
    '}h                     ^    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	d Z
 G d de      Zy)	    )NumberN)constraints)ExponentialFamily)broadcast_allGammac                 ,    t        j                  |       S N)torch_standard_gamma)concentrations    X/var/www/html/test/engine/venv/lib/python3.12/site-packages/torch/distributions/gamma.pyr   r      s      //    c                       e Zd ZdZej
                  ej
                  dZej                  ZdZ	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ed        Zd Zd Z xZS )r   aC  
    Creates a Gamma distribution parameterized by shape :attr:`concentration` and :attr:`rate`.

    Example::

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

    Args:
        concentration (float or Tensor): shape parameter of the distribution
            (often referred to as alpha)
        rate (float or Tensor): rate = 1 / scale of the distribution
            (often referred to as beta)
    r   rateTr   c                 4    | j                   | j                  z  S r	   r   selfs    r   meanz
Gamma.mean(   s    !!DII--r   c                 Z    | j                   dz
  | j                  z  j                  d      S )N   r   min)r   r   clampr   s    r   modez
Gamma.mode,   s*    ##a'4994;;;BBr   c                 R    | j                   | j                  j                  d      z  S )N   )r   r   powr   s    r   variancezGamma.variance0   s     !!DIIMM!$444r   c                     t        ||      \  | _        | _        t        |t              r%t        |t              rt        j                         }n| j                  j                         }t        | %  ||       y )Nvalidate_args)
r   r   r   
isinstancer   r
   Sizesizesuper__init__)r   r   r   r"   batch_shape	__class__s        r   r'   zGamma.__init__4   s]    (5mT(J%DImV,D&1I**,K,,113KMBr   c                 *   | j                  t        |      }t        j                  |      }| j                  j                  |      |_        | j                  j                  |      |_        t        t        |#  |d       | j                  |_	        |S )NFr!   )
_get_checked_instancer   r
   r$   r   expandr   r&   r'   _validate_args)r   r(   	_instancenewr)   s       r   r,   zGamma.expand<   sy    ((	:jj- ..55kB99##K0eS";e"D!00
r   c                 6   | j                  |      }t        | j                  j                  |            | j                  j                  |      z  }|j                         j                  t        j                  |j                        j                         |S )Nr   )_extended_shaper   r   r,   r   detachclamp_r
   finfodtypetiny)r   sample_shapeshapevalues       r   rsamplezGamma.rsampleE   s    $$\2 2 2 9 9% @ADIIDTDTE
 
 	EKK(-- 	 	
 r   c                    t        j                  || j                  j                  | j                  j                        }| j
                  r| j                  |       t        j                  | j                  | j                        t        j                  | j                  dz
  |      z   | j                  |z  z
  t        j                  | j                        z
  S )N)r5   devicer   )
r
   	as_tensorr   r5   r<   r-   _validate_samplexlogyr   lgammar   r9   s     r   log_probzGamma.log_probO   s    TYY__TYYEUEUV!!%(KK**DII6kk$,,q0%89ii%  ll4--./	
r   c                     | j                   t        j                  | j                        z
  t        j                  | j                         z   d| j                   z
  t        j
                  | j                         z  z   S )Ng      ?)r   r
   logr   r@   digammar   s    r   entropyzGamma.entropyZ   sf    ii		"#ll4--./ T'''5==9K9K+LLM	
r   c                 :    | j                   dz
  | j                   fS Nr   r   r   s    r   _natural_paramszGamma._natural_paramsb   s    ""Q&
33r   c                     t        j                  |dz         |dz   t        j                  |j                                z  z   S rH   )r
   r@   rD   
reciprocal)r   xys      r   _log_normalizerzGamma._log_normalizerf   s4    ||AE"a!euyy!,,./I%IIIr   c                     | j                   r| j                  |       t        j                  j	                  | j
                  | j                  |z        S r	   )r-   r>   r
   specialgammaincr   r   rA   s     r   cdfz	Gamma.cdfi   s?    !!%(}}%%d&8&8$))e:KLLr   r	   )__name__
__module____qualname____doc__r   positivearg_constraintsnonnegativesupporthas_rsample_mean_carrier_measurepropertyr   r   r   r'   r,   r
   r$   r:   rB   rF   rI   rN   rR   __classcell__)r)   s   @r   r   r      s    " %--$$O %%GK. . C C 5 5C $.5::< 	

 4 4JMr   )numbersr   r
   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   __all__r   r    r   r   <module>re      s1      + < 3)0]M ]Mr   