o
    Zh                     @   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gZ
G dd deZdS )	    N)Tensor)constraints)Distribution)_sum_rightmost)_size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efd	d
ZedefddZejdd ZedefddZedefddZedefddZe fdefddZe fded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__ N/var/www/auris/lib/python3.10/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.expandreturnc                 C      | j jS N)r   has_rsampler   r   r   r   r    K      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#   O   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$   U   s   zIndependent.supportc                 C   r   r   )r   meanr!   r   r   r   r&   \   r"   zIndependent.meanc                 C   r   r   )r   moder!   r   r   r   r'   `   r"   zIndependent.modec                 C   r   r   )r   variancer!   r   r   r   r(   d   r"   zIndependent.variancec                 C      | j |S r   )r   sampler   sample_shaper   r   r   r*   h      zIndependent.sampler,   c                 C   r)   r   )r   rsampler+   r   r   r   r.   k   r-   zIndependent.rsamplec                 C   s   | j |}t|| jS r   )r   log_probr   r   )r   valuer/   r   r   r   r/   n   s   zIndependent.log_probc                 C   s   | j  }t|| jS r   )r   entropyr   r   )r   r1   r   r   r   r1   r   s   
zIndependent.entropyTc                 C   s    | j dkr	td| jj|dS )Nr   z5Enumeration over cartesian product is not implemented)r   )r   NotImplementedErrorr   enumerate_support)r   r   r   r   r   r3   v   s
   
zIndependent.enumerate_supportc                 C   s   | j jd| j d| j d S )N(z, ))r   __name__r   r   r!   r   r   r   __repr__}   s   zIndependent.__repr__r   )T) r6   
__module____qualname____doc__r   dictstrr   
Constraint__annotations__r   r   propertyboolr    r#   Zdependent_propertyr$   r   r&   r'   r(   r   r   r*   r   r.   r/   r1   r3   r7   __classcell__r   r   r   r   r      s0   
 

)r   r   Ztorch.distributionsr   Z torch.distributions.distributionr   Ztorch.distributions.utilsr   Ztorch.typesr   __all__r   r   r   r   r   <module>   s   