
    קg0                     |    d dl Z d dlmZmZ d dlZd dlmZ d dlmZ d dl	m
Z
mZ d dlmZ dgZ G d de          ZdS )	    N)NumberReal)constraints)ExponentialFamily)_standard_normalbroadcast_all)_sizeNormalc                   L    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ed             Zd fd
	Zd fd	Z ej                    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
   a+  
    Creates a normal (also called Gaussian) distribution parameterized by
    :attr:`loc` and :attr:`scale`.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Normal(torch.tensor([0.0]), torch.tensor([1.0]))
        >>> m.sample()  # normally distributed with loc=0 and scale=1
        tensor([ 0.1046])

    Args:
        loc (float or Tensor): mean of the distribution (often referred to as mu)
        scale (float or Tensor): standard deviation of the distribution
            (often referred to as sigma)
    )locscaleTr   c                     | j         S Nr   selfs    V/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/distributions/normal.pymeanzNormal.mean%   	    x    c                     | j         S r   r   r   s    r   modezNormal.mode)   r   r   c                     | j         S r   )r   r   s    r   stddevzNormal.stddev-   s
    zr   c                 6    | j                             d          S N   )r   powr   s    r   variancezNormal.variance1   s    {q!!!r   Nc                 6   t          ||          \  | _        | _        t          |t                    r)t          |t                    rt          j                    }n| j                                        }t                      	                    ||           d S )Nvalidate_args)
r   r   r   
isinstancer   torchSizesizesuper__init__)r   r   r   r"   batch_shape	__class__s        r   r(   zNormal.__init__5   s}    ,S%88$*c6"" 	*z%'@'@ 	**,,KK(--//KMBBBBBr   c                 N   |                      t          |          }t          j        |          }| j                            |          |_        | j                            |          |_        t          t          |                              |d           | j	        |_	        |S )NFr!   )
_get_checked_instancer
   r$   r%   r   expandr   r'   r(   _validate_args)r   r)   	_instancenewr*   s       r   r-   zNormal.expand=   s    ((;;j--(//+..J%%k22	fc##Ku#EEE!0
r   c                    |                      |          }t          j                    5  t          j        | j                            |          | j                            |                    cd d d            S # 1 swxY w Y   d S r   )_extended_shaper$   no_gradnormalr   r-   r   )r   sample_shapeshapes      r   samplezNormal.sampleF   s    $$\22]__ 	R 	R< 6 6
8I8I%8P8PQQ	R 	R 	R 	R 	R 	R 	R 	R 	R 	R 	R 	R 	R 	R 	R 	R 	R 	Rs   AA;;A?A?r5   returnc                     |                      |          }t          || j        j        | j        j                  }| j        || j        z  z   S )N)dtypedevice)r2   r   r   r:   r;   r   )r   r5   r6   epss       r   rsamplezNormal.rsampleK   sE    $$\22uDHN48?SSSx#
***r   c                 |   | j         r|                     |           | j        dz  }t          | j        t                    rt          j        | j                  n| j                                        }|| j        z
  dz   d|z  z  |z
  t          j        t          j        dt
          j	        z                      z
  S r   )
r.   _validate_sampler   r#   r   mathlogr   sqrtpi)r   valuevar	log_scales       r   log_probzNormal.log_probP   s     	)!!%(((j!m$.tz4$@$@VDHTZ   djnnFVFV 	 txA%&!c'2htyTW--../	
r   c                     | j         r|                     |           ddt          j        || j        z
  | j                                        z  t          j        d          z            z   z  S )N      ?   r   )	r.   r?   r$   erfr   r   
reciprocalr@   rB   r   rD   s     r   cdfz
Normal.cdf^   sh     	)!!%(((	548+tz/D/D/F/FFSTUVVV
 	
r   c                     | j         | j        t          j        d|z  dz
            z  t	          j        d          z  z   S )Nr   rJ   )r   r   r$   erfinvr@   rB   rM   s     r   icdfzNormal.icdfe   s5    x$*u|AIM'B'BBTYq\\QQQr   c                     ddt          j        dt           j        z            z  z   t          j        | j                  z   S )NrI   r   )r@   rA   rC   r$   r   r   s    r   entropyzNormal.entropyh   s3    S48AK000059TZ3H3HHHr   c                     | j         | j                            d          z  d| j                            d                                          z  fS )Nr   g      )r   r   r   rL   r   s    r   _natural_paramszNormal._natural_paramsk   sA    4:>>!,,,dTZ^^A5F5F5Q5Q5S5S.STTr   c                     d|                     d          z  |z  dt          j        t          j         |z            z  z   S )Ng      пr   rI   )r   r$   rA   r@   rC   )r   xys      r   _log_normalizerzNormal._log_normalizero   s8    quuQxx!#cEItwhl,C,C&CCCr   r   )__name__
__module____qualname____doc__r   realpositivearg_constraintssupporthas_rsample_mean_carrier_measurepropertyr   r   r   r   r(   r-   r$   r%   r7   r	   Tensorr=   rG   rN   rQ   rS   rU   rY   __classcell__)r*   s   @r   r
   r
      s          *.9MNNOGK  X   X   X " " X"C C C C C C      #-%*,, R R R R
 -7EJLL + +E +U\ + + + +

 
 

 
 
R R RI I I U U XUD D D D D D Dr   )r@   numbersr   r   r$   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   r   torch.typesr	   __all__r
    r   r   <module>rn      s                      + + + + + + < < < < < < E E E E E E E E       *aD aD aD aD aD aD aD aD aD aDr   