U
    9%e*                     @   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 dd	gZG d
d deZG dd	 d	eZdS )    N)constraints)Categorical)Distribution)TransformedDistribution)ExpTransform)broadcast_allclamp_probsExpRelaxedCategoricalRelaxedOneHotCategoricalc                       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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 ExpRelaxedCategorical parameterized by
    :attr:`temperature`, and either :attr:`probs` or :attr:`logits` (but not both).
    Returns the log of a point in the simplex. Based on the interface to
    :class:`OneHotCategorical`.

    Implementation based on [1].

    See also: :func:`torch.distributions.OneHotCategorical`

    Args:
        temperature (Tensor): relaxation temperature
        probs (Tensor): event probabilities
        logits (Tensor): unnormalized log probability for each event

    [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logitsTNc                    s@   t ||| _|| _| jj}| jjdd  }t j|||d d S )Nvalidate_args)r   _categoricaltemperaturebatch_shapeparam_shapesuper__init__)selfr   r   r   r   r   event_shape	__class__ f/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/distributions/relaxed_categorical.pyr   (   s
    zExpRelaxedCategorical.__init__c                    sP   |  t|}t|}| j|_| j||_tt|j|| j	dd | j
|_
|S )NFr   )_get_checked_instancer	   torchSizer   r   expandr   r   r   _validate_argsr   r   	_instancenewr   r   r   r    /   s    

  zExpRelaxedCategorical.expandc                 O   s   | j j||S N)r   _new)r   argskwargsr   r   r   r&   :   s    zExpRelaxedCategorical._newc                 C   s   | j jS r%   )r   r   r   r   r   r   r   =   s    z!ExpRelaxedCategorical.param_shapec                 C   s   | j jS r%   )r   r   r)   r   r   r   r   A   s    zExpRelaxedCategorical.logitsc                 C   s   | j jS r%   )r   r   r)   r   r   r   r   E   s    zExpRelaxedCategorical.probsc                 C   sX   |  |}ttj|| jj| jjd}|    }| j| | j }||j	ddd S )N)dtypedevicer   TdimZkeepdim)
Z_extended_shaper   r   Zrandr   r*   r+   logr   	logsumexp)r   Zsample_shapeshapeZuniformsZgumbelsZscoresr   r   r   rsampleI   s    
zExpRelaxedCategorical.rsamplec                 C   s   | j j}| jr| | t| j|\}}t| jt	|
 | j |d   }||| j }||jddd d}|| S )N   r   Tr,   )r   Z_num_eventsr!   Z_validate_sampler   r   r   Z	full_liker   floatlgammar.   mulr/   sum)r   valueKr   Z	log_scaleZscorer   r   r   log_probR   s    
 zExpRelaxedCategorical.log_prob)NNN)N)__name__
__module____qualname____doc__r   simplexreal_vectorarg_constraintssupporthas_rsampler   r    r&   propertyr   r   r   r   r   r1   r9   __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 RelaxedOneHotCategorical distribution parametrized by
    :attr:`temperature`, and either :attr:`probs` or :attr:`logits`.
    This is a relaxed version of the :class:`OneHotCategorical` distribution, so
    its samples are on simplex, and are reparametrizable.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
        >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
        ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
        >>> m.sample()
        tensor([ 0.1294,  0.2324,  0.3859,  0.2523])

    Args:
        temperature (Tensor): relaxation temperature
        probs (Tensor): event probabilities
        logits (Tensor): unnormalized log probability for each event
    r   TNc                    s(   t ||||d}t j|t |d d S )Nr   )r	   r   r   r   )r   r   r   r   r   	base_distr   r   r   r   w   s       z!RelaxedOneHotCategorical.__init__c                    s   |  t|}t j||dS )N)r#   )r   r
   r   r    r"   r   r   r   r    }   s    zRelaxedOneHotCategorical.expandc                 C   s   | j jS r%   )rE   r   r)   r   r   r   r      s    z$RelaxedOneHotCategorical.temperaturec                 C   s   | j jS r%   )rE   r   r)   r   r   r   r      s    zRelaxedOneHotCategorical.logitsc                 C   s   | j jS r%   )rE   r   r)   r   r   r   r      s    zRelaxedOneHotCategorical.probs)NNN)N)r:   r;   r<   r=   r   r>   r?   r@   rA   rB   r   r    rC   r   r   r   rD   r   r   r   r   r
   _   s   

)r   Ztorch.distributionsr   Ztorch.distributions.categoricalr   Z torch.distributions.distributionr   Z,torch.distributions.transformed_distributionr   Ztorch.distributions.transformsr   Ztorch.distributions.utilsr   r   __all__r	   r
   r   r   r   r   <module>   s   S