o
    ,h                     @   s~   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	 d dl
mZmZ d dlmZmZ dgZG d	d de	ZdS )
    N)OptionalUnion)Tensor)constraints)ExponentialFamily)_standard_normalbroadcast_all)_Number_sizeNormalc                	       s(  e Zd ZdZejejdZejZdZ	dZ
edefddZedefdd	Zedefd
dZedefddZ	d'deeef deeef dee ddf fddZd' fdd	Ze fddZe fdedefddZdd Zdd Zdd  Zd!d" Zede eef fd#d$Z!d%d& Z"  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   returnc                 C      | j S Nr   self r   U/var/www/html/scripts/venv/lib/python3.10/site-packages/torch/distributions/normal.pymean'      zNormal.meanc                 C   r   r   r   r   r   r   r   mode+   r   zNormal.modec                 C   r   r   )r   r   r   r   r   stddev/   r   zNormal.stddevc                 C   s   | j dS N   )r   powr   r   r   r   variance3   s   zNormal.varianceNr   r   validate_argsc                    sN   t ||\| _| _t|trt|trt }n| j }t j	||d d S )Nr   )
r   r   r   
isinstancer	   torchSizesizesuper__init__)r   r   r   r   batch_shape	__class__r   r   r%   7   s
   

zNormal.__init__c                    sR   |  t|}t|}| j||_| j||_tt|j|dd | j	|_	|S )NFr   )
_get_checked_instancer   r!   r"   r   expandr   r$   r%   _validate_args)r   r&   	_instancenewr'   r   r   r*   D   s   
zNormal.expandc                 C   sR   |  |}t  t| j|| j|W  d    S 1 s"w   Y  d S r   )_extended_shaper!   no_gradnormalr   r*   r   )r   sample_shapeshaper   r   r   sampleM   s   

$zNormal.sampler1   c                 C   s0   |  |}t|| jj| jjd}| j|| j  S )N)dtypedevice)r.   r   r   r4   r5   r   )r   r1   r2   epsr   r   r   rsampleR   s   
zNormal.rsamplec                 C   sn   | j r| | | jd }t| jtrt| jn| j }|| j d  d|  | ttdtj	  S r   )
r+   _validate_sampler   r    r	   mathlogr   sqrtpi)r   valuevar	log_scaler   r   r   log_probW   s   


zNormal.log_probc                 C   s<   | j r| | ddt|| j | j  td   S )N      ?   r   )	r+   r8   r!   erfr   r   
reciprocalr9   r;   r   r=   r   r   r   cdfg   s
   
&z
Normal.cdfc                 C   s(   | j | jtd| d  td  S )Nr   rB   )r   r   r!   erfinvr9   r;   rE   r   r   r   icdfn      (zNormal.icdfc                 C   s$   ddt dt j   t| j S )NrA   r   )r9   r:   r<   r!   r   r   r   r   r   entropyq   s   $zNormal.entropyc                 C   s&   | j | jd d| jd  fS )Nr   g      )r   r   r   rD   r   r   r   r   _natural_paramst   s   &zNormal._natural_paramsc                 C   s(   d| d | dttj |   S )Ng      пr   rA   )r   r!   r:   r9   r<   )r   xyr   r   r   _log_normalizerx   rI   zNormal._log_normalizerr   )$__name__
__module____qualname____doc__r   realpositivearg_constraintssupporthas_rsample_mean_carrier_measurepropertyr   r   r   r   r   r   floatr   boolr%   r*   r!   r"   r3   r
   r7   r@   rF   rH   rJ   tuplerK   rN   __classcell__r   r   r'   r   r      sD    

	)r9   typingr   r   r!   r   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   r   torch.typesr	   r
   __all__r   r   r   r   r   <module>   s   