+
    –°³j°  ã                   ó^   € ^ RI Ht ^ RIHt ^ RIHt ^ RIHt ^ RIH	t	 R.t
 ! R R]4      tR# )	é    ©ÚTensor)Úconstraints©ÚNormal)ÚTransformedDistribution)ÚExpTransformÚ	LogNormalc                   óD  a a€ ] tR t^t oRtR]P                  R]P                  /t]P                  t	Rt
RV3R lV 3R llltRV 3R llt]V3R lR	 l4       t]V3R
 lR l4       t]V3R lR l4       t]V3R lR l4       t]V3R lR l4       tR tV3R ltRtVtV ;t# )r
   a  
Creates a log-normal distribution parameterized by
:attr:`loc` and :attr:`scale` where::

    X ~ Normal(loc, scale)
    Y = exp(X) ~ LogNormal(loc, scale)

Example::

    >>> # xdoctest: +IGNORE_WANT("non-deterministic")
    >>> m = LogNormal(torch.tensor([0.0]), torch.tensor([1.0]))
    >>> m.sample()  # log-normal distributed with mean=0 and stddev=1
    tensor([ 0.1046])

Args:
    loc (float or Tensor): mean of log of distribution
    scale (float or Tensor): standard deviation of log of the distribution
ÚlocÚscaleTc                ó^   <€ V ^8„  d   QhRS[ S[,          RS[ S[,          RS[R,          RR/# )é   r   r   Úvalidate_argsNÚreturn)r   ÚfloatÚbool)ÚformatÚ__classdict__s   "€Ú_/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/distributions/log_normal.pyÚ__annotate__ÚLogNormal.__annotate__(   sE   ø€ ÷ Qñ Qá‘e�^ðQñ ™�~ðQñ ˜d•{ð	Qð
 
ñQó    c                óV   <€ \        WVR 7      p\        SV `	  V\        4       VR 7       R# ))r   N)r   ÚsuperÚ__init__r	   )Úselfr   r   r   Ú	base_distÚ	__class__s   &&&& €r   r   ÚLogNormal.__init__(   s'   ø€ ô ˜3°]ÔCˆ	Ü‰Ñ˜¤L£NÀ-ÐÖPr   c                óP   <€ V P                  \        V4      p\        SV `  WR 7      # ))Ú	_instance)Ú_get_checked_instancer
   r   Úexpand)r   Úbatch_shaper"   Únewr   s   &&& €r   r$   ÚLogNormal.expand1   s&   ø€ Ø×(Ñ(¬°IÓ>ˆÜ‰w‰~˜kˆ~Ó9Ð9r   c                ó    <€ V ^8„  d   QhRS[ /# ©r   r   r   )r   r   s   "€r   r   r   6   s   ø€ ÷ "ñ "‘Vñ "r   c                ó.   € V P                   P                  # ©N)r   r   ©r   s   &r   r   ÚLogNormal.loc5   s   € à�~‰~×!Ñ!Ð!r   c                ó    <€ V ^8„  d   QhRS[ /# r)   r   )r   r   s   "€r   r   r   :   s   ø€ ÷ $ñ $‘vñ $r   c                ó.   € V P                   P                  # r+   )r   r   r,   s   &r   r   ÚLogNormal.scale9   s   € à�~‰~×#Ñ#Ð#r   c                ó    <€ V ^8„  d   QhRS[ /# r)   r   )r   r   s   "€r   r   r   >   s   ø€ ÷ 8ñ 8‘fñ 8r   c                ó„   € V P                   V P                  P                  ^4      ^,          ,           P                  4       # ©r   )r   r   ÚpowÚexpr,   s   &r   ÚmeanÚLogNormal.mean=   s,   € à—‘˜4Ÿ:™:Ÿ>™>¨!Ó,¨qÕ0Õ0×5Ñ5Ó7Ð7r   c                ó    <€ V ^8„  d   QhRS[ /# r)   r   )r   r   s   "€r   r   r   B   s   ø€ ÷ 6ñ 6‘fñ 6r   c                ót   € V P                   V P                  P                  4       ,
          P                  4       # r+   )r   r   Úsquarer5   r,   s   &r   ÚmodeÚLogNormal.modeA   s'   € à—‘˜4Ÿ:™:×,Ñ,Ó.Õ.×3Ñ3Ó5Ð5r   c                ó    <€ V ^8„  d   QhRS[ /# r)   r   )r   r   s   "€r   r   r   F   s   ø€ ÷ Bñ B™&ñ Br   c                ó²   € V P                   P                  ^4      pVP                  4       ^V P                  ,          V,           P	                  4       ,          # r3   )r   r4   Úexpm1r   r5   )r   Úscale_sqs   & r   ÚvarianceÚLogNormal.varianceE   s<   € à—:‘:—>‘> !Ó$ˆØ�~‰~Ó 1 t§x¡x¥<°(Õ#:×"?Ñ"?Ó"AÕAÐAr   c                óX   € V P                   P                  4       V P                  ,           # r+   )r   Úentropyr   r,   s   &r   rD   ÚLogNormal.entropyJ   s   € Ø�~‰~×%Ñ%Ó'¨$¯(©(Õ2Ð2r   c                ó&   <€ V ^8„  d   Qh/ S[ ;R&   # )r   r   r   )r   r   s   "€r   r   r      s   ø‡ ‚ ñ2 Ñò3 r   © r+   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__r   ÚrealÚpositiveÚarg_constraintsÚsupportÚhas_rsampler   r$   Úpropertyr   r   r6   r;   rA   rD   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r   r   s   @@r   r
   r
      sº   ù‡ € ñð& ˜k×.Ñ.°¸×9MÑ9MÐN€Oà×"Ñ"€GØ€K÷Qõ Q÷:ð ÷"ó ð"ð ÷$ó ð$ð ÷8ó ð8ð ÷6ó ð6ð ÷Bó ðBò3÷{ … r   N)Útorchr   Útorch.distributionsr   Útorch.distributions.normalr   Ú,torch.distributions.transformed_distributionr   Útorch.distributions.transformsr	   Ú__all__r
   rG   r   r   Ú<module>r]      s,   ðõ Ý +Ý -Ý PÝ 7ð ˆ-€ô>3Ð'ö >3r   