
    Ǆg	                     d    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y)	    )NumberN)constraints)ExponentialFamily)broadcast_all)_sizeExponentialc                        e Zd ZdZdej
                  iZej                  Zd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ed        Zd Z xZS )r   an  
    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    | j                   j                         S Nr
   
reciprocalselfs    g/home/mcse/projects/flask_80/flask-venv/lib/python3.12/site-packages/torch/distributions/exponential.pymeanzExponential.mean!       yy##%%    c                 @    t        j                  | j                        S r   )torch
zeros_liker
   r   s    r   modezExponential.mode%   s    		**r   c                 6    | j                   j                         S r   r   r   s    r   stddevzExponential.stddev)   r   r   c                 8    | j                   j                  d      S )N)r
   powr   s    r   variancezExponential.variance-   s    yy}}R  r   c                     t        |      \  | _        t        |t              rt	        j
                         n| j                  j                         }t        | !  ||       y )Nvalidate_args)	r   r
   
isinstancer   r   Sizesizesuper__init__)r   r
   r!   batch_shape	__class__s       r   r&   zExponential.__init__1   sD    $T*&0v&>ejjlDIINNDTMBr   c                     | j                  t        |      }t        j                  |      }| j                  j                  |      |_        t        t        |  |d       | j                  |_        |S )NFr    )	_get_checked_instancer   r   r#   r
   expandr%   r&   _validate_args)r   r'   	_instancenewr(   s       r   r+   zExponential.expand6   s`    ((i@jj-99##K0k3(E(J!00
r   sample_shapereturnc                     | j                  |      }| j                  j                  |      j                         | j                  z  S r   )_extended_shaper
   r.   exponential_)r   r/   shapes      r   rsamplezExponential.rsample>   s7    $$\2yy}}U#002TYY>>r   c                     | j                   r| j                  |       | j                  j                         | j                  |z  z
  S r   )r,   _validate_sampler
   logr   values     r   log_probzExponential.log_probB   s7    !!%(yy}}U!222r   c                     | j                   r| j                  |       dt        j                  | j                   |z        z
  S )N   )r,   r7   r   expr
   r9   s     r   cdfzExponential.cdfG   s8    !!%(599diiZ%/000r   c                 J    t        j                  |        | j                  z  S r   )r   log1pr
   r9   s     r   icdfzExponential.icdfL   s    UF##dii//r   c                 F    dt        j                  | j                        z
  S )Ng      ?)r   r8   r
   r   s    r   entropyzExponential.entropyO   s    UYYtyy)))r   c                     | j                    fS r   )r
   r   s    r   _natural_paramszExponential._natural_paramsR   s    
}r   c                 0    t        j                  |        S r   )r   r8   )r   xs     r   _log_normalizerzExponential._log_normalizerV   s    		1"~r   r   )__name__
__module____qualname____doc__r   positivearg_constraintsnonnegativesupporthas_rsample_mean_carrier_measurepropertyr   r   r   r   r&   r+   r   r#   r   Tensorr5   r;   r?   rB   rD   rF   rI   __classcell__)r(   s   @r   r   r      s     {334O%%GK& & + + & & ! !C
 -7EJJL ?E ?U\\ ?3
1
0*  r   )numbersr   r   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   torch.typesr   __all__r    r   r   <module>r^      s/      + < 3  /I# Ir   