
    ǄgC"                         d dl Z d dlmZ d dlZd dlmZ d dlmZ d dlm	Z	m
Z
mZmZmZ d dlmZ d dlmZ dgZ G d	 de      Zy)
    N)Number)constraints)ExponentialFamily)broadcast_allclamp_probslazy_propertylogits_to_probsprobs_to_logits) binary_cross_entropy_with_logits)_sizeContinuousBernoullic                       e Zd ZdZej
                  ej                  dZej
                  ZdZ	dZ
	 d fd	Zd fd	Zd Zd Zd	 Zd
 Zed        Zed        Zed        Zed        Zed        Zed        Z ej4                         fdZ ej4                         fdedej:                  fdZd Zd Z d Z!d Z"ed        Z#d Z$ xZ%S )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   Tc                 F   |d u |d u k(  rt        d      |t        |t              }t        |      \  | _        |A| j
                  d   j                  | j                        j                         st        d      t        | j                        | _        n"t        |t              }t        |      \  | _	        || j                  n| j                  | _
        |rt        j                         }n| j                  j                         }|| _        t        | A  ||       y )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          p/home/mcse/projects/flask_80/flask-venv/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.pyr   zContinuousBernoulli.__init__5   s     TMv~.M  "5&1I)%0MTZ (++G4::4::FJJL$%MNN$TZZ0DJ"662I*62NT[$)$5djj4;;**,K++**,K
MB    c                    | j                  t        |      }| j                  |_        t        j                  |      }d| j
                  v r1| j                  j                  |      |_        |j                  |_        d| j
                  v r1| j                  j                  |      |_	        |j                  |_        t        t        |/  |d       | j                  |_        |S )Nr   r   Fr   )_get_checked_instancer   r   r   r   __dict__r   expandr   r   r   r   _validate_args)r    r#   	_instancenewr$   s       r%   r*   zContinuousBernoulli.expandP   s    (()<iHJJ	jj-dmm#

))+6CICJt}}$++K8CJCJ!30E0R!00
r&   c                 :     | j                   j                  |i |S N)r   r-   )r    argskwargss      r%   _newzContinuousBernoulli._new^   s    t{{///r&   c                     t        j                  t        j                  | j                  | j                  d         t        j
                  | j                  | j                  d               S )Nr      )r   maxler   r   gtr    s    r%   _outside_unstable_regionz,ContinuousBernoulli._outside_unstable_regiona   sG    yyHHTZZA/$**djjQRm1T
 	
r&   c                     t        j                  | j                         | j                  | j                  d   t        j
                  | j                        z        S )Nr   )r   wherer9   r   r   	ones_liker8   s    r%   
_cut_probszContinuousBernoulli._cut_probsf   sC    {{))+JJJJqMEOODJJ77
 	
r&   c           	      F   | j                         }t        j                  t        j                  |d      |t        j                  |            }t        j                  t        j
                  |d      |t        j                  |            }t        j                  t        j                  t        j                  |       t        j                  |      z
              t        j                  t        j                  |d      t        j                  d|z        t        j                  d|z  dz
              z
  }t        j                  | j                  dz
  d      }t        j                  d      dd|z  z   |z  z   }t        j                  | j                         ||      S )zLcomputes the log normalizing constant as a function of the 'probs' parameter      ?g              @      ?   gUUUUUU?g'}'}@)r=   r   r;   r6   
zeros_likeger<   logabslog1ppowr   mathr9   )r    	cut_probscut_probs_below_halfcut_probs_above_halflog_normxtaylors          r%   _cont_bern_log_normz'ContinuousBernoulli._cont_bern_log_normm   s;   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:HfMMr&   c                 D   | j                         }|d|z  dz
  z  dt        j                  |       t        j                  |      z
  z  z   }| j                  dz
  }dddt        j
                  |d      z  z   |z  z   }t        j                  | j                         ||      S )Nr@   rA   r?   gUUUUUU?gll?rB   )r=   r   rG   rE   r   rH   r;   r9   )r    rJ   musrN   rO   s        r%   meanzContinuousBernoulli.mean   s    OO%	3?S01CKK
#eii	&::5
 
 JJ	K%))Aq/$AAQFF{{488:CHHr&   c                 @    t        j                  | j                        S r/   )r   sqrtvariancer8   s    r%   stddevzContinuousBernoulli.stddev   s    zz$--((r&   c                    | j                         }||dz
  z  t        j                  dd|z  z
  d      z  dt        j                  t        j                  |       t        j                  |      z
  d      z  z   }t        j                  | j
                  dz
  d      }ddd|z  z
  |z  z
  }t        j                  | j                         ||      S )NrA   r@   rB   r?   gUUUUUU?g?ggjV?)r=   r   rH   rG   rE   r   r;   r9   )r    rJ   varsrN   rO   s        r%   rV   zContinuousBernoulli.variance   s    OO%	IO,uyy#	/!10
 
%))EKK
3eii	6JJANNO IIdjj3&*zMA,==BB{{488:D&IIr&   c                 0    t        | j                  d      S NT)	is_binary)r
   r   r8   s    r%   r   zContinuousBernoulli.logits   s    tzzT::r&   c                 B    t        t        | j                  d            S r[   )r   r	   r   r8   s    r%   r   zContinuousBernoulli.probs   s    ?4;;$GHHr&   c                 6    | j                   j                         S r/   )r   r   r8   s    r%   param_shapezContinuousBernoulli.param_shape   s    {{!!r&   c                    | j                  |      }t        j                  || j                  j                  | j                  j
                        }t        j                         5  | j                  |      cd d d        S # 1 sw Y   y xY wN)dtypedevice)_extended_shaper   randr   rb   rc   no_gradicdfr    sample_shapeshapeus       r%   samplezContinuousBernoulli.sample   sa    $$\2JJuDJJ$4$4TZZ=N=NO]]_ 	 99Q<	  	  	 s   &BB
ri   returnc                     | j                  |      }t        j                  || j                  j                  | j                  j
                        }| j                  |      S ra   )rd   r   re   r   rb   rc   rg   rh   s       r%   rsamplezContinuousBernoulli.rsample   sF    $$\2JJuDJJ$4$4TZZ=N=NOyy|r&   c                     | j                   r| j                  |       t        | j                  |      \  }}t	        ||d       | j                         z   S )Nnone)	reduction)r+   _validate_sampler   r   r   rP   )r    valuer   s      r%   log_probzContinuousBernoulli.log_prob   sR    !!%(%dkk59-fevNN&&()	
r&   c           
         | j                   r| j                  |       | j                         }t        j                  ||      t        j                  d|z
  d|z
        z  |z   dz
  d|z  dz
  z  }t        j
                  | j                         ||      }t        j
                  t        j                  |d      t        j                  |      t        j
                  t        j                  |d      t        j                  |      |            S )NrA   r@   g        )r+   rs   r=   r   rH   r;   r9   r6   rC   rD   r<   )r    rt   rJ   cdfsunbounded_cdfss        r%   cdfzContinuousBernoulli.cdf   s    !!%(OO%	IIi'%))C)OS5[*QQ 9_s"	$
 T%B%B%DdER{{HHUC U#KK,eooe.DnU
 	
r&   c           	      4   | j                         }t        j                  | j                         t        j                  | |d|z  dz
  z  z         t        j                  |       z
  t        j
                  |      t        j                  |       z
  z  |      S )Nr@   rA   )r=   r   r;   r9   rG   rE   )r    rt   rJ   s      r%   rg   zContinuousBernoulli.icdf   s    OO%	{{))+YJ#	/C2G)HHI++yj)* yy#ekk9*&==	?
 
 	
r&   c                     t        j                  | j                         }t        j                  | j                        }| j                  ||z
  z  | j                         z
  |z
  S r/   )r   rG   r   rE   rS   rP   )r    
log_probs0
log_probs1s      r%   entropyzContinuousBernoulli.entropy   sW    [[$**-
YYtzz*
IIj01&&()	
r&   c                     | j                   fS r/   )r   r8   s    r%   _natural_paramsz#ContinuousBernoulli._natural_params   s    ~r&   c                    t        j                  t        j                  || j                  d   dz
        t        j                  || j                  d   dz
              }t        j
                  ||| j                  d   dz
  t        j                  |      z        }t        j                  t        j                  t        j                  |      dz
              t        j                  t        j                  |            z
  }d|z  t        j                  |d      dz  z   t        j                  |d      dz  z
  }t        j
                  |||      S )	zLcomputes the log normalizing constant as a function of the natural parameterr   r?   r4   rA   rB   g      8@   g     @)r   r5   r6   r   r7   r;   r<   rE   rF   exprH   )r    rN   out_unst_regcut_nat_paramsrM   rO   s         r%   _log_normalizerz#ContinuousBernoulli._log_normalizer   s   yyHHQ

1+,ehhq$**Q-#:M.N
 !djjmc1U__Q5GG
 99UYYuyy'@3'FGH599IIn%L
 
 q599Q?T11EIIaOf4LL{{<6::r&   )NN)gV-?gx&1?Nr/   )&__name__
__module____qualname____doc__r   unit_intervalrealr   support_mean_carrier_measurehas_rsampler   r*   r2   r9   r=   rP   propertyrS   rW   rV   r   r   r   r_   r   r   rl   r   Tensorro   ru   ry   rg   r~   r   r   __classcell__)r$   s   @r%   r   r      s>   2 !, 9 9[EUEUVO''GK KOC60


N( I I ) ) J J ; ; I I " " #-%**,   -7EJJL E U\\ 


 


  ;r&   )rI   numbersr   r   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   r   r   r	   r
   torch.nn.functionalr   torch.typesr   __all__r    r&   r%   <module>r      s@       + <  A  !
!X;+ X;r&   