a
    MSic                     @   s   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 G dd	 d	eZG d
d deZdS )    N)Number)constraints)Distribution)TransformedDistribution)SigmoidTransform)broadcast_allprobs_to_logitslogits_to_probslazy_propertyclamp_probsc                       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 )LogitRelaxedBernoullia  
    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 u |d u krtd|d ur>t|t}t|\| _nt|t}t|\| _|d urb| jn| j| _|rxt	 }n
| j
 }t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superr   __init__)selfr   r   r   r   	is_scalarbatch_shape	__class__ a/var/www/html/django/DPS/env/lib/python3.9/site-packages/torch/distributions/relaxed_bernoulli.pyr   !   s    



zLogitRelaxedBernoulli.__init__c                    s~   |  t|}t|}| j|_d| jv r>| j||_|j|_d| jv 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$   2   s    


zLogitRelaxedBernoulli.expandc                 O   s   | j j|i |S N)r   r(   )r   argskwargsr    r    r!   _new@   s    zLogitRelaxedBernoulli._newc                 C   s   t | jddS NT)	is_binary)r   r   r   r    r    r!   r   C   s    zLogitRelaxedBernoulli.logitsc                 C   s   t | jddS r-   )r	   r   r/   r    r    r!   r   G   s    zLogitRelaxedBernoulli.probsc                 C   s
   | j  S r)   )r   r   r/   r    r    r!   param_shapeK   s    z!LogitRelaxedBernoulli.param_shapec                 C   s\   |  |}t| j|}ttj||j|jd}| | 	  |  | 	  | j
 S )N)dtypedevice)_extended_shaper   r   r$   r   randr1   r2   loglog1pr   )r   sample_shapeshaper   Zuniformsr    r    r!   rsampleO   s    
zLogitRelaxedBernoulli.rsamplec                 C   sN   | j r| | t| j|\}}||| j }| j | d|    S )N   )	r%   _validate_sampler   r   mulr   r5   expr6   )r   valuer   diffr    r    r!   log_probU   s
    
zLogitRelaxedBernoulli.log_prob)NNN)N)__name__
__module____qualname____doc__r   unit_intervalrealarg_constraintssupportr   r$   r,   r
   r   r   propertyr0   r   r   r9   r@   __classcell__r    r    r   r!   r   
   s    


r   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 )RelaxedBernoullia  
    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::

        >>> 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t| j|t |d d S )Nr   )r   r   rK   r   r   )r   r   r   r   r   	base_distr   r    r!   r   u   s
    zRelaxedBernoulli.__init__c                    s    |  t|}tt| j||dS )N)r'   )r"   rK   r   r$   r&   r   r    r!   r$   {   s    zRelaxedBernoulli.expandc                 C   s   | j jS r)   )rL   r   r/   r    r    r!   r      s    zRelaxedBernoulli.temperaturec                 C   s   | j jS r)   )rL   r   r/   r    r    r!   r      s    zRelaxedBernoulli.logitsc                 C   s   | j jS r)   )rL   r   r/   r    r    r!   r      s    zRelaxedBernoulli.probs)NNN)N)rA   rB   rC   rD   r   rE   rF   rG   rH   has_rsampler   r$   rI   r   r   r   rJ   r    r    r   r!   rK   ]   s   

rK   )r   numbersr   Ztorch.distributionsr    torch.distributions.distributionr   ,torch.distributions.transformed_distributionr   Ztorch.distributions.transformsr   torch.distributions.utilsr   r   r	   r
   r   r   rK   r    r    r    r!   <module>   s   S