
    rhE	                         d dl Z d dlmZmZ d dlZd dlmZ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y)
    N)OptionalUnion)infTensor)constraints)Normal)TransformedDistribution)AbsTransform
HalfNormalc                       e Zd ZU dZdej
                  iZej                  ZdZ	e
ed<   	 ddeeef   dee   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d Zd Zd Zd Z xZS )r   a  
    Creates a half-normal distribution parameterized by `scale` where::

        X ~ Normal(0, scale)
        Y = |X| ~ HalfNormal(scale)

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = HalfNormal(torch.tensor([1.0]))
        >>> m.sample()  # half-normal distributed with scale=1
        tensor([ 0.1046])

    Args:
        scale (float or Tensor): scale of the full Normal distribution
    scaleT	base_distNvalidate_argsreturnc                 V    t        d|d      }t        | 	  |t               |       y )Nr   F)r   )r   super__init__r
   )selfr   r   r   	__class__s       r/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/torch/distributions/half_normal.pyr   zHalfNormal.__init__'   s)    
 1e59	LN-P    c                 R    | j                  t        |      }t        |   ||      S )N)	_instance)_get_checked_instancer   r   expand)r   batch_shaper   newr   s       r   r   zHalfNormal.expand/   s(    ((Y?w~kS~99r   c                 .    | j                   j                  S N)r   r   r   s    r   r   zHalfNormal.scale3   s    ~~###r   c                 h    | j                   t        j                  dt        j                  z        z  S N   )r   mathsqrtpir    s    r   meanzHalfNormal.mean7   s"    zzDIIa$''k222r   c                 @    t        j                  | j                        S r   )torch
zeros_liker   r    s    r   modezHalfNormal.mode;   s    

++r   c                 f    | j                   j                  d      ddt        j                  z  z
  z  S Nr#      )r   powr$   r&   r    s    r   variancezHalfNormal.variance?   s&    zz~~a ADGGO44r   c                     | j                   r| j                  |       | j                  j                  |      t	        j
                  d      z   }t        j                  |dk\  |t               }|S )Nr#   r   )	_validate_args_validate_sampler   log_probr$   logr)   wherer   )r   valuer4   s      r   r4   zHalfNormal.log_probC   sW    !!%(>>**51DHHQK?;;uz8cT:r   c                 ~    | j                   r| j                  |       d| j                  j                  |      z  dz
  S r-   )r2   r3   r   cdf)r   r7   s     r   r9   zHalfNormal.cdfJ   s8    !!%(4>>%%e,,q00r   c                 D    | j                   j                  |dz   dz        S )Nr.   r#   )r   icdf)r   probs     r   r;   zHalfNormal.icdfO   s    ~~""D1H>22r   c                 b    | j                   j                         t        j                  d      z
  S r"   )r   entropyr$   r5   r    s    r   r>   zHalfNormal.entropyR   s"    ~~%%'$((1+55r   r   )__name__
__module____qualname____doc__r   positivearg_constraintsnonnegativesupporthas_rsampler   __annotations__r   r   floatr   boolr   r   propertyr   r'   r+   r0   r4   r9   r;   r>   __classcell__)r   s   @r   r   r      s    "  4 45O%%GK
 )-QVU]#Q  ~Q 
	Q: $v $ $ 3f 3 3 ,f , , 5& 5 51
36r   )r$   typingr   r   r)   r   r   torch.distributionsr   torch.distributions.normalr   ,torch.distributions.transformed_distributionr	   torch.distributions.transformsr
   __all__r    r   r   <module>rT      s5     "   + - P 7 .C6( C6r   