a
    þdh  ã                   @   s†   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eee ee edœdd	„Zdeee ed
 ee edœdd„ZdS )é    )ÚOptional)ÚTensor)ÚLiteral)Ú_check_inputÚ_reduce_distance_matrix)Ú_safe_matmulN)ÚxÚyÚzero_diagonalÚreturnc                 C   s.   t | ||ƒ\} }}t| |ƒ}|r*| 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   Zfill_diagonal_)r   r	   r
   Údistance© r   úp/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/pairwise/linear.pyÚ"_pairwise_linear_similarity_update   s
    

r   )ÚmeanÚsumÚnoneN)r   r	   Ú	reductionr
   r   c                 C   s   t | ||ƒ}t||ƒ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   r   r   r   Úpairwise_linear_similarity*   s    )r   )NN)NNN)Útypingr   Ztorchr   Ztyping_extensionsr   Z(torchmetrics.functional.pairwise.helpersr   r   Ztorchmetrics.utilities.computer   Úboolr   r   r   r   r   r   Ú<module>   s&    ÿþ   üû