
    /j
                     l    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 dlm	Z	 dgZ
 G d de          ZdS )	    N)Tensor)constraints)Gamma)TransformedDistribution)PowerTransformInverseGammac            	       $    e Zd ZU dZej        ej        dZej        ZdZe	e
d<   	 ddeez  deez  dedz  d	df fd
Zd fd	Zed	efd            Zed	efd            Zed	efd            Zed	efd            Zed	efd            Zd Z xZS )r   a  
    Creates an inverse gamma distribution parameterized by :attr:`concentration` and :attr:`rate`
    where::

        X ~ Gamma(concentration, rate)
        Y = 1 / X ~ InverseGamma(concentration, rate)

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
        >>> m = InverseGamma(torch.tensor([2.0]), torch.tensor([3.0]))
        >>> m.sample()
        tensor([ 1.2953])

    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)
    )concentrationrateT	base_distNr
   r   validate_argsreturnc                     t          |||          }|j                            d           }t                                          |t          |          |           d S )N)r    )r   r   new_onessuper__init__r   )selfr
   r   r   r   neg_one	__class__s         f/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/torch/distributions/inverse_gamma.pyr   zInverseGamma.__init__.   sk     -]KKK	>**2...~g..m 	 	
 	
 	
 	
 	
    c                     |                      t          |          }t                                          ||          S )N)	_instance)_get_checked_instancer   r   expand)r   batch_shaper   newr   s       r   r   zInverseGamma.expand:   s2    ((yAAww~~kS~999r   c                     | j         j        S N)r   r
   r   s    r   r
   zInverseGamma.concentration>   s    ~++r   c                     | j         j        S r    )r   r   r!   s    r   r   zInverseGamma.rateB   s    ~""r   c                 x    | j         | j        dz
  z  }t          j        | j        dk    |t          j                  S N   )r   r
   torchwhereinfr   results     r   meanzInverseGamma.meanF   s4    d0145{4-1659EEEr   c                 &    | j         | j        dz   z  S r$   )r   r
   r!   s    r   modezInverseGamma.modeK   s    yD.233r   c                     | j                                         | j        dz
                                  | j        dz
  z  z  }t          j        | j        dk    |t          j                  S )Nr%      )r   squarer
   r&   r'   r(   r)   s     r   variancezInverseGamma.varianceO   s^    !!##!#++--1Ca1GH
 {4-1659EEEr   c                     | j         | j                                        z   | j                                         z   d| j         z   | j                                         z  z
  S r$   )r
   r   loglgammadigammar!   s    r   entropyzInverseGamma.entropyV   s]    immoo ''))* 4%%);)C)C)E)EEF	
r   r    )__name__
__module____qualname____doc__r   positivearg_constraintssupporthas_rsampler   __annotations__r   floatboolr   r   propertyr
   r   r+   r-   r1   r6   __classcell__)r   s   @r   r   r      s         , %-$ O
 "GK &*	

 

~

 un

 d{	


 


 

 

 

 

 

: : : : : : ,v , , , X, #f # # # X# Ff F F F XF 4f 4 4 4 X4 F& F F F XF
 
 
 
 
 
 
r   )r&   r   torch.distributionsr   torch.distributions.gammar   ,torch.distributions.transformed_distributionr   torch.distributions.transformsr   __all__r   r   r   r   <module>rI      s           + + + + + + + + + + + + P P P P P P 9 9 9 9 9 9 
N
 N
 N
 N
 N
* N
 N
 N
 N
 N
r   