U
    9%e                     @   sR   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gZ	G dd deZ
dS )    )DictN)constraints)Distribution)_sum_rightmostIndependentc                       s   e Zd ZU dZi Zeeejf e	d< d! fdd	Z
d" fdd	Zedd	 Zed
d Zejdd Zedd Zedd Zedd Ze fddZe fddZdd Zdd Zd#ddZdd  Z  ZS )$r   a  
    Reinterprets some of the batch dims of a distribution as event dims.

    This is mainly useful for changing the shape of the result of
    :meth:`log_prob`. For example to create a diagonal Normal distribution with
    the same shape as a Multivariate Normal distribution (so they are
    interchangeable), you can::

        >>> from torch.distributions.multivariate_normal import MultivariateNormal
        >>> from torch.distributions.normal import Normal
        >>> loc = torch.zeros(3)
        >>> scale = torch.ones(3)
        >>> mvn = MultivariateNormal(loc, scale_tril=torch.diag(scale))
        >>> [mvn.batch_shape, mvn.event_shape]
        [torch.Size([]), torch.Size([3])]
        >>> normal = Normal(loc, scale)
        >>> [normal.batch_shape, normal.event_shape]
        [torch.Size([3]), torch.Size([])]
        >>> diagn = Independent(normal, 1)
        >>> [diagn.batch_shape, diagn.event_shape]
        [torch.Size([]), torch.Size([3])]

    Args:
        base_distribution (torch.distributions.distribution.Distribution): a
            base distribution
        reinterpreted_batch_ndims (int): the number of batch dims to
            reinterpret as event dims
    arg_constraintsNc                    s   |t |jkr(td| dt |j |j|j }|t |j }|d t ||  }|t || d  }|| _|| _t j|||d d S )NzQExpected reinterpreted_batch_ndims <= len(base_distribution.batch_shape), actual z vs validate_args)lenbatch_shape
ValueErrorevent_shape	base_distreinterpreted_batch_ndimssuper__init__)selfZbase_distributionr   r	   shapeZ	event_dimr   r   	__class__ ^/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/distributions/independent.pyr   *   s    zIndependent.__init__c                    s`   |  t|}t|}| j|| jd | j  |_| j|_tt|j	|| jdd | j
|_
|S )NFr   )Z_get_checked_instancer   torchSizer   expandr   r   r   r   Z_validate_args)r   r   Z	_instancenewr   r   r   r   :   s    

  zIndependent.expandc                 C   s   | j jS N)r   has_rsampler   r   r   r   r   G   s    zIndependent.has_rsamplec                 C   s   | j dkrdS | jjS )Nr   F)r   r   has_enumerate_supportr   r   r   r   r   K   s    
z!Independent.has_enumerate_supportc                 C   s    | j j}| jrt|| j}|S r   )r   supportr   r   Zindependent)r   resultr   r   r   r    Q   s    zIndependent.supportc                 C   s   | j jS r   )r   meanr   r   r   r   r"   X   s    zIndependent.meanc                 C   s   | j jS r   )r   moder   r   r   r   r#   \   s    zIndependent.modec                 C   s   | j jS r   )r   variancer   r   r   r   r$   `   s    zIndependent.variancec                 C   s   | j |S r   )r   sampler   Zsample_shaper   r   r   r%   d   s    zIndependent.samplec                 C   s   | j |S r   )r   rsampler&   r   r   r   r'   g   s    zIndependent.rsamplec                 C   s   | j |}t|| jS r   )r   log_probr   r   )r   valuer(   r   r   r   r(   j   s    zIndependent.log_probc                 C   s   | j  }t|| jS r   )r   entropyr   r   )r   r*   r   r   r   r*   n   s    
zIndependent.entropyTc                 C   s    | j dkrtd| jj|dS )Nr   z5Enumeration over cartesian product is not implemented)r   )r   NotImplementedErrorr   enumerate_support)r   r   r   r   r   r,   r   s
    
zIndependent.enumerate_supportc                 C   s   | j jd| j d| j d S )N(z, ))r   __name__r   r   r   r   r   r   __repr__y   s    zIndependent.__repr__)N)N)T)r/   
__module____qualname____doc__r   r   strr   
Constraint__annotations__r   r   propertyr   r   Zdependent_propertyr    r"   r#   r$   r   r   r%   r'   r(   r*   r,   r0   __classcell__r   r   r   r   r      s.   
 






)typingr   r   Ztorch.distributionsr   Z torch.distributions.distributionr   Ztorch.distributions.utilsr   __all__r   r   r   r   r   <module>   s   