
    IЦi                     |    S SK r S SKJr  S SKrS SKJrJr  S SKJr  S SKJ	r	  S SK
Jr  S SKJr  S/r " S	 S\	5      rg)
    N)Number)infnan)constraints)Distribution)broadcast_all)_sizeCauchyc                      ^  \ rS rSrSr\R                  \R                  S.r\R                  r	Sr
SU 4S jjrSU 4S jjr\S 5       r\S 5       r\S	 5       r\R$                  " 5       4S
\S\R(                  4S jjrS rS rS rS rSrU =r$ )r
      a  
Samples from a Cauchy (Lorentz) distribution. The distribution of the ratio of
independent normally distributed random variables with means `0` follows a
Cauchy distribution.

Example::

    >>> # xdoctest: +IGNORE_WANT("non-deterministic")
    >>> m = Cauchy(torch.tensor([0.0]), torch.tensor([1.0]))
    >>> m.sample()  # sample from a Cauchy distribution with loc=0 and scale=1
    tensor([ 2.3214])

Args:
    loc (float or Tensor): mode or median of the distribution.
    scale (float or Tensor): half width at half maximum.
)locscaleTc                   > [        X5      u  U l        U l        [        U[        5      (       a+  [        U[        5      (       a  [
        R                  " 5       nOU R                  R                  5       n[        TU ]%  XCS9  g )Nvalidate_args)
r   r   r   
isinstancer   torchSizesizesuper__init__)selfr   r   r   batch_shape	__class__s        Y/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/torch/distributions/cauchy.pyr   Cauchy.__init__%   sY    ,S8$*c6""z%'@'@**,K((--/KB    c                 &  > U R                  [        U5      n[        R                  " U5      nU R                  R                  U5      Ul        U R                  R                  U5      Ul        [        [        U]#  USS9  U R                  Ul	        U$ )NFr   )
_get_checked_instancer
   r   r   r   expandr   r   r   _validate_args)r   r   	_instancenewr   s       r   r    Cauchy.expand-   st    ((;jj-((//+.JJ%%k2	fc#Ku#E!00
r   c                     [         R                  " U R                  5       [        U R                  R
                  U R                  R                  S9$ N)dtypedevice)r   full_extended_shaper   r   r'   r(   r   s    r   meanCauchy.mean6   5    zz  "Ctxx~~dhhoo
 	
r   c                     U R                   $ N)r   r+   s    r   modeCauchy.mode<   s    xxr   c                     [         R                  " U R                  5       [        U R                  R
                  U R                  R                  S9$ r&   )r   r)   r*   r   r   r'   r(   r+   s    r   varianceCauchy.variance@   r.   r   sample_shapereturnc                     U R                  U5      nU R                  R                  U5      R                  5       nU R                  X0R                  -  -   $ r0   )r*   r   r#   cauchy_r   )r   r6   shapeepss       r   rsampleCauchy.rsampleF   sC    $$\2hhll5!))+xx#

***r   c                     U R                   (       a  U R                  U5        [        R                  " [        R                  5      * U R
                  R                  5       -
  XR                  -
  U R
                  -  S-  R                  5       -
  $ )N   )r!   _validate_samplemathlogpir   r   log1pr   values     r   log_probCauchy.log_probK   sj    !!%(XXdggjjnn!TZZ/A5<<>?	
r   c                     U R                   (       a  U R                  U5        [        R                  " XR                  -
  U R
                  -  5      [        R                  -  S-   $ Ng      ?)r!   r@   r   atanr   r   rA   rC   rE   s     r   cdf
Cauchy.cdfT   sF    !!%(zz588+tzz9:TWWDsJJr   c                     [         R                  " [        R                  US-
  -  5      U R                  -  U R
                  -   $ rJ   )r   tanrA   rC   r   r   rE   s     r   icdfCauchy.icdfY   s0    yyECK01DJJ>IIr   c                     [         R                  " S[         R                  -  5      U R                  R                  5       -   $ )N   )rA   rB   rC   r   r+   s    r   entropyCauchy.entropy\   s)    xxDGG$tzz~~'777r   r0   )__name__
__module____qualname____firstlineno____doc__r   realpositivearg_constraintssupporthas_rsampler   r    propertyr,   r1   r4   r   r   r	   Tensorr<   rG   rL   rP   rT   __static_attributes____classcell__)r   s   @r   r
   r
      s      *..9M9MNOGKC 
 

   
 

 -2JJL +E +U\\ +

K
J8 8r   )rA   numbersr   r   r   r   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr   torch.typesr	   __all__r
    r   r   <module>rk      s4        + 9 3  *M8\ M8r   