
    9j                         d dl Z d dl mZ d dlmZ d dlm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mZmZ d	d
gZ G d d	e      Z G d d
e      Zy)    N)Tensor)constraints)Distribution)TransformedDistribution)SigmoidTransform)broadcast_allclamp_probslazy_propertylogits_to_probsprobs_to_logits)_Number_sizeNumberLogitRelaxedBernoulliRelaxedBernoullic                   H    e Zd ZdZej
                  ej                  dZej                  Z	 	 	 dde	de	e
z  dz  de	e
z  dz  dedz  ddf
 fd	Zd fd
	Zd Zede	fd       Zede	fd       Zedej(                  fd       Z ej(                         fdede	fdZd Z xZS )r   a  
    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logitsNtemperaturer   r   validate_argsreturnc                    || _         |d u |d u k(  rt        d      |#t        |t              }t	        |      \  | _        n/|t        d      t        |t              }t	        |      \  | _        || j
                  n| j                  | _        |rt        j                         }n| j                  j                         }t        | 5  ||       y )Nz;Either `probs` or `logits` must be specified, but not both.zlogits is unexpectedly Noner   )r   
ValueError
isinstancer   r   r   AssertionErrorr   _paramtorchSizesizesuper__init__)selfr   r   r   r   	is_scalarbatch_shape	__class__s          e/media/conek/DATA/Code/OCR/venv/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.pyr#   zLogitRelaxedBernoulli.__init__.   s     'TMv~.M  "5'2I)%0MTZ~$%BCC"673I*62NT[$)$5djj4;;**,K++**,KMB    c                    | j                  t        |      }t        j                  |      }| j                  |_        d| j
                  v r1| j                  j                  |      |_        |j                  |_        d| j
                  v r1| j                  j                  |      |_	        |j                  |_        t        t        |/  |d       | j                  |_        |S )Nr   r   Fr   )_get_checked_instancer   r   r    r   __dict__r   expandr   r   r"   r#   _validate_argsr$   r&   	_instancenewr'   s       r(   r-   zLogitRelaxedBernoulli.expandK   s    (()>	Jjj-**dmm#

))+6CICJt}}$++K8CJCJ#S2;e2T!00
r)   c                 :     | j                   j                  |i |S N)r   r1   )r$   argskwargss      r(   _newzLogitRelaxedBernoulli._newY   s    t{{///r)   c                 0    t        | j                  d      S NT)	is_binary)r   r   r$   s    r(   r   zLogitRelaxedBernoulli.logits\   s    tzzT::r)   c                 0    t        | j                  d      S r8   )r   r   r:   s    r(   r   zLogitRelaxedBernoulli.probs`   s    t{{d;;r)   c                 6    | j                   j                         S r3   )r   r!   r:   s    r(   param_shapez!LogitRelaxedBernoulli.param_shaped   s    {{!!r)   sample_shapec                 z   | j                  |      }t        | j                  j                  |            }t        t	        j
                  ||j                  |j                              }|j                         | j                         z
  |j                         z   | j                         z
  | j                  z  S )N)dtypedevice)_extended_shaper	   r   r-   r   randr@   rA   loglog1pr   )r$   r>   shaper   uniformss        r(   rsamplezLogitRelaxedBernoulli.rsampleh   s    $$\2DJJ--e45JJuEKKE
 LLNxi..00599;>5&AQQ 	r)   c                 (   | j                   r| j                  |       t        | j                  |      \  }}||j	                  | j
                        z
  }| j
                  j                         |z   d|j                         j                         z  z
  S )N   )	r.   _validate_sampler   r   mulr   rD   exprE   )r$   valuer   diffs       r(   log_probzLogitRelaxedBernoulli.log_probr   sy    !!%(%dkk59		$"2"233##%,q488:3C3C3E/EEEr)   NNNr3   )__name__
__module____qualname____doc__r   unit_intervalrealarg_constraintssupportr   r   boolr#   r-   r6   r
   r   r   propertyr   r    r=   r   rH   rP   __classcell__r'   s   @r(   r   r      s   ( !, 9 9[EUEUVOG
 )-)-%)CC %C $&	C
 d{C 
C:0 ; ; ; <v < < "UZZ " " -7EJJL E V Fr)   c                       e Zd ZU dZej
                  ej                  dZej
                  ZdZ	e
ed<   	 	 	 ddedeez  dz  deez  dz  d	edz  d
df
 fdZd fd	Zed
efd       Zed
efd       Zed
efd       Z xZS )r   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   T	base_distNr   r   r   r   r   c                 T    t        |||      }t        | 	  |t               |       y )Nr   )r   r"   r#   r   )r$   r   r   r   r   r_   r'   s         r(   r#   zRelaxedBernoulli.__init__   s+     *+ufE	$4$6mTr)   c                 R    | j                  t        |      }t        |   ||      S )N)r0   )r+   r   r"   r-   r/   s       r(   r-   zRelaxedBernoulli.expand   s)    (()99Ew~kS~99r)   c                 .    | j                   j                  S r3   )r_   r   r:   s    r(   r   zRelaxedBernoulli.temperature   s    ~~)))r)   c                 .    | j                   j                  S r3   )r_   r   r:   s    r(   r   zRelaxedBernoulli.logits   s    ~~$$$r)   c                 .    | j                   j                  S r3   )r_   r   r:   s    r(   r   zRelaxedBernoulli.probs   s    ~~###r)   rQ   r3   )rR   rS   rT   rU   r   rV   rW   rX   rY   has_rsampler   __annotations__r   r   rZ   r#   r-   r[   r   r   r   r\   r]   s   @r(   r   r   z   s    ( !, 9 9[EUEUVO''GK$$
 )-)-%)UU %U $&	U
 d{U 
U: *V * * % % % $v $ $r)   )r   r   torch.distributionsr    torch.distributions.distributionr   ,torch.distributions.transformed_distributionr   torch.distributions.transformsr   torch.distributions.utilsr   r	   r
   r   r   torch.typesr   r   r   __all__r   r    r)   r(   <module>ro      sV      + 9 P ;  / . #$6
7aFL aFH4$. 4$r)   