
    ǄgP                     d    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 dl	m
Z
 dgZ G d de      Zy)	    )DictN)constraints)Distribution)_sum_rightmost)_sizeIndependentc                   \    e Zd ZU dZi Zeeej                  f   e	d<   	 d fd	Z
d fd	Zed        Zed        Zej                  d        Zed        Zed	        Zed
        Z ej*                         fdZ ej*                         fdedej0                  fdZd Zd ZddZd Z x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_constraintsc                 d   |t        |j                        kD  r$t        d| dt        |j                               |j                  |j                  z   }|t        |j                        z   }|d t        |      |z
   }|t        |      |z
  d  }|| _        || _        t        |   |||       y )NzQExpected reinterpreted_batch_ndims <= len(base_distribution.batch_shape), actual z vs validate_args)lenbatch_shape
ValueErrorevent_shape	base_distreinterpreted_batch_ndimssuper__init__)	selfbase_distributionr   r   shape	event_dimr   r   	__class__s	           g/home/mcse/projects/flask_80/flask-venv/lib/python3.12/site-packages/torch/distributions/independent.pyr   zIndependent.__init__-   s     %s+<+H+H'II34D=N=Z=Z9[8\^  "--0A0M0MM-4E4Q4Q0RR	4c%j945CJ245*)B&kO    c                 V   | j                  t        |      }t        j                  |      }| j                  j                  || j                  d | j                   z         |_        | j                  |_        t        t        |'  || j                  d       | j                  |_
        |S )NFr   )_get_checked_instancer   torchSizer   expandr   r   r   r   _validate_args)r   r   	_instancenewr   s       r   r!   zIndependent.expand=   s    ((i@jj---$**+KT-K-KLL
 )-(F(F%k3()) 	) 	
 "00
r   c                 .    | j                   j                  S N)r   has_rsampler   s    r   r'   zIndependent.has_rsampleJ   s    ~~)))r   c                 N    | j                   dkD  ry| j                  j                  S )Nr   F)r   r   has_enumerate_supportr(   s    r   r*   z!Independent.has_enumerate_supportN   s#    ))A-~~333r   c                     | j                   j                  }| j                  r t        j                  || j                        }|S r&   )r   supportr   r   independent)r   results     r   r,   zIndependent.supportT   s7    '')) ,,VT5S5STFr   c                 .    | j                   j                  S r&   )r   meanr(   s    r   r0   zIndependent.mean[       ~~"""r   c                 .    | j                   j                  S r&   )r   moder(   s    r   r3   zIndependent.mode_   r1   r   c                 .    | j                   j                  S r&   )r   variancer(   s    r   r5   zIndependent.variancec   s    ~~&&&r   c                 8    | j                   j                  |      S r&   )r   sampler   sample_shapes     r   r7   zIndependent.sampleg   s    ~~$$\22r   r9   returnc                 8    | j                   j                  |      S r&   )r   rsampler8   s     r   r<   zIndependent.rsamplej   s    ~~%%l33r   c                 d    | j                   j                  |      }t        || j                        S r&   )r   log_probr   r   )r   valuer>   s      r   r>   zIndependent.log_probm   s)    >>**51h(F(FGGr   c                 b    | j                   j                         }t        || j                        S r&   )r   entropyr   r   )r   rA   s     r   rA   zIndependent.entropyq   s'    ..((*gt'E'EFFr   c                 n    | j                   dkD  rt        d      | j                  j                  |      S )Nr   z5Enumeration over cartesian product is not implemented)r!   )r   NotImplementedErrorr   enumerate_support)r   r!   s     r   rD   zIndependent.enumerate_supportu   s:    ))A-%G  ~~//v/>>r   c                 j    | j                   j                  d| j                   d| j                   dz   S )N(z, ))r   __name__r   r   r(   s    r   __repr__zIndependent.__repr__|   s8    NN##$..!D$B$B#C1EF	
r   r&   )T)rH   
__module____qualname____doc__r
   r   strr   
Constraint__annotations__r   r!   propertyr'   r*   dependent_propertyr,   r0   r3   r5   r   r    r7   r   Tensorr<   r>   rA   rD   rI   __classcell__)r   s   @r   r   r      s    8 :<OT#{5556; KOP  * * 4 4
 ## $ # # # # ' ' #-%**, 3 -7EJJL 4E 4U\\ 4HG?
r   )typingr   r   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr   torch.typesr   __all__r    r   r   <module>r[      s.      + 9 4  /r
, r
r   