
    j                         d dl mZmZ d dlZd dlmZ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 ed	e	
          Z G d de	ee                   ZdS )    )GenericTypeVarN)SizeTensor)constraints)Distribution)_sum_rightmost)_sizeIndependentD)boundc            	           e Zd ZU dZi Zeeej        f         e	d<   e
e	d<   	 dde
dededz  ddf fd	Zd fd
	Zedefd            Zedefd            Zej        d             Zedefd            Zedefd            Zedefd            Z ej                    fdefdZ ej                    fdede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_constraints	base_distNbase_distributionreinterpreted_batch_ndimsvalidate_argsreturnc                    |t          |j                  k    r't          d| dt          |j                             |j        |j        z   }|t          |j                  z   }|d t          |          |z
           }|t          |          |z
  d          }|| _        || _        t                                          |||           d S )NzQExpected reinterpreted_batch_ndims <= len(base_distribution.batch_shape), actual z vs r   )lenbatch_shape
ValueErrorevent_shaper   r   super__init__)	selfr   r   r   shape	event_dimr   r   	__class__s	           i/lsinfo/ai/hellotax_ai/base_platform/venv/lib/python3.11/site-packages/torch/distributions/independent.pyr   zIndependent.__init__3   s     %s+<+H'I'III^3^ ^9<=N=Z9[9[^ ^   (36G6SS2S9J9V5W5WW	4c%jj9445CJJ2445*)B&kOOOOO    c                 ^   |                      t          |          }t          j        |          }| j                            || j        d | j                 z             |_        | j        |_        t          t          |          	                    || j        d           | j
        |_
        |S )NFr   )_get_checked_instancer   torchr   r   expandr   r   r   r   _validate_args)r   r   	_instancenewr    s       r!   r&   zIndependent.expandG   s    ((i@@j----$*+KT-K+KLL
 
 )-(F%k3(() 	) 	
 	
 	
 "0
r"   c                     | j         j        S N)r   has_rsampler   s    r!   r,   zIndependent.has_rsampleT   s    ~))r"   c                 4    | j         dk    rdS | j        j        S )Nr   F)r   r   has_enumerate_supportr-   s    r!   r/   z!Independent.has_enumerate_supportX   s     )A--5~33r"   c                 `    | j         j        }| j        rt          j        || j                  }|S r+   )r   supportr   r   independent)r   results     r!   r1   zIndependent.support^   s4     ') 	U ,VT5STTFr"   c                     | j         j        S r+   )r   meanr-   s    r!   r5   zIndependent.meanf       ~""r"   c                     | j         j        S r+   )r   moder-   s    r!   r8   zIndependent.modej   r6   r"   c                     | j         j        S r+   )r   variancer-   s    r!   r:   zIndependent.variancen   s    ~&&r"   c                 6    | j                             |          S r+   )r   sampler   sample_shapes     r!   r<   zIndependent.sampler   s    ~$$\222r"   r>   c                 6    | j                             |          S r+   )r   rsampler=   s     r!   r@   zIndependent.rsampleu   s    ~%%l333r"   c                 `    | j                             |          }t          || j                  S r+   )r   log_probr	   r   )r   valuerB   s      r!   rB   zIndependent.log_probx   s*    >**511h(FGGGr"   c                 ^    | j                                         }t          || j                  S r+   )r   entropyr	   r   )r   rE   s     r!   rE   zIndependent.entropy|   s(    .((**gt'EFFFr"   Tc                 l    | j         dk    rt          d          | j                            |          S )Nr   z5Enumeration over cartesian product is not implemented)r&   )r   NotImplementedErrorr   enumerate_support)r   r&   s     r!   rH   zIndependent.enumerate_support   s@    )A--%G   ~//v/>>>r"   c                 B    | j         j        d| j         d| j         dz   S )N(z, ))r    __name__r   r   r-   s    r!   __repr__zIndependent.__repr__   s0    N#E$.EED$BEEEF	
r"   r+   )T)"rL   
__module____qualname____doc__r   dictstrr   
Constraint__annotations__r   intboolr   r&   propertyr,   r/   dependent_propertyr1   r   r5   r8   r:   r%   r   r<   r
   r@   rB   rE   rH   rM   __classcell__)r    s   @r!   r   r      sX         : :<OT#{556;;;LLL &*	P PP $'P d{	P
 
P P P P P P(      *T * * * X* 4t 4 4 4 X4
 #  $# #f # # # X# #f # # # X# '& ' ' ' X' #-%*,, 3 36 3 3 3 3 -7EJLL 4 4E 4V 4 4 4 4H H HG G G? ? ? ?
 
 
 
 
 
 
r"   )typingr   r   r%   r   r   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr	   torch.typesr
   __all__r   r    r"   r!   <module>ra      s    # # # # # # # #          + + + + + + 9 9 9 9 9 9 4 4 4 4 4 4       / GC|$$$y
 y
 y
 y
 y
,
 y
 y
 y
 y
 y
r"   