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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__)selfbase_distributionr   r
   shape	event_dimr   r   	__class__ s/var/www/html/construction_image-detection-poc/venv/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	   )_get_checked_instancer   torchSizer   expandr   r   r   r   _validate_args)r   r   	_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$   )r   supportr   r   independent)r   resultr   r   r   r)   U   s   zIndependent.supportc                 C   r#   r$   )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$   )r   sampler   sample_shaper   r   r   r0   h      zIndependent.sampler2   c                 C   r/   r$   )r   rsampler1   r   r   r   r4   k   r3   zIndependent.rsamplec                 C   s   | j |}t|| jS r$   )r   log_probr   r   )r   valuer5   r   r   r   r5   n   s   zIndependent.log_probc                 C   s   | j  }t|| jS r$   )r   entropyr   r   )r   r7   r   r   r   r7   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   r9   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) r<   
__module____qualname____doc__r   dictstrr   
Constraint__annotations__r   r   propertyboolr%   r(   dependent_propertyr)   r   r,   r-   r.   r   r   r0   r   r4   r5   r7   r9   r=   __classcell__r   r   r   r   r      s0   
 

)r   r   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr   torch.typesr   __all__r   r   r   r   r   <module>   s   