o
    !i                     @   sZ   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d Z
G d	d deZdS )
    )NumberN)constraints)ExponentialFamily)broadcast_allGammac                 C   s
   t | S N)torch_standard_gamma)concentration r   h/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/torch/distributions/gamma.pyr	      s   
r	   c                       s   e Zd ZdZejejdZejZdZ	dZ
edd Zedd Zed	d
 Zd fdd	Zd fdd	Ze fddZdd Zdd Zedd Zdd Zdd Z  ZS )r   aB  
    Creates a Gamma distribution parameterized by shape :attr:`concentration` and :attr:`rate`.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
        >>> 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                 C   s   | j | j S r   r   selfr   r   r   mean(   s   z
Gamma.meanc                 C   s   | j d | j jddS )N   r   min)r
   r   clampr   r   r   r   mode,   s   z
Gamma.modec                 C   s   | j | jd S )N   )r
   r   powr   r   r   r   variance0      zGamma.varianceNc                    sN   t ||\| _| _t|trt|trt }n| j }t j	||d d S )Nvalidate_args)
r   r
   r   
isinstancer   r   Sizesizesuper__init__)r   r
   r   r   batch_shape	__class__r   r   r!   4   s
   

zGamma.__init__c                    sR   |  t|}t|}| j||_| j||_tt|j|dd | j	|_	|S )NFr   )
Z_get_checked_instancer   r   r   r
   expandr   r    r!   _validate_args)r   r"   Z	_instancenewr#   r   r   r%   <   s   
zGamma.expandc                 C   sD   |  |}t| j|| j| }| jt|j	j
d |S )Nr   )Z_extended_shaper	   r
   r%   r   detachZclamp_r   ZfinfodtypeZtiny)r   Zsample_shapeshapevaluer   r   r   rsampleE   s   
zGamma.rsamplec                 C   s`   t j|| jj| jjd}| jr| | t | j| jt | jd | | j|  t 	| j S )N)r)   devicer   )
r   Z	as_tensorr   r)   r-   r&   _validate_sampleZxlogyr
   lgammar   r+   r   r   r   log_probO   s   

zGamma.log_probc                 C   s4   | j t| j t| j  d| j  t| j   S )Ng      ?)r
   r   logr   r/   Zdigammar   r   r   r   entropyZ   s   

zGamma.entropyc                 C   s   | j d | j fS Nr   r   r   r   r   r   _natural_paramsb   r   zGamma._natural_paramsc                 C   s&   t |d |d t |    S r4   )r   r/   r2   Z
reciprocal)r   xyr   r   r   _log_normalizerf   s   &zGamma._log_normalizerc                 C   s&   | j r| | tj| j| j| S r   )r&   r.   r   ZspecialZgammaincr
   r   r0   r   r   r   cdfi   s   
z	Gamma.cdfr   )__name__
__module____qualname____doc__r   ZpositiveZarg_constraintsZnonnegativeZsupportZhas_rsampleZ_mean_carrier_measurepropertyr   r   r   r!   r%   r   r   r,   r1   r3   r5   r8   r9   __classcell__r   r   r#   r   r      s.    


	

)numbersr   r   Ztorch.distributionsr   Ztorch.distributions.exp_familyr   Ztorch.distributions.utilsr   __all__r	   r   r   r   r   r   <module>   s    