o
    h                     @   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 d dlmZmZ d	d
gZG dd	 d	eZG dd
 d
eZdS )    N)Tensor)constraints)Distribution)TransformedDistribution)SigmoidTransform)broadcast_allclamp_probslazy_propertylogits_to_probsprobs_to_logits)_Number_size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
efddZed
efddZed
ejfddZe fded
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 u |d u krtd|d urt|t}t|\| _nt|t}t|\| _|d ur1| jn| j| _|r<t	 }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   	is_scalarbatch_shape	__class__ y/var/www/html/construction_image-detection-poc/venv/lib/python3.10/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$   r'   ?   s   


zLogitRelaxedBernoulli.expandc                 O   s   | j j|i |S N)r   r+   )r   argskwargsr#   r#   r$   _newM   s   zLogitRelaxedBernoulli._newreturnc                 C      t | jddS NT)	is_binary)r   r   r   r#   r#   r$   r   P      zLogitRelaxedBernoulli.logitsc                 C   r1   r2   )r
   r   r4   r#   r#   r$   r   T   r5   zLogitRelaxedBernoulli.probsc                 C   s
   | j  S r,   )r   r   r4   r#   r#   r$   param_shapeX   s   
z!LogitRelaxedBernoulli.param_shapesample_shapec                 C   s\   |  |}t| j|}ttj||j|jd}| | 	  |  | 	  | j
 S )N)dtypedevice)_extended_shaper   r   r'   r   randr8   r9   loglog1pr   )r   r7   shaper   uniformsr#   r#   r$   rsample\   s   
"zLogitRelaxedBernoulli.rsamplec                 C   sN   | j r| | t| j|\}}||| j }| j | d|    S )N   )	r(   _validate_sampler   r   mulr   r<   expr=   )r   valuer   diffr#   r#   r$   log_probf   s
   
zLogitRelaxedBernoulli.log_probNNNr,   )__name__
__module____qualname____doc__r   unit_intervalrealarg_constraintssupportr   r'   r/   r	   r   r   r   propertyr   r   r6   r   r@   rG   __classcell__r#   r#   r!   r$   r      s    
c                       s~   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	efd
dZed	efddZed	ef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-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   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$   r'      s   zRelaxedBernoulli.expandr0   c                 C      | j jS r,   )rS   r   r4   r#   r#   r$   r         zRelaxedBernoulli.temperaturec                 C   rT   r,   )rS   r   r4   r#   r#   r$   r      rU   zRelaxedBernoulli.logitsc                 C   rT   r,   )rS   r   r4   r#   r#   r$   r      rU   zRelaxedBernoulli.probsrH   r,   )rI   rJ   rK   rL   r   rM   rN   rO   rP   has_rsampler   r'   rQ   r   r   r   r   rR   r#   r#   r!   r$   r   n   s    )r   r   torch.distributionsr    torch.distributions.distributionr   ,torch.distributions.transformed_distributionr   torch.distributions.transformsr   torch.distributions.utilsr   r   r	   r
   r   torch.typesr   r   __all__r   r   r#   r#   r#   r$   <module>   s   Y