
    [Th"                         S SK r S SKrS SKJr  S SKJr  S SKJr  S SKJrJ	r	J
r
JrJr  S SKJr  S SKJrJr  S/r " S	 S\5      rg)
    N)Tensor)constraints)ExponentialFamily)broadcast_allclamp_probslazy_propertylogits_to_probsprobs_to_logits) binary_cross_entropy_with_logits)_Number_sizeContinuousBernoullic                     ^  \ rS rSrSr\R                  \R                  S.r\R                  r	Sr
Sr S SU 4S jjjrSU 4S jjrS	 rS
 rS rS r\S\4S j5       r\S\4S j5       r\S\4S j5       r\S\4S j5       r\S\4S j5       r\S\R6                  4S j5       r\R6                  " 5       4S jr\R6                  " 5       4S\S\4S jjrS r S r!S r"S r#\S\$\   4S j5       r%S r&Sr'U =r($ ) r      a  
Creates a continuous Bernoulli distribution parameterized by :attr:`probs`
or :attr:`logits` (but not both).

The distribution is supported in [0, 1] and parameterized by 'probs' (in
(0,1)) or 'logits' (real-valued). Note that, unlike the Bernoulli, 'probs'
does not correspond to a probability and 'logits' does not correspond to
log-odds, but the same names are used due to the similarity with the
Bernoulli. See [1] for more details.

Example::

    >>> # xdoctest: +IGNORE_WANT("non-deterministic")
    >>> m = ContinuousBernoulli(torch.tensor([0.3]))
    >>> m.sample()
    tensor([ 0.2538])

Args:
    probs (Number, Tensor): (0,1) valued parameters
    logits (Number, Tensor): real valued parameters whose sigmoid matches 'probs'

[1] The continuous Bernoulli: fixing a pervasive error in variational
autoencoders, Loaiza-Ganem G and Cunningham JP, NeurIPS 2019.
https://arxiv.org/abs/1907.06845
)probslogitsr   Treturnc                 Z  > US L US L :X  a  [        S5      eUb  [        U[        5      n[        U5      u  U l        UbF  U R
                  S   R                  U R                  5      R                  5       (       d  [        S5      e[        U R                  5      U l        O"[        U[        5      n[        U5      u  U l	        Ub  U R                  OU R                  U l
        U(       a  [        R                  " 5       nOU R                  R                  5       nX0l        [        TU ]A  XdS9  g )Nz;Either `probs` or `logits` must be specified, but not both.r   z&The parameter probs has invalid valuesvalidate_args)
ValueError
isinstancer   r   r   arg_constraintscheckallr   r   _paramtorchSizesize_limssuper__init__)selfr   r   limsr   	is_scalarbatch_shape	__class__s          `/var/www/auris/envauris/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.pyr"   ContinuousBernoulli.__init__6   s     TMv~.M  "5'2I)%0MTZ (++G4::4::FJJLL$%MNN$TZZ0DJ"673I*62NT[$)$5djj4;;**,K++**,K
B    c                   > U R                  [        U5      nU R                  Ul        [        R                  " U5      nSU R
                  ;   a1  U R                  R                  U5      Ul        UR                  Ul        SU R
                  ;   a1  U R                  R                  U5      Ul	        UR                  Ul        [        [        U]/  USS9  U R                  Ul        U$ )Nr   r   Fr   )_get_checked_instancer   r    r   r   __dict__r   expandr   r   r!   r"   _validate_args)r#   r&   	_instancenewr'   s       r(   r.   ContinuousBernoulli.expandQ   s    (()<iHJJ	jj-dmm#

))+6CICJt}}$++K8CJCJ!30E0R!00
r*   c                 :    U R                   R                  " U0 UD6$ N)r   r1   )r#   argskwargss      r(   _newContinuousBernoulli._new_   s    {{///r*   c                     [         R                  " [         R                  " U R                  U R                  S   5      [         R
                  " U R                  U R                  S   5      5      $ )Nr      )r   maxler   r    gtr#   s    r(   _outside_unstable_region,ContinuousBernoulli._outside_unstable_regionb   sG    yyHHTZZA/$**djjQRm1T
 	
r*   c                     [         R                  " U R                  5       U R                  U R                  S   [         R
                  " U R                  5      -  5      $ )Nr   )r   wherer?   r   r    	ones_liker>   s    r(   
_cut_probsContinuousBernoulli._cut_probsg   sC    {{))+JJJJqMEOODJJ77
 	
r*   c           	      f   U R                  5       n[        R                  " [        R                  " US5      U[        R                  " U5      5      n[        R                  " [        R
                  " US5      U[        R                  " U5      5      n[        R                  " [        R                  " [        R                  " U* 5      [        R                  " U5      -
  5      5      [        R                  " [        R                  " US5      [        R                  " SU-  5      [        R                  " SU-  S-
  5      5      -
  n[        R                  " U R                  S-
  S5      n[        R                  " S5      SSU-  -   U-  -   n[        R                  " U R                  5       XF5      $ )zLcomputes the log normalizing constant as a function of the 'probs' parameter      ?g              @      ?   gUUUUUU?g'}'}@)rD   r   rB   r<   
zeros_likegerC   logabslog1ppowr   mathr?   )r#   	cut_probscut_probs_below_halfcut_probs_above_halflog_normxtaylors          r(   _cont_bern_log_norm'ContinuousBernoulli._cont_bern_log_normn   s9   OO%	${{HHY$i1A1A)1L 
  %{{HHY$i1K 
 99IIekk9*-		)0DDE
KKHHY$KK334IIc00367

 IIdjj3&*#)lQ.>">!!CC{{488:HMMr*   c                 J   U R                  5       nUSU-  S-
  -  S[        R                  " U* 5      [        R                  " U5      -
  -  -   nU R                  S-
  nSSS[        R
                  " US5      -  -   U-  -   n[        R                  " U R                  5       X$5      $ )NrH   rI   rG   gUUUUUU?gll?rJ   )rD   r   rO   rM   r   rP   rB   r?   )r#   rR   musrV   rW   s        r(   meanContinuousBernoulli.mean   s    OO%	3?S01CKK
#eii	&::5
 
 JJ	K%))Aq/$AAQFF{{488:CHHr*   c                 B    [         R                  " U R                  5      $ r4   )r   sqrtvariancer>   s    r(   stddevContinuousBernoulli.stddev   s    zz$--((r*   c                    U R                  5       nXS-
  -  [        R                  " SSU-  -
  S5      -  S[        R                  " [        R                  " U* 5      [        R                  " U5      -
  S5      -  -   n[        R                  " U R
                  S-
  S5      nSSSU-  -
  U-  -
  n[        R                  " U R                  5       X$5      $ )NrI   rH   rJ   rG   gUUUUUU?g?ggjV?)rD   r   rP   rO   rM   r   rB   r?   )r#   rR   varsrV   rW   s        r(   r`   ContinuousBernoulli.variance   s    OO%	O,uyy#	/!10
 
%))EKK
3eii	6JJANNO IIdjj3&*zMA,==BB{{488:DIIr*   c                 *    [        U R                  SS9$ NT)	is_binary)r
   r   r>   s    r(   r   ContinuousBernoulli.logits   s    tzzT::r*   c                 <    [        [        U R                  SS95      $ rg   )r   r	   r   r>   s    r(   r   ContinuousBernoulli.probs   s    ?4;;$GHHr*   c                 6    U R                   R                  5       $ r4   )r   r   r>   s    r(   param_shapeContinuousBernoulli.param_shape   s    {{!!r*   c                     U R                  U5      n[        R                  " X R                  R                  U R                  R
                  S9n[        R                  " 5          U R                  U5      sS S S 5        $ ! , (       d  f       g = fN)dtypedevice)_extended_shaper   randr   rq   rr   no_gradicdfr#   sample_shapeshapeus       r(   sampleContinuousBernoulli.sample   sT    $$\2JJuJJ$4$4TZZ=N=NO]]_99Q< __s   $A??
Brx   c                     U R                  U5      n[        R                  " X R                  R                  U R                  R
                  S9nU R                  U5      $ rp   )rs   r   rt   r   rq   rr   rv   rw   s       r(   rsampleContinuousBernoulli.rsample   sD    $$\2JJuJJ$4$4TZZ=N=NOyy|r*   c                     U R                   (       a  U R                  U5        [        U R                  U5      u  p![	        X!SS9* U R                  5       -   $ )Nnone)	reduction)r/   _validate_sampler   r   r   rX   )r#   valuer   s      r(   log_probContinuousBernoulli.log_prob   sN    !!%(%dkk59-fvNN&&()	
r*   c           
      6   U R                   (       a  U R                  U5        U R                  5       n[        R                  " X!5      [        R                  " SU-
  SU-
  5      -  U-   S-
  SU-  S-
  -  n[        R
                  " U R                  5       X15      n[        R
                  " [        R                  " US5      [        R                  " U5      [        R
                  " [        R                  " US5      [        R                  " U5      U5      5      $ )NrI   rH   g        )r/   r   rD   r   rP   rB   r?   r<   rK   rL   rC   )r#   r   rR   cdfsunbounded_cdfss        r(   cdfContinuousBernoulli.cdf   s    !!%(OO%	IIi'%))C)OS5[*QQ 9_s"	$
 T%B%B%DdR{{HHUC U#KK,eooe.DnU
 	
r*   c           	      >   U R                  5       n[        R                  " U R                  5       [        R                  " U* USU-  S-
  -  -   5      [        R                  " U* 5      -
  [        R
                  " U5      [        R                  " U* 5      -
  -  U5      $ )NrH   rI   )rD   r   rB   r?   rO   rM   )r#   r   rR   s      r(   rv   ContinuousBernoulli.icdf   s    OO%	{{))+YJ#	/C2G)HHI++yj)* yy#ekk9*&==	?
 
 	
r*   c                     [         R                  " U R                  * 5      n[         R                  " U R                  5      nU R                  X-
  -  U R                  5       -
  U-
  $ r4   )r   rO   r   rM   r\   rX   )r#   
log_probs0
log_probs1s      r(   entropyContinuousBernoulli.entropy   sU    [[$**-
YYtzz*
II01&&()	
r*   c                     U R                   4$ r4   )r   r>   s    r(   _natural_params#ContinuousBernoulli._natural_params   s    ~r*   c                    [         R                  " [         R                  " XR                  S   S-
  5      [         R                  " XR                  S   S-
  5      5      n[         R
                  " X!U R                  S   S-
  [         R                  " U5      -  5      n[         R                  " [         R                  " [         R                  R                  U5      5      5      [         R                  " [         R                  " U5      5      -
  nSU-  [         R                  " US5      S-  -   [         R                  " US5      S-  -
  n[         R
                  " X$U5      $ )zLcomputes the log normalizing constant as a function of the natural parameterr   rG   r:   rJ   g      8@   g     @)r   r;   r<   r    r=   rB   rC   rM   rN   specialexpm1rP   )r#   rV   out_unst_regcut_nat_paramsrU   rW   s         r(   _log_normalizer#ContinuousBernoulli._log_normalizer   s    yyHHQ

1+,ehhq**Q-#:M.N
 djjmc1U__Q5GG
 99IIemm)).9:
IIeii/01 q599Q?T11EIIaOf4LL{{<6::r*   )r    r   r   r   )NN)gV-?gx&1?N)r   Nr4   ))__name__
__module____qualname____firstlineno____doc__r   unit_intervalrealr   support_mean_carrier_measurehas_rsampler"   r.   r7   r?   rD   rX   propertyr   r\   ra   r`   r   r   r   r   r   rm   r{   r   r~   r   r   rv   r   tupler   r   __static_attributes____classcell__)r'   s   @r(   r   r      s   4 !, 9 9[EUEUVO''GK KOC	C C60


N( If I I ) ) ) J& J J ; ; ; Iv I I "UZZ " " #(**,   -2JJL E V 


 


 v  ; ;r*   )rQ   r   r   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   r   r   r	   r
   torch.nn.functionalr   torch.typesr   r   __all__r    r*   r(   <module>r      s@       + <  A & !
!Y;+ Y;r*   