
    sg0                     t    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y)	    N)NumberReal)constraints)ExponentialFamily)_standard_normalbroadcast_all)_sizeNormalc                   ^    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    M/var/www/html/venv/lib/python3.12/site-packages/torch/distributions/normal.pymeanzNormal.mean%       xx    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    zzr   c                 8    | j                   j                  d      S N   )r   powr   s    r   variancezNormal.variance1   s    {{q!!r   c                     t        ||      \  | _        | _        t        |t              r%t        |t              rt        j                         }n| j                  j                         }t        | %  ||       y )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   sW    ,S%8$*c6"z%'@**,K((--/KMBr   c                 *   | j                  t        |      }t        j                  |      }| j                  j                  |      |_        | j                  j                  |      |_        t        t        |#  |d       | j                  |_	        |S )NFr!   )
_get_checked_instancer
   r$   r%   r   expandr   r'   r(   _validate_args)r   r)   	_instancenewr*   s       r   r-   zNormal.expand=   st    ((;jj-((//+.JJ%%k2	fc#Ku#E!00
r   c                    | j                  |      }t        j                         5  t        j                  | j                  j                  |      | j                  j                  |            cd d d        S # 1 sw Y   y xY wr   )_extended_shaper$   no_gradnormalr   r-   r   )r   sample_shapeshapes      r   samplezNormal.sampleF   s^    $$\2]]_ 	R<< 6

8I8I%8PQ	R 	R 	Rs   AA88Br5   returnc                     | j                  |      }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   sH    $$\2uDHHNN488??Sxx#

***r   c                    | j                   r| j                  |       | j                  dz  }t        | j                  t              rt        j                  | j                        n| j                  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    !!%(jj!m$.tzz4$@DHHTZZ djjnnFV 	 txxA%&!c'2hhtyyTWW-./	
r   c                     | j                   r| j                  |       ddt        j                  || j                  z
  | j
                  j                         z  t        j                  d      z        z   z  S )N      ?   r   )	r.   r?   r$   erfr   r   
reciprocalr@   rB   r   rD   s     r   cdfz
Normal.cdf^   s`    !!%(		5488+tzz/D/D/FFSTUVV
 	
r   c                     | j                   | j                  t        j                  d|z  dz
        z  t	        j
                  d      z  z   S )Nr   rJ   )r   r   r$   erfinvr@   rB   rM   s     r   icdfzNormal.icdfe   s8    xx$**u||AIM'BBTYYq\QQQr   c                     ddt        j                  dt         j                  z        z  z   t        j                  | j                        z   S )NrI   r   )r@   rA   rC   r$   r   r   s    r   entropyzNormal.entropyh   s5    S488AK000599TZZ3HHHr   c                     | j                   | j                  j                  d      z  d| j                  j                  d      j                         z  fS )Nr   g      )r   r   r   rL   r   s    r   _natural_paramszNormal._natural_paramsk   s>    4::>>!,,dTZZ^^A5F5Q5Q5S.STTr   c                     d|j                  d      z  |z  dt        j                  t        j                   |z        z  z   S )Ng      пr   rI   )r   r$   rA   r@   rC   )r   xys      r   _log_normalizerzNormal._log_normalizero   s7    quuQx!#cEIItwwhl,C&CCCr   r   )__name__
__module____qualname____doc__r   realpositivearg_constraintssupporthas_rsample_mean_carrier_measurepropertyr   r   r   r   r(   r-   r$   r%   r7   r	   Tensorr=   rG   rN   rQ   rS   rU   rY   __classcell__)r*   s   @r   r
   r
      s      *..9M9MNOGK      " "C #-%**, R
 -7EJJL +E +U\\ +


RI U UDr   )r@   numbersr   r   r$   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   r   torch.typesr	   __all__r
    r   r   <module>rn      s4        + < E  *aD aDr   