
    rh                     p    d dl mZmZ d dlmZ d dlmZ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	)
    )OptionalUnion)Tensor)constraintsIndependent)Normal)TransformedDistribution)StickBreakingTransformLogisticNormalc            	            e Zd ZU dZej
                  ej                  dZej                  Z	dZ
ee   ed<   	 ddeeef   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 xZS )r   a9  
    Creates a logistic-normal distribution parameterized by :attr:`loc` and :attr:`scale`
    that define the base `Normal` distribution transformed with the
    `StickBreakingTransform` such that::

        X ~ LogisticNormal(loc, scale)
        Y = log(X / (1 - X.cumsum(-1)))[..., :-1] ~ Normal(loc, scale)

    Args:
        loc (float or Tensor): mean of the base distribution
        scale (float or Tensor): standard deviation of the base distribution

    Example::

        >>> # logistic-normal distributed with mean=(0, 0, 0) and stddev=(1, 1, 1)
        >>> # of the base Normal distribution
        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = LogisticNormal(torch.tensor([0.0] * 3), torch.tensor([1.0] * 3))
        >>> m.sample()
        tensor([ 0.7653,  0.0341,  0.0579,  0.1427])

    )locscaleT	base_distNr   r   validate_argsreturnc                     t        |||      }|j                  s|j                  dg      }t        |   |t               |       y )N)r      )r   batch_shapeexpandsuper__init__r
   )selfr   r   r   r   	__class__s        v/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/torch/distributions/logistic_normal.pyr   zLogisticNormal.__init__+   sK     3]C	$$!((!-I-/} 	 	
    c                 R    | j                  t        |      }t        |   ||      S )N)	_instance)_get_checked_instancer   r   r   )r   r   r   newr   s       r   r   zLogisticNormal.expand8   s(    ((Cw~kS~99r   c                 B    | j                   j                   j                  S N)r   r   r   s    r   r   zLogisticNormal.loc<   s    ~~''+++r   c                 B    | j                   j                   j                  S r!   )r   r   r"   s    r   r   zLogisticNormal.scale@   s    ~~''---r   r!   )__name__
__module____qualname____doc__r   realpositivearg_constraintssimplexsupporthas_rsampler   r   __annotations__r   r   floatr   boolr   r   propertyr   r   __classcell__)r   s   @r   r   r      s    . *..9M9MNO!!GK6"" )-	
65=!
 VU]#
  ~	

 

: ,V , , .v . .r   N)typingr   r   torchr   torch.distributionsr   r   torch.distributions.normalr   ,torch.distributions.transformed_distributionr	   torch.distributions.transformsr
   __all__r    r   r   <module>r;      s.    "  8 - P A 
4., 4.r   