear_similarity
        >>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)
        >>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)
        >>> pairwise_linear_similarity(x, y)
        tensor([[ 2.,  7.],
                [ 3., 11.],
                [ 5., 18.]])
        >>> pairwise_linear_similarity(x)
        tensor([[ 0., 21., 34.],
                [21.,  0., 55.],
                [34., 55.,  0.]])

    )r   r   )r	   r
   r   r   r   s        r   pairwise_linear_similarityr   *   s     R 2!QFH"8Y77r   )NN)NNN)typingr   torchr   typing_extensionsr   (torchmetrics.functional.pairwise.helpersr   r   torchmetrics.utilities.computer   boolr   r    r   r   <module>r       s      % Z 7 LP6":B4.* 6:$(	*8*8*8 23*8 D>	*8
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