
    '}h                     X    d dl mZ d dlZd dlmZ d dlmZ d dlmZ dgZ	 G d de      Z
y)    )NumberN)constraints)Distribution)broadcast_allLaplacec                        e Zd ZdZej
                  ej                  dZej
                  ZdZ	e
d        Ze
d        Ze
d        Ze
d        Zd fd	Zd fd		Z ej$                         fd
Zd Zd Zd Zd Z xZS )r   a  
    Creates a Laplace distribution parameterized by :attr:`loc` and :attr:`scale`.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Laplace(torch.tensor([0.0]), torch.tensor([1.0]))
        >>> m.sample()  # Laplace distributed with loc=0, scale=1
        tensor([ 0.1046])

    Args:
        loc (float or Tensor): mean of the distribution
        scale (float or Tensor): scale of the distribution
    )locscaleTc                     | j                   S Nr	   selfs    Z/var/www/html/test/engine/venv/lib/python3.12/site-packages/torch/distributions/laplace.pymeanzLaplace.mean       xx    c                     | j                   S r   r   r   s    r   modezLaplace.mode"   r   r   c                 >    d| j                   j                  d      z  S N   )r
   powr   s    r   variancezLaplace.variance&   s    4::>>!$$$r   c                      d| j                   z  S )Ng;f?)r
   r   s    r   stddevzLaplace.stddev*   s    $**$$r   c                     t        ||      \  | _        | _        t        |t              r%t        |t              rt        j                         }n| j                  j                         }t        | %  ||       y )Nvalidate_args)
r   r	   r
   
isinstancer   torchSizesizesuper__init__)r   r	   r
   r   batch_shape	__class__s        r   r%   zLaplace.__init__.   sW    ,S%8$*c6"z%'@**,K((--/KMBr   c                 *   | j                  t        |      }t        j                  |      }| j                  j                  |      |_        | j                  j                  |      |_        t        t        |#  |d       | j                  |_	        |S )NFr   )
_get_checked_instancer   r!   r"   r	   expandr
   r$   r%   _validate_args)r   r&   	_instancenewr'   s       r   r*   zLaplace.expand6   st    (()<jj-((//+.JJ%%k2	gs$[$F!00
r   c                    | j                  |      }t        j                  | j                  j                        }t        j
                  j                         rt        j                  || j                  j                  | j                  j                        dz  dz
  }| j                  | j                  |j                         z  t        j                  |j                         j                  |j                               z  z
  S | j                  j                  |      j!                  |j"                  dz
  d      }| j                  | j                  |j                         z  t        j                  |j                                z  z
  S )N)dtypedevicer      )min)_extended_shaper!   finfor	   r/   _C_get_tracing_staterandr0   r
   signlog1pabsclamptinyr-   uniform_eps)r   sample_shapeshaper4   us        r   rsamplezLaplace.rsample?   s   $$\2DHHNN+88&&(

5txxORSSVWWA88djj16683ekk5::..7    HHLL((Q: xx$**qvvx/%++quuwh2GGGGr   c                     | j                   r| j                  |       t        j                  d| j                  z         t        j
                  || j                  z
        | j                  z  z
  S r   )r+   _validate_sampler!   logr
   r:   r	   r   values     r   log_probzLaplace.log_probM   sS    !!%(		!djj.))EIIedhh6F,G$**,TTTr   c                     | j                   r| j                  |       dd|| j                  z
  j                         z  t	        j
                  || j                  z
  j                          | j                  z        z  z
  S )N      ?)r+   rD   r	   r8   r!   expm1r:   r
   rF   s     r   cdfzLaplace.cdfR   sp    !!%(SEDHH,2244u{{dhh##%%

28
 
 
 	
r   c                     |dz
  }| j                   | j                  |j                         z  t        j                  d|j                         z        z  z
  S )NrJ   )r	   r
   r8   r!   r9   r:   )r   rG   terms      r   icdfzLaplace.icdfY   sA    s{xx$**{{}4u{{2
?7SSSSr   c                 L    dt        j                  d| j                  z        z   S )Nr1   r   )r!   rE   r
   r   s    r   entropyzLaplace.entropy]   s    599Q^,,,r   r   )__name__
__module____qualname____doc__r   realpositivearg_constraintssupporthas_rsamplepropertyr   r   r   r   r%   r*   r!   r"   rB   rH   rL   rP   rR   __classcell__)r'   s   @r   r   r      s     *..9M9MNOGK    % % % %C $.5::< HU

T-r   )numbersr   r!   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr   __all__r    r   r   <module>rd      s)      + 9 3+S-l S-r   