
    sg                     b    d dl mc mZ d dlmZ ddlmZ ddgZ G d de      Z	 G d de      Z
y)	    N)Tensor   )ModulePairwiseDistanceCosineSimilarityc            	       t     e Zd ZU dZg dZeed<   eed<   eed<   	 ddedededdf fd	Zd
e	de	de	fdZ
 xZS )r   aM  
    Computes the pairwise distance between input vectors, or between columns of input matrices.

    Distances are computed using ``p``-norm, with constant ``eps`` added to avoid division by zero
    if ``p`` is negative, i.e.:

    .. math ::
        \mathrm{dist}\left(x, y\right) = \left\Vert x-y + \epsilon e \right\Vert_p,

    where :math:`e` is the vector of ones and the ``p``-norm is given by.

    .. math ::
        \Vert x \Vert _p = \left( \sum_{i=1}^n  \vert x_i \vert ^ p \right) ^ {1/p}.

    Args:
        p (real, optional): the norm degree. Can be negative. Default: 2
        eps (float, optional): Small value to avoid division by zero.
            Default: 1e-6
        keepdim (bool, optional): Determines whether or not to keep the vector dimension.
            Default: False
    Shape:
        - Input1: :math:`(N, D)` or :math:`(D)` where `N = batch dimension` and `D = vector dimension`
        - Input2: :math:`(N, D)` or :math:`(D)`, same shape as the Input1
        - Output: :math:`(N)` or :math:`()` based on input dimension.
          If :attr:`keepdim` is ``True``, then :math:`(N, 1)` or :math:`(1)` based on input dimension.

    Examples::
        >>> pdist = nn.PairwiseDistance(p=2)
        >>> input1 = torch.randn(100, 128)
        >>> input2 = torch.randn(100, 128)
        >>> output = pdist(input1, input2)
    )normepskeepdimr	   r
   r   preturnNc                 L    t         |           || _        || _        || _        y N)super__init__r	   r
   r   )selfr   r
   r   	__class__s       L/var/www/html/venv/lib/python3.12/site-packages/torch/nn/modules/distance.pyr   zPairwiseDistance.__init__1   s%     		    x1x2c                 p    t        j                  ||| j                  | j                  | j                        S r   )Fpairwise_distancer	   r
   r   r   r   r   s      r   forwardzPairwiseDistance.forward9   s'    ""2r499dhhMMr   )g       @gư>F)__name__
__module____qualname____doc____constants__float__annotations__boolr   r   r   __classcell__r   s   @r   r   r   
   si    B /M
K	JM BG#(:>	N& Nf N Nr   c                   d     e Zd ZU dZddgZeed<   eed<   d
dededdf fdZde	de	de	fd	Z
 xZS )r   a  Returns cosine similarity between :math:`x_1` and :math:`x_2`, computed along `dim`.

    .. math ::
        \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}.

    Args:
        dim (int, optional): Dimension where cosine similarity is computed. Default: 1
        eps (float, optional): Small value to avoid division by zero.
            Default: 1e-8
    Shape:
        - Input1: :math:`(\ast_1, D, \ast_2)` where D is at position `dim`
        - Input2: :math:`(\ast_1, D, \ast_2)`, same number of dimensions as x1, matching x1 size at dimension `dim`,
              and broadcastable with x1 at other dimensions.
        - Output: :math:`(\ast_1, \ast_2)`
    Examples::
        >>> input1 = torch.randn(100, 128)
        >>> input2 = torch.randn(100, 128)
        >>> cos = nn.CosineSimilarity(dim=1, eps=1e-6)
        >>> output = cos(input1, input2)
    dimr
   r   Nc                 >    t         |           || _        || _        y r   )r   r   r(   r
   )r   r(   r
   r   s      r   r   zCosineSimilarity.__init__W   s    r   r   r   c                 Z    t        j                  ||| j                  | j                        S r   )r   cosine_similarityr(   r
   r   s      r   r   zCosineSimilarity.forward\   s!    ""2r488TXX>>r   )r   g:0yE>)r   r   r   r    r!   intr#   r"   r   r   r   r%   r&   s   @r   r   r   =   sQ    * ENM	H	JC % 4 
?& ?f ? ?r   )torch.nn.functionalnn
functionalr   torchr   moduler   __all__r   r    r   r   <module>r4      s9        1
20Nv 0Nf ?v  ?r   