
    &VjiE	                         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dS )
    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                     t          d|d          }t                                          |t                      |           d S )Nr   F)r   )r   super__init__r
   )selfr   r   r   	__class__s       Y/root/voice-cloning/.venv/lib/python3.11/site-packages/torch/distributions/half_normal.pyr   zHalfNormal.__init__'   sB    
 1e5999	LNN-PPPPP    c                     |                      t          |          }t                                          ||          S )N)	_instance)_get_checked_instancer   r   expand)r   batch_shaper   newr   s       r   r   zHalfNormal.expand/   s2    ((Y??ww~~kS~999r   c                     | j         j        S N)r   r   r   s    r   r   zHalfNormal.scale3   s    ~##r   c                 T    | j         t          j        dt          j        z            z  S N   )r   mathsqrtpir    s    r   meanzHalfNormal.mean7   s    zDIa$'k2222r   c                 4    t          j        | j                  S r   )torch
zeros_liker   r    s    r   modezHalfNormal.mode;   s    
+++r   c                 \    | j                             d          ddt          j        z  z
  z  S Nr#      )r   powr$   r&   r    s    r   variancezHalfNormal.variance?   s%    z~~a  ADGO44r   c                     | j         r|                     |           | j                            |          t	          j        d          z   }t          j        |dk    |t                     }|S )Nr#   r   )	_validate_args_validate_sampler   log_probr$   logr)   wherer   )r   valuer4   s      r   r4   zHalfNormal.log_probC   sa     	)!!%(((>**511DHQKK?;uz8cT::r   c                 z    | j         r|                     |           d| j                            |          z  dz
  S r-   )r2   r3   r   cdf)r   r7   s     r   r9   zHalfNormal.cdfJ   sA     	)!!%(((4>%%e,,,q00r   c                 B    | j                             |dz   dz            S )Nr.   r#   )r   icdf)r   probs     r   r;   zHalfNormal.icdfO   s     ~""D1H>222r   c                 ^    | j                                         t          j        d          z
  S r"   )r   entropyr$   r5   r    s    r   r>   zHalfNormal.entropyR   s#    ~%%''$(1++55r   r   )__name__
__module____qualname____doc__r   positivearg_constraintsnonnegativesupporthas_rsampler   __annotations__r   r   floatr   boolr   r   propertyr   r'   r+   r0   r4   r9   r;   r>   __classcell__)r   s   @r   r   r      s         "  45O%GK
 )-Q QVU]#Q  ~Q 
	Q Q Q Q Q Q: : : : : : $v $ $ $ X$ 3f 3 3 3 X3 ,f , , , X, 5& 5 5 5 X5  1 1 1
3 3 36 6 6 6 6 6 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      s     " " " " " " " "          + + + + + + - - - - - - P P P P P P 7 7 7 7 7 7 .C6 C6 C6 C6 C6( C6 C6 C6 C6 C6r   