
    IЦi	                     d    S SK Jr  S SK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)	    )NumberN)constraints)ExponentialFamily)broadcast_all)_sizeExponentialc                   4  ^  \ rS rSrSrS\R                  0r\R                  r	Sr
Sr\S 5       r\S 5       r\S 5       r\S	 5       rSU 4S
 jjrSU 4S jjr\R(                  " 5       4S\S\R,                  4S jjrS rS rS rS r\S 5       rS rSrU =r$ )r      aJ  
Creates a Exponential distribution parameterized by :attr:`rate`.

Example::

    >>> # xdoctest: +IGNORE_WANT("non-deterministic")
    >>> m = Exponential(torch.tensor([1.0]))
    >>> m.sample()  # Exponential distributed with rate=1
    tensor([ 0.1046])

Args:
    rate (float or Tensor): rate = 1 / scale of the distribution
rateTr   c                 6    U R                   R                  5       $ Nr   
reciprocalselfs    ^/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/torch/distributions/exponential.pymeanExponential.mean!       yy##%%    c                 B    [         R                  " U R                  5      $ r   )torch
zeros_liker   r   s    r   modeExponential.mode%   s    		**r   c                 6    U R                   R                  5       $ r   r   r   s    r   stddevExponential.stddev)   r   r   c                 8    U R                   R                  S5      $ )N)r   powr   s    r   varianceExponential.variance-   s    yy}}R  r   c                    > [        U5      u  U l        [        U[        5      (       a  [        R
                  " 5       OU R                  R                  5       n[        TU ]!  X2S9  g )Nvalidate_args)	r   r   
isinstancer   r   Sizesizesuper__init__)r   r   r&   batch_shape	__class__s       r   r+   Exponential.__init__1   sE    $T*&0v&>&>ejjlDIINNDTBr   c                    > U R                  [        U5      n[        R                  " U5      nU R                  R                  U5      Ul        [        [        U]  USS9  U R                  Ul        U$ )NFr%   )	_get_checked_instancer   r   r(   r   expandr*   r+   _validate_args)r   r,   	_instancenewr-   s       r   r1   Exponential.expand6   s`    ((i@jj-99##K0k3(E(J!00
r   sample_shapereturnc                     U R                  U5      nU R                  R                  U5      R                  5       U R                  -  $ r   )_extended_shaper   r4   exponential_)r   r6   shapes      r   rsampleExponential.rsample>   s7    $$\2yy}}U#002TYY>>r   c                     U R                   (       a  U R                  U5        U R                  R                  5       U R                  U-  -
  $ r   )r2   _validate_sampler   logr   values     r   log_probExponential.log_probB   s7    !!%(yy}}U!222r   c                     U R                   (       a  U R                  U5        S[        R                  " U R                  * U-  5      -
  $ )N   )r2   r?   r   expr   rA   s     r   cdfExponential.cdfG   s8    !!%(599diiZ%/000r   c                 L    [         R                  " U* 5      * U R                  -  $ r   )r   log1pr   rA   s     r   icdfExponential.icdfL   s    UF##dii//r   c                 H    S[         R                  " U R                  5      -
  $ )Ng      ?)r   r@   r   r   s    r   entropyExponential.entropyO   s    UYYtyy)))r   c                     U R                   * 4$ r   r   r   s    r   _natural_paramsExponential._natural_paramsR   s    
}r   c                 2    [         R                  " U* 5      * $ r   )r   r@   )r   xs     r   _log_normalizerExponential._log_normalizerV   s    		1"~r   rR   r   ) __name__
__module____qualname____firstlineno____doc__r   positivearg_constraintsnonnegativesupporthas_rsample_mean_carrier_measurepropertyr   r   r   r"   r+   r1   r   r(   r   Tensorr<   rC   rH   rL   rO   rS   rW   __static_attributes____classcell__)r-   s   @r   r   r      s     {334O%%GK& & + + & & ! !C
 -2JJL ?E ?U\\ ?3
1
0*   r   )numbersr   r   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   torch.typesr   __all__r    r   r   <module>ro      s/      + < 3  /I# Ir   