Ë
    M²jÀ  ã                   ó`   — 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)	é    )ÚTensor)ÚconstraintsÚIndependent)ÚNormal)ÚTransformedDistribution)ÚStickBreakingTransformÚLogisticNormalc            	       óâ   ‡ — e Zd ZU dZej
                  ej                  dœZej                  Z	dZ
ee   ed<   	 ddeez  deez  dedz  d	dfˆ fd
„Zdˆ fd„	Zed	efd„«       Zed	efd„«       Zˆ 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])

    )ÚlocÚscaleTÚ	base_distNr   r   Úvalidate_argsÚreturnc                 ó’   •— t        |||¬«      }|j                  s|j                  dg«      }t        ‰| �  |t        «       |¬«       y )N)r   é   )r   Úbatch_shapeÚexpandÚsuperÚ__init__r   )Úselfr   r   r   r   Ú	__class__s        €úb/var/www/html/ia-enhanced/venv/lib/python3.12/site-packages/torch/distributions/logistic_normal.pyr   zLogisticNormal.__init__,   sK   ø€ ô ˜3 °]ÔCˆ	Ø×$Ò$Ø!×(Ñ(¨!¨Ó-ˆIÜ‰ÑØÔ-Ó/¸}ð 	õ 	
ó    c                 óR   •— | j                  t        |«      }t        ‰| �  ||¬«      S )N)Ú	_instance)Ú_get_checked_instancer	   r   r   )r   r   r   Únewr   s       €r   r   zLogisticNormal.expand9   s(   ø€ Ø×(Ñ(¬¸ÓCˆÜ‰w‰~˜k°Sˆ~Ó9Ð9r   c                 óB   — | j                   j                   j                  S ©N)r   r   ©r   s    r   r   zLogisticNormal.loc=   s   € à�~‰~×'Ñ'×+Ñ+Ð+r   c                 óB   — | j                   j                   j                  S r   )r   r   r    s    r   r   zLogisticNormal.scaleA   s   € à�~‰~×'Ñ'×-Ñ-Ð-r   r   )Ú__name__Ú
__module__Ú__qualname__Ú__doc__r   ÚrealÚpositiveÚarg_constraintsÚsimplexÚsupportÚhas_rsampler   r   Ú__annotations__r   ÚfloatÚboolr   r   Úpropertyr   r   Ú__classcell__)r   s   @r   r	   r	      s²   ø… ñð. *×.Ñ.¸×9MÑ9MÑN€Oà×!Ñ!€GØ€Kà˜6Ñ"Ó"ð &*ñ	
à�e‰^ð
ð ˜‰~ð
ð ˜d‘{ð	
ð
 
õ
õ:ð ð,�Vò ,ó ð,ð ð.�vò .ó ô.r   N)Útorchr   Útorch.distributionsr   r   Útorch.distributions.normalr   Ú,torch.distributions.transformed_distributionr   Útorch.distributions.transformsr   Ú__all__r	   © r   r   ú<module>r8      s-   ðõ ß 8Ý -Ý PÝ Að Ð
€ô6.Ð,õ 6.r   