
    IЦi                     l    S SK r S SKrS SKJr  S SKJr  S SKJr  S SKJr  S SK	J
r
  S/r " S S\5      rg)	    N)inf)constraints)Normal)TransformedDistribution)AbsTransform
HalfNormalc                      ^  \ rS rSrSrS\R                  0r\R                  r	Sr
SU 4S jjrSU 4S jjr\S 5       r\S 5       r\S	 5       r\S
 5       rS rS rS rS rSrU =r$ )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
scaleTc                 J   > [        SUSS9n[        TU ]	  U[        5       US9  g )Nr   F)validate_args)r   super__init__r   )selfr   r   	base_dist	__class__s       ^/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/torch/distributions/half_normal.pyr   HalfNormal.__init__$   s'    1e59	LN-P    c                 J   > U R                  [        U5      n[        TU ]  XS9$ )N)	_instance)_get_checked_instancer   r   expand)r   batch_shaper   newr   s       r   r   HalfNormal.expand(   s&    ((Y?w~k~99r   c                 .    U R                   R                  $ N)r   r   r   s    r   r   HalfNormal.scale,   s    ~~###r   c                 j    U R                   [        R                  " S[        R                  -  5      -  $ N   )r   mathsqrtpir   s    r   meanHalfNormal.mean0   s"    zzDIIa$''k222r   c                 B    [         R                  " U R                  5      $ r   )torch
zeros_liker   r   s    r   modeHalfNormal.mode4   s    

++r   c                 f    U R                   R                  S5      SS[        R                  -  -
  -  $ Nr#      )r   powr$   r&   r   s    r   varianceHalfNormal.variance8   s&    zz~~a ADGGO44r   c                     U R                   (       a  U R                  U5        U R                  R                  U5      [        R
                  " S5      -   n[        R                  " US:  U[        * 5      nU$ )Nr#   r   )	_validate_args_validate_sampler   log_probr$   logr*   wherer   )r   valuer7   s      r   r7   HalfNormal.log_prob<   sW    !!%(>>**51DHHQK?;;uz8cT:r   c                     U R                   (       a  U R                  U5        SU R                  R                  U5      -  S-
  $ r/   )r5   r6   r   cdf)r   r:   s     r   r=   HalfNormal.cdfC   s8    !!%(4>>%%e,,q00r   c                 D    U R                   R                  US-   S-  5      $ )Nr0   r#   )r   icdf)r   probs     r   r@   HalfNormal.icdfH   s    ~~""D1H>22r   c                 d    U R                   R                  5       [        R                  " S5      -
  $ r"   )r   entropyr$   r8   r   s    r   rD   HalfNormal.entropyK   s"    ~~%%'$((1+55r    r   )__name__
__module____qualname____firstlineno____doc__r   positivearg_constraintsnonnegativesupporthas_rsampler   r   propertyr   r'   r,   r2   r7   r=   r@   rD   __static_attributes____classcell__)r   s   @r   r   r      s       4 45O%%GKQ: $ $ 3 3 , , 5 51
36 6r   )r$   r*   r   torch.distributionsr   torch.distributions.normalr   ,torch.distributions.transformed_distributionr   torch.distributions.transformsr   __all__r   rF   r   r   <module>rY      s0       + - P 7 .=6( =6r   