Ë
    P/æj  ã                   ót   — d dl Z d dlmZ d dlmZ d dlmZ d dlmZ d dl	m
Z
mZ d dlmZ dgZ G d	„ de«      Zy)
é    N)Úconstraints)ÚExponential)Úeuler_constant)ÚTransformedDistribution)ÚAffineTransformÚPowerTransform)Úbroadcast_allÚWeibullc                   ó°   ‡ — e Zd ZdZej
                  ej
                  dœZej
                  Zd	ˆ fd„	Zd	ˆ fd„	Z	e
d„ «       Ze
d„ «       Ze
d„ «       Zd„ Zˆ xZS )
r
   aî  
    Samples from a two-parameter Weibull distribution.

    Example:

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Weibull(torch.tensor([1.0]), torch.tensor([1.0]))
        >>> m.sample()  # sample from a Weibull distribution with scale=1, concentration=1
        tensor([ 0.4784])

    Args:
        scale (float or Tensor): Scale parameter of distribution (lambda).
        concentration (float or Tensor): Concentration parameter of distribution (k/shape).
    )ÚscaleÚconcentrationc                 óH  •— t        ||«      \  | _        | _        | j                  j                  «       | _        t        t        j                  | j                  «      |¬«      }t        | j                  ¬«      t        d| j                  ¬«      g}t        ‰| �-  |||¬«       y )N©Úvalidate_args©Úexponentr   ©Úlocr   )r	   r   r   Ú
reciprocalÚconcentration_reciprocalr   ÚtorchÚ	ones_liker   r   ÚsuperÚ__init__)Úselfr   r   r   Ú	base_distÚ
transformsÚ	__class__s         €ú^/root/repos/pocs/video-evals/.venv/lib/python3.12/site-packages/torch/distributions/weibull.pyr   zWeibull.__init__#   s‡   ø€ Ü)6°u¸mÓ)LÑ&ˆŒ
�DÔ&Ø(,×(:Ñ(:×(EÑ(EÓ(GˆÔ%ÜÜ�O‰O˜DŸJ™JÓ'°}ô
ˆ	ô  D×$AÑ$AÔBÜ ¨¯©Ô4ð
ˆ
ô 	‰Ñ˜ J¸mÐÕLó    c                 óÐ  •— | j                  t        |«      }| j                  j                  |«      |_        | j                  j                  |«      |_        |j                  j                  «       |_        | j                  j                  |«      }t        |j                  ¬«      t        d|j                  ¬«      g}t        t        |�/  ||d¬«       | j                  |_        |S )Nr   r   r   Fr   )Ú_get_checked_instancer
   r   Úexpandr   r   r   r   r   r   r   r   Ú_validate_args)r   Úbatch_shapeÚ	_instanceÚnewr   r   r   s         €r   r#   zWeibull.expand/   s½   ø€ Ø×(Ñ(¬°)Ó<ˆØ—J‘J×%Ñ% kÓ2ˆŒ	Ø ×.Ñ.×5Ñ5°kÓBˆÔØ'*×'8Ñ'8×'CÑ'CÓ'EˆÔ$Ø—N‘N×)Ñ)¨+Ó6ˆ	ä C×$@Ñ$@ÔAÜ ¨¯©Ô3ð
ˆ
ô 	Œg�sÑ$ Y°
È%Ð$ÔPØ!×0Ñ0ˆÔØˆ
r    c                 ó†   — | j                   t        j                  t        j                  d| j                  z   «      «      z  S ©Né   )r   r   ÚexpÚlgammar   ©r   s    r   ÚmeanzWeibull.mean=   s.   € à�z‰zœEŸI™I¤e§l¡l°1°t×7TÑ7TÑ3TÓ&UÓVÑVÐVr    c                 óŠ   — | j                   | j                  dz
  | j                  z  | j                  j                  «       z  z  S r)   )r   r   r   r-   s    r   ÚmodezWeibull.modeA   sE   € ð �J‰JØ×"Ñ" QÑ&¨$×*<Ñ*<Ñ<Ø×!Ñ!×,Ñ,Ó.ñ/ñ/ð	
r    c           	      ó  — | j                   j                  d«      t        j                  t        j                  dd| j
                  z  z   «      «      t        j                  dt        j                  d| j
                  z   «      z  «      z
  z  S )Né   r*   )r   Úpowr   r+   r,   r   r-   s    r   ÚvariancezWeibull.varianceI   sl   € à�z‰z�~‰~˜aÓ Ü�I‰I”e—l‘l 1 q¨4×+HÑ+HÑ'HÑ#HÓIÓJÜ�i‰i˜œEŸL™L¨¨T×-JÑ-JÑ)JÓKÑKÓLñMñ
ð 	
r    c                 óŽ   — t         d| j                  z
  z  t        j                  | j                  | j                  z  «      z   dz   S r)   )r   r   r   Úlogr   r-   s    r   ÚentropyzWeibull.entropyP   sC   € ä˜a $×"?Ñ"?Ñ?Ñ@Ü�i‰i˜Ÿ
™
 T×%BÑ%BÑBÓCñDàñð	
r    )N)Ú__name__Ú
__module__Ú__qualname__Ú__doc__r   ÚpositiveÚarg_constraintsÚsupportr   r#   Úpropertyr.   r0   r4   r7   Ú__classcell__)r   s   @r   r
   r
      s~   ø„ ñð ×%Ñ%Ø$×-Ñ-ñ€Oð ×"Ñ"€Gõ
Mõð ñWó ðWð ñ
ó ð
ð ñ
ó ð
ö
r    )r   Útorch.distributionsr   Útorch.distributions.exponentialr   Útorch.distributions.gumbelr   Ú,torch.distributions.transformed_distributionr   Útorch.distributions.transformsr   r   Útorch.distributions.utilsr	   Ú__all__r
   © r    r   ú<module>rI      s2   ðã Ý +Ý 7Ý 5Ý Pß JÝ 3ð ˆ+€ôG
Ð%õ G
r    