Ë
    þÍ:jh  ã                   ó¢   — d dl mZ d dlmZ d dlmZ d dlmZmZ d dl	m
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)é    )ÚOptional)ÚTensor)ÚLiteral)Ú_check_inputÚ_reduce_distance_matrix)Ú_safe_matmulNÚxÚyÚzero_diagonalÚreturnc                 óf   — t        | ||«      \  } }}t        | |«      }|r|j                  d«       |S )zêCalculate 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   Úfill_diagonal_)r	   r
   r   Údistances       ú|/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/pairwise/linear.pyÚ"_pairwise_linear_similarity_updater      s;   € ô ' q¨!¨]Ó;Ñ€A€qˆ-ä˜A˜qÓ!€HÙØ×Ñ Ô"Ø€Oó    Ú	reduction)ÚmeanÚsumÚnoneNc                 ó4   — 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   Úpairwise_linear_similarityr   *   s    € ôR 2°!°Q¸ÓF€HÜ" 8¨YÓ7Ð7r   )NN)NNN)Útypingr   Útorchr   Útyping_extensionsr   Ú(torchmetrics.functional.pairwise.helpersr   r   Útorchmetrics.utilities.computer   Úboolr   r   © r   r   ú<module>r       s˜   ðõ å Ý %ç ZÝ 7ð LPñØðØ˜6Ñ"ðØ:BÀ4¹.ðàóð* Ø6:Ø$(ñ	*8Øð*8à�Ñð*8ð Ð2Ñ3ð*8ð ˜D‘>ð	*8ð
 ô*8r   