
    rhD                         d dl mZmZ 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)    )OptionalUnionN)Tensor)constraints)Distribution)TransformedDistribution)SigmoidTransform)broadcast_allclamp_probslazy_propertylogits_to_probsprobs_to_logits)_Number_sizeNumberLogitRelaxedBernoulliRelaxedBernoullic                   P    e Zd ZdZej
                  ej                  dZej                  Z	 	 	 dde	de
ee	ef      de
ee	ef      de
e   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&|J t        |t              }t	        |      \  | _        || j
                  n| j                  | _        |rt        j                         }n| j                  j                         }t        | 1  ||       y )Nz;Either `probs` or `logits` must be specified, but not both.r   )r   
ValueError
isinstancer   r
   r   r   _paramtorchSizesizesuper__init__)selfr   r   r   r   	is_scalarbatch_shape	__class__s          x/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.pyr$   zLogitRelaxedBernoulli.__init__.   s     'TMv~.M  "5'2I)%0MTZ%%%"673I*62NT[$)$5djj4;;**,K++**,KMB    c                    | j                  t        |      }t        j                  |      }| j                  |_        d| j
                  v r1| j                  j                  |      |_        |j                  |_        d| j
                  v r1| j                  j                  |      |_	        |j                  |_        t        t        |/  |d       | j                  |_        |S )Nr   r   Fr   )_get_checked_instancer   r    r!   r   __dict__r   expandr   r   r#   r$   _validate_argsr%   r'   	_instancenewr(   s       r)   r.   zLogitRelaxedBernoulli.expandH   s    (()>	Jjj-**dmm#

))+6CICJt}}$++K8CJCJ#S2;e2T!00
r*   c                 :     | j                   j                  |i |S N)r   r2   )r%   argskwargss      r)   _newzLogitRelaxedBernoulli._newV   s    t{{///r*   c                 0    t        | j                  d      S NT)	is_binary)r   r   r%   s    r)   r   zLogitRelaxedBernoulli.logitsY   s    tzzT::r*   c                 0    t        | j                  d      S r9   )r   r   r;   s    r)   r   zLogitRelaxedBernoulli.probs]   s    t{{d;;r*   c                 6    | j                   j                         S r4   )r   r"   r;   s    r)   param_shapez!LogitRelaxedBernoulli.param_shapea   s    {{!!r*   sample_shapec                 z   | j                  |      }t        | j                  j                  |            }t        t	        j
                  ||j                  |j                              }|j                         | j                         z
  |j                         z   | j                         z
  | j                  z  S )N)dtypedevice)_extended_shaper   r   r.   r    randrA   rB   loglog1pr   )r%   r?   shaper   uniformss        r)   rsamplezLogitRelaxedBernoulli.rsamplee   s    $$\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   rE   exprF   )r%   valuer   diffs       r)   log_probzLogitRelaxedBernoulli.log_probo   sy    !!%(%dkk59		$"2"233##%,q488:3C3C3E/EEEr*   NNNr4   )__name__
__module____qualname____doc__r   unit_intervalrealarg_constraintssupportr   r   r   r   boolr$   r.   r7   r   r   r   propertyr    r!   r>   r   rI   rQ   __classcell__r(   s   @r)   r   r      s   & !, 9 9[EUEUVOG
 2626(,CC ffn-.C vv~./	C
  ~C 
C4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eef      deeeef      d	ee   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$6mTr*   c                 R    | j                  t        |      }t        |   ||      S )N)r1   )r,   r   r#   r.   r0   s       r)   r.   zRelaxedBernoulli.expand   s)    (()99Ew~kS~99r*   c                 .    | j                   j                  S r4   )r`   r   r;   s    r)   r   zRelaxedBernoulli.temperature   s    ~~)))r*   c                 .    | j                   j                  S r4   )r`   r   r;   s    r)   r   zRelaxedBernoulli.logits   s    ~~$$$r*   c                 .    | j                   j                  S r4   )r`   r   r;   s    r)   r   zRelaxedBernoulli.probs   s    ~~###r*   rR   r4   )rS   rT   rU   rV   r   rW   rX   rY   rZ   has_rsampler   __annotations__r   r   r   r   r[   r$   r.   r\   r   r   r   r]   r^   s   @r)   r   r   w   s    ( !, 9 9[EUEUVO''GK$$
 2626(,UU ffn-.U vv~./	U
  ~U 
U: *V * * % % % $v $ $r*   )typingr   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>rq      sW    "   + 9 P ;  / . #$6
7]FL ]F@2$. 2$r*   