
    קg                     l    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 dl	m
Z
 dgZ G d de          ZdS )	    )NumberN)constraints)Distribution)broadcast_all)_size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ed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    W/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/distributions/laplace.pymeanzLaplace.mean!   	    x    c                     | j         S r   r   r   s    r   modezLaplace.mode%   r   r   c                 <    d| 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    $*$$r   Nc                 6   t          ||          \  | _        | _        t          |t                    r)t          |t                    rt          j                    }n| j                                        }t                      	                    ||           d S )Nvalidate_args)
r   r
   r   
isinstancer   torchSizesizesuper__init__)r   r
   r   r    batch_shape	__class__s        r   r&   zLaplace.__init__1   s}    ,S%88$*c6"" 	*z%'@'@ 	**,,KK(--//KMBBBBBr   c                 N   |                      t          |          }t          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.expand9   s    (()<<j--(//+..J%%k22	gs$$[$FFF!0
r   sample_shapereturnc                    |                      |          }t          j        | j        j                  }t          j                                        rt          j        || j        j        | j        j                  dz  dz
  }| j        | j	        |
                                z  t          j        |                                                    |j                             z  z
  S | j                            |                              |j        dz
  d          }| j        | j	        |
                                z  t          j        |                                           z  z
  S )N)dtypedevicer      )min)_extended_shaper"   finfor
   r2   _C_get_tracing_staterandr3   r   signlog1pabsclamptinyr.   uniform_eps)r   r/   shaper7   us        r   rsamplezLaplace.rsampleB   s   $$\22DHN++8&&(( 	
5txOOORSSVWWA8dj166883ek5:...7 7    HLL((Q:: x$*qvvxx/%+quuwwh2G2GGGGr   c                     | j         r|                     |           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_probP   sW     	)!!%(((	!dj.)))EIedh6F,G,G$*,TTTr   c                     | j         r|                     |           dd|| j        z
                                  z  t	          j        || j        z
                                   | j        z            z  z
  S )N      ?)r,   rF   r
   r;   r"   expm1r=   r   rH   s     r   cdfzLaplace.cdfU   s{     	)!!%(((SEDH,22444u{dh##%%%
28
 8
 
 
 	
r   c                     |dz
  }| j         | j        |                                z  t          j        d|                                z            z  z
  S )NrL   )r
   r   r;   r"   r<   r=   )r   rI   terms      r   icdfzLaplace.icdf\   sB    s{x$*{{}}4u{2

?7S7SSSSr   c                 @    dt          j        d| j        z            z   S )Nr4   r   )r"   rG   r   r   s    r   entropyzLaplace.entropy`   s    59Q^,,,,r   r   )__name__
__module____qualname____doc__r   realpositivearg_constraintssupporthas_rsamplepropertyr   r   r   r   r&   r+   r"   r#   r   TensorrD   rJ   rN   rR   rT   __classcell__)r(   s   @r   r   r      sv         *.9MNNOGK  X   X % % X% % % X%C C C C C C      -7EJLL H HE HU\ H H H HU U U

 
 
T T T- - - - - - -r   )numbersr   r"   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr   torch.typesr   __all__r    r   r   <module>rh      s           + + + + + + 9 9 9 9 9 9 3 3 3 3 3 3       +S- S- S- S- S-l S- S- S- S- S-r   