+
    –°³j_  ã                   ó„   € ^ RI t ^ RI HtHt ^ RIHt ^ RIHt ^ RIHtH	t	 ^ RI
Ht ^ RIHtHt R.tR	 t ! R
 R]4      tR# )é    N)ÚnanÚTensor)Úconstraints)ÚTransformedDistribution)ÚAffineTransformÚPowerTransform)ÚUniform)Úbroadcast_allÚeuler_constantÚKumaraswamyc                óþ   € ^W ,          ,           p\         P                  ! V4      \         P                  ! V4      ,           \         P                  ! W1,           4      ,
          pV\         P                  ! V4      ,          # )z7
Computes nth moment of Kumaraswamy using torch.lgamma
)ÚtorchÚlgammaÚexp)ÚaÚbÚnÚarg1Ú	log_values   &&&  Ú`/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/distributions/kumaraswamy.pyÚ_momentsr      sN   € ð ˆq�u�9€DÜ—’˜TÓ"¤U§\¢\°!£_Õ4´u·|²|ÀDÅHÓ7MÕM€IØŒu�yŠy˜Ó#Õ#Ð#ó    c                   ó   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R tRtVtV ;t# )r   a#  
Samples from a Kumaraswamy distribution.

Example::

    >>> # xdoctest: +IGNORE_WANT("non-deterministic")
    >>> m = Kumaraswamy(torch.tensor([1.0]), torch.tensor([1.0]))
    >>> m.sample()  # sample from a Kumaraswamy distribution with concentration alpha=1 and beta=1
    tensor([ 0.1729])

Args:
    concentration1 (float or Tensor): 1st concentration parameter of the distribution
        (often referred to as alpha)
    concentration0 (float or Tensor): 2nd concentration parameter of the distribution
        (often referred to as beta)
Úconcentration1Úconcentration0Tc                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   "€r   Ú__annotate__ÚKumaraswamy.__annotate__2   sE   ø€ ÷ Mñ Má¡�ðMñ ¡�ðMñ ˜d•{ð	Mð
 
ñMr   c                óš  <€ \        W4      w  V n        V n        \        \        P
                  ! V P                  ^ 4      \        P
                  ! V P                  ^4      VR7      p\        V P                  P                  4       R7      \        RRR7      \        V P                  P                  4       R7      .p\        SV `)  WEVR7       R# )r   )r   )Úexponentg      ð?)ÚlocÚscaleNg      ð¿)r
   r   r   r	   r   Ú	full_liker   Ú
reciprocalr   ÚsuperÚ__init__)Úselfr   r   r   Ú	base_distÚ
transformsÚ	__class__s   &&&&  €r   r-   ÚKumaraswamy.__init__2   sª   ø€ ô 4AØó4
Ñ0ˆÔ˜TÔ0ô Ü�OŠO˜D×/Ñ/°Ó3Ü�OŠO˜D×/Ñ/°Ó3Ø'ô
ˆ	ô  D×$7Ñ$7×$BÑ$BÓ$DÔEÜ ¨4Ô0Ü D×$7Ñ$7×$BÑ$BÓ$DÔEð
ˆ
ô 	‰Ñ˜¸mÐÖLr   c                óÐ   <€ V P                  \        V4      pV P                  P                  V4      Vn        V P                  P                  V4      Vn        \
        SV `  WR 7      # ))Ú	_instance)Ú_get_checked_instancer   r   Úexpandr   r,   )r.   Úbatch_shaper4   Únewr1   s   &&& €r   r6   ÚKumaraswamy.expandH   sX   ø€ Ø×(Ñ(¬°iÓ@ˆØ!×0Ñ0×7Ñ7¸ÓDˆÔØ!×0Ñ0×7Ñ7¸ÓDˆÔÜ‰w‰~˜kˆ~Ó9Ð9r   c                ó    <€ V ^8„  d   QhRS[ /# ©r   r   ©r   )r"   r#   s   "€r   r$   r%   O   s   ø€ ÷ Eñ E‘fñ Er   c                óD   € \        V P                  V P                  ^4      # ©é   )r   r   r   ©r.   s   &r   ÚmeanÚKumaraswamy.meanN   s   € ä˜×+Ñ+¨T×-@Ñ-@À!ÓDÐDr   c                ó    <€ V ^8„  d   QhRS[ /# r;   r<   )r"   r#   s   "€r   r$   r%   S   s   ø€ ÷ ñ ‘fñ r   c                óJ  € V P                   P                  4       V P                   ) P                  4       ,          V P                   ) V P                  ,          P                  4       ,
          p\        WP                   ^8  V P                  ^8  ,          &   VP                  4       # r>   )r   r+   Úlog1pr   r   r   )r.   Úlog_modes   & r   ÚmodeÚKumaraswamy.modeR   s‹   € ð ×Ñ×*Ñ*Ó,°×1DÑ1DÐ0D×/KÑ/KÓ/MÕMØ×#Ñ#Ð# d×&9Ñ&9Õ9×@Ñ@ÓBõCð 	ô KNˆ×%Ñ%¨Ñ)¨d×.AÑ.AÀAÑ.EÕFÑGØ�|‰|‹~Ðr   c                ó    <€ V ^8„  d   QhRS[ /# r;   r<   )r"   r#   s   "€r   r$   r%   ]   s   ø€ ÷ 
ñ 
™&ñ 
r   c                ó�   € \        V P                  V P                  ^4      \        P                  ! V P
                  ^4      ,
          # )r   )r   r   r   r   ÚpowrA   r@   s   &r   ÚvarianceÚKumaraswamy.variance\   s9   € ä˜×+Ñ+¨T×-@Ñ-@À!ÓDÄuÇyÂyØ�I‰I�qóH
õ 
ð 	
r   c                óœ  € ^V P                   P                  4       ,
          p^V P                  P                  4       ,
          p\        P                  ! V P                  ^,           4      \
        ,           pVW,          ,           \        P                  ! V P                   4      ,
          \        P                  ! V P                  4      ,
          # r>   )r   r+   r   r   Údigammar   Úlog)r.   Út1Út0ÚH0s   &   r   ÚentropyÚKumaraswamy.entropyb   s”   € Ø�×$Ñ$×/Ñ/Ó1Õ1ˆØ�×$Ñ$×/Ñ/Ó1Õ1ˆÜ�]Š]˜4×.Ñ.°Õ2Ó3´nÕDˆàØ�gõä�iŠi˜×+Ñ+Ó,õ-ô �iŠi˜×+Ñ+Ó,õ-ð	
r   )r   r   )N)Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__r   ÚpositiveÚarg_constraintsÚunit_intervalÚsupportÚhas_rsampler-   r6   ÚpropertyrA   rG   rL   rT   Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r1   r#   s   @@r   r   r      s”   ù‡ € ñð$ 	˜+×.Ñ.Ø˜+×.Ñ.ð€Oð
 ×'Ñ'€GØ€K÷Mõ M÷,:ð ÷Eó ðEð ÷ó ðð ÷
ó ð
÷
	
ò 	
r   )r   r   r   Útorch.distributionsr   Ú,torch.distributions.transformed_distributionr   Útorch.distributions.transformsr   r   Útorch.distributions.uniformr	   Útorch.distributions.utilsr
   r   Ú__all__r   r   © r   r   Ú<module>rk      s9   ðó ß Ý +Ý Pß JÝ /ß Cð ˆ/€ò$ôS
Ð)ö S
r   