U
    9%e                     @   s   d dl 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mZmZmZmZ dd	gZG d
d deZG dd	 d	eZdS )    )NumberN)constraints)Distribution)TransformedDistribution)SigmoidTransform)broadcast_allclamp_probslazy_propertylogits_to_probsprobs_to_logitsLogitRelaxedBernoulliRelaxedBernoullic                       s   e Zd ZdZejejdZejZd fdd	Z	d fdd	Z
dd	 Zed
d Zedd Zedd Ze fddZdd Z  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logitsNc                    s   || _ |d k|d kkrtd|d k	r>t|t}t|\| _nt|t}t|\| _|d k	rb| jn| j| _|rxt	 }n
| j
 }t j||d d S )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   Z	is_scalarbatch_shape	__class__ d/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/distributions/relaxed_bernoulli.pyr   )   s    



zLogitRelaxedBernoulli.__init__c                    s~   |  t|}t|}| j|_d| jkr>| j||_|j|_d| jkr^| j	||_	|j	|_t
t|j|dd | j|_|S )Nr   r   Fr   )_get_checked_instancer   r   r   r   __dict__r   expandr   r   r   r   _validate_argsr   r   	_instancenewr   r    r!   r$   <   s    


zLogitRelaxedBernoulli.expandc                 O   s   | j j||S N)r   r(   )r   argskwargsr    r    r!   _newJ   s    zLogitRelaxedBernoulli._newc                 C   s   t | jddS NT)Z	is_binary)r   r   r   r    r    r!   r   M   s    zLogitRelaxedBernoulli.logitsc                 C   s   t | jddS r-   )r
   r   r.   r    r    r!   r   Q   s    zLogitRelaxedBernoulli.probsc                 C   s
   | j  S r)   )r   r   r.   r    r    r!   param_shapeU   s    z!LogitRelaxedBernoulli.param_shapec                 C   s\   |  |}t| j|}ttj||j|jd}| | 	  |  | 	  | j
 S )N)dtypedevice)Z_extended_shaper   r   r$   r   Zrandr0   r1   loglog1pr   )r   Zsample_shapeshaper   Zuniformsr    r    r!   rsampleY   s    
"zLogitRelaxedBernoulli.rsamplec                 C   sN   | j r| | t| j|\}}||| j }| j | d|    S )N   )	r%   Z_validate_sampler   r   mulr   r2   expr3   )r   valuer   diffr    r    r!   log_probc   s
    
zLogitRelaxedBernoulli.log_prob)NNN)N)__name__
__module____qualname____doc__r   unit_intervalrealarg_constraintssupportr   r$   r,   r	   r   r   propertyr/   r   r   r5   r;   __classcell__r    r    r   r!   r      s   



c                       sl   e Zd ZdZejejdZejZdZ	d fdd	Z
d fdd	Zed	d
 Zedd Zedd Z  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-deterinistic")
        >>> 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   TNc                    s$   t |||}t j|t |d d S )Nr   )r   r   r   r   )r   r   r   r   r   	base_distr   r    r!   r      s    zRelaxedBernoulli.__init__c                    s   |  t|}t j||dS )N)r'   )r"   r   r   r$   r&   r   r    r!   r$      s    zRelaxedBernoulli.expandc                 C   s   | j jS r)   )rF   r   r.   r    r    r!   r      s    zRelaxedBernoulli.temperaturec                 C   s   | j jS r)   )rF   r   r.   r    r    r!   r      s    zRelaxedBernoulli.logitsc                 C   s   | j jS r)   )rF   r   r.   r    r    r!   r      s    zRelaxedBernoulli.probs)NNN)N)r<   r=   r>   r?   r   r@   rA   rB   rC   Zhas_rsampler   r$   rD   r   r   r   rE   r    r    r   r!   r   k   s   

)numbersr   r   Ztorch.distributionsr   Z torch.distributions.distributionr   Z,torch.distributions.transformed_distributionr   Ztorch.distributions.transformsr   Ztorch.distributions.utilsr   r   r	   r
   r   __all__r   r   r    r    r    r!   <module>   s   X