
    IЦiM                         S SK J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
  S SKJrJrJrJrJr  S SKJr  S	S
/r " S S	\5      r " S S
\5      rg)    )NumberN)constraints)Distribution)TransformedDistribution)SigmoidTransform)broadcast_allclamp_probslazy_propertylogits_to_probsprobs_to_logits)_sizeLogitRelaxedBernoulliRelaxedBernoullic                     ^  \ rS rSrSr\R                  \R                  S.r\R                  r	SU 4S jjr
SU 4S jjrS r\S 5       r\S 5       r\S	 5       r\R&                  " 5       4S
\S\R*                  4S jjrS rSrU =r$ )r      aO  
Creates a LogitRelaxedBernoulli distribution parameterized by :attr:`probs`
or :attr:`logits` (but not both), which is the logit of a RelaxedBernoulli
distribution.

Samples are logits of values in (0, 1). See [1] for more details.

Args:
    temperature (Tensor): relaxation temperature
    probs (Number, Tensor): the probability of sampling `1`
    logits (Number, Tensor): the log-odds of sampling `1`

[1] The Concrete Distribution: A Continuous Relaxation of Discrete Random
Variables (Maddison et al., 2017)

[2] Categorical Reparametrization with Gumbel-Softmax
(Jang et al., 2017)
probslogitsc                   > Xl         US L US L :X  a  [        S5      eUb#  [        U[        5      n[	        U5      u  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[        TU ]1  XdS9  g )Nz;Either `probs` or `logits` must be specified, but not both.validate_args)temperature
ValueError
isinstancer   r   r   r   _paramtorchSizesizesuper__init__)selfr   r   r   r   	is_scalarbatch_shape	__class__s          d/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.pyr    LogitRelaxedBernoulli.__init__,   s    &TMv~.M  "5&1I)%0MTZ"662I*62NT[$)$5djj4;;**,K++**,KB    c                   > U R                  [        U5      n[        R                  " U5      nU R                  Ul        SU R
                  ;   a1  U R                  R                  U5      Ul        UR                  Ul        SU R
                  ;   a1  U R                  R                  U5      Ul	        UR                  Ul        [        [        U]/  USS9  U R                  Ul        U$ )Nr   r   Fr   )_get_checked_instancer   r   r   r   __dict__r   expandr   r   r   r    _validate_argsr!   r#   	_instancenewr$   s       r%   r+   LogitRelaxedBernoulli.expand?   s    (()>	Jjj-**dmm#

))+6CICJt}}$++K8CJCJ#S2;e2T!00
r'   c                 :    U R                   R                  " U0 UD6$ N)r   r/   )r!   argskwargss      r%   _newLogitRelaxedBernoulli._newM   s    {{///r'   c                 *    [        U R                  SS9$ NT)	is_binary)r   r   r!   s    r%   r   LogitRelaxedBernoulli.logitsP   s    tzzT::r'   c                 *    [        U R                  SS9$ r8   )r   r   r:   s    r%   r   LogitRelaxedBernoulli.probsT   s    t{{d;;r'   c                 6    U R                   R                  5       $ r2   )r   r   r:   s    r%   param_shape!LogitRelaxedBernoulli.param_shapeX   s    {{!!r'   sample_shapereturnc                 t   U R                  U5      n[        U R                  R                  U5      5      n[        [        R
                  " X#R                  UR                  S95      nUR                  5       U* R                  5       -
  UR                  5       -   U* R                  5       -
  U R                  -  $ )N)dtypedevice)_extended_shaper	   r   r+   r   randrD   rE   loglog1pr   )r!   rA   shaper   uniformss        r%   rsampleLogitRelaxedBernoulli.rsample\   s    $$\2DJJ--e45JJuKKE
 LLNxi..00599;>5&AQQ 	r'   c                 .   U R                   (       a  U R                  U5        [        U R                  U5      u  p!X!R	                  U R
                  5      -
  nU R
                  R                  5       U-   SUR                  5       R                  5       -  -
  $ )N   )	r,   _validate_sampler   r   mulr   rH   exprI   )r!   valuer   diffs       r%   log_probLogitRelaxedBernoulli.log_probf   su    !!%(%dkk59		$"2"233##%,q488:3C3C3E/EEEr'   )r   r   r   r   NNNr2   )__name__
__module____qualname____firstlineno____doc__r   unit_intervalrealarg_constraintssupportr    r+   r5   r
   r   r   propertyr?   r   r   r   TensorrL   rU   __static_attributes____classcell__r$   s   @r%   r   r      s    $ !, 9 9[EUEUVOGC&0 ; ; < < " " -2JJL E U\\ F Fr'   c                      ^  \ rS rSrSr\R                  \R                  S.r\R                  r	Sr
SU 4S jjrSU 4S jjr\S 5       r\S 5       r\S	 5       rS
rU =r$ )r   n   a  
Creates a RelaxedBernoulli distribution, parametrized by
:attr:`temperature`, and either :attr:`probs` or :attr:`logits`
(but not both). This is a relaxed version of the `Bernoulli` distribution,
so the values are in (0, 1), and has reparametrizable samples.

Example::

    >>> # xdoctest: +IGNORE_WANT("non-deterministic")
    >>> m = RelaxedBernoulli(torch.tensor([2.2]),
    ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
    >>> m.sample()
    tensor([ 0.2951,  0.3442,  0.8918,  0.9021])

Args:
    temperature (Tensor): relaxation temperature
    probs (Number, Tensor): the probability of sampling `1`
    logits (Number, Tensor): the log-odds of sampling `1`
r   Tc                 L   > [        XU5      n[        TU ]	  U[        5       US9  g )Nr   )r   r   r    r   )r!   r   r   r   r   	base_distr$   s         r%   r    RelaxedBernoulli.__init__   s'    )+fE	$4$6mTr'   c                 J   > U R                  [        U5      n[        TU ]  XS9$ )N)r.   )r)   r   r   r+   r-   s       r%   r+   RelaxedBernoulli.expand   s'    (()99Ew~k~99r'   c                 .    U R                   R                  $ r2   )ri   r   r:   s    r%   r   RelaxedBernoulli.temperature   s    ~~)))r'   c                 .    U R                   R                  $ r2   )ri   r   r:   s    r%   r   RelaxedBernoulli.logits   s    ~~$$$r'   c                 .    U R                   R                  $ r2   )ri   r   r:   s    r%   r   RelaxedBernoulli.probs   s    ~~###r'    rW   r2   )rX   rY   rZ   r[   r\   r   r]   r^   r_   r`   has_rsampler    r+   ra   r   r   r   rc   rd   re   s   @r%   r   r   n   sw    & !, 9 9[EUEUVO''GKU: * * % % $ $r'   )numbersr   r   torch.distributionsr    torch.distributions.distributionr   ,torch.distributions.transformed_distributionr   torch.distributions.transformsr   torch.distributions.utilsr   r	   r
   r   r   torch.typesr   __all__r   r   rs   r'   r%   <module>r}      sQ      + 9 P ;   #$6
7UFL UFp*$. *$r'   