
    '}hK                     t    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	d Z
 G d d	e      Z G d
 de      Zy)    N)Function)once_differentiable)constraints)ExponentialFamily	Dirichletc                     |j                  dd      j                  |      }t        j                  | ||      }||| |z  j                  dd      z
  z  S NT)sum	expand_astorch_dirichlet_grad)xconcentrationgrad_outputtotalgrads        \/var/www/html/test/engine/venv/lib/python3.12/site-packages/torch/distributions/dirichlet.py_Dirichlet_backwardr      sT    b$'11-@E  M59D;!k/!6!6r4!@@AA    c                   6    e Zd Zed        Zeed               Zy)
_Dirichletc                 T    t        j                  |      }| j                  ||       |S N)r   _sample_dirichletsave_for_backward)ctxr   r   s      r   forwardz_Dirichlet.forward   s'    ##M2a/r   c                 :    | j                   \  }}t        |||      S r   )saved_tensorsr   )r   r   r   r   s       r   backwardz_Dirichlet.backward   s#     ,,="1m[AAr   N)__name__
__module____qualname__staticmethodr   r   r!    r   r   r   r      s2     
 B  Br   r   c                        e Zd ZdZd ej
                  ej                  d      iZej                  Z	dZ
d fd	Zd fd	ZddZd Zed	        Zed
        Zed        Zd Zed        Zd Z xZS )r   a  
    Creates a Dirichlet distribution parameterized by concentration :attr:`concentration`.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Dirichlet(torch.tensor([0.5, 0.5]))
        >>> m.sample()  # Dirichlet distributed with concentration [0.5, 0.5]
        tensor([ 0.1046,  0.8954])

    Args:
        concentration (Tensor): concentration parameter of the distribution
            (often referred to as alpha)
    r      Tc                     |j                         dk  rt        d      || _        |j                  d d |j                  dd  }}t        |   |||       y )Nr(   z;`concentration` parameter must be at least one-dimensional.r
   validate_args)dim
ValueErrorr   shapesuper__init__)selfr   r+   batch_shapeevent_shape	__class__s        r   r0   zDirichlet.__init__4   sg    "M  +#0#6#6s#;]=P=PQSQT=U[kOr   c                    | j                  t        |      }t        j                  |      }| j                  j                  || j                  z         |_        t        t        |#  || j                  d       | j                  |_	        |S )NFr*   )
_get_checked_instancer   r   Sizer   expandr3   r/   r0   _validate_args)r1   r2   	_instancenewr4   s       r   r8   zDirichlet.expand=   s}    ((I>jj- ..55kDDTDT6TUi&)) 	' 	
 "00
r   c                     | j                  |      }| j                  j                  |      }t        j	                  |      S r   )_extended_shaper   r8   r   apply)r1   sample_shaper.   r   s       r   rsamplezDirichlet.rsampleG   s9    $$\2**11%8..r   c                 \   | j                   r| j                  |       t        j                  | j                  dz
  |      j                  d      t        j                  | j                  j                  d            z   t        j                  | j                        j                  d      z
  S )N      ?r
   )r9   _validate_sampler   xlogyr   r   lgamma)r1   values     r   log_probzDirichlet.log_probL   s    !!%(KK**S0%8<<R@ll4--11"567ll4--.22267	
r   c                 T    | j                   | j                   j                  dd      z  S r	   )r   r   r1   s    r   meanzDirichlet.meanU   s&    !!D$6$6$:$:2t$DDDr   c                 d   | j                   dz
  j                  d      }||j                  dd      z  }| j                   dk  j                  d      }t        j
                  j                  j                  ||   j                  d      |j                  d         j                  |      ||<   |S )Nr(   g        )minr
   T)axis)r   clampr   allr   nn
functionalone_hotargmaxr.   to)r1   concentrationm1modemasks       r   rV   zDirichlet.modeY   s    --188S8A!4!4R!>>""Q&+++4XX((00J2&(=(=b(A

"T( 	T
 r   c                     | j                   j                  dd      }| j                   || j                   z
  z  |j                  d      |dz   z  z  S )Nr
   T   r(   )r   r   pow)r1   con0s     r   variancezDirichlet.variancec   sT    !!%%b$/d(((*xx{dQh')	
r   c                    | j                   j                  d      }| j                   j                  d      }t        j                  | j                         j                  d      t        j                  |      z
  ||z
  t        j
                  |      z  z
  | j                   dz
  t        j
                  | j                         z  j                  d      z
  S )Nr
   rB   )r   sizer   r   rE   digamma)r1   ka0s      r   entropyzDirichlet.entropyl   s    ##B'##B'LL++,004ll22vr**+ ""S(EMM$:L:L,MMRRSUVW	
r   c                     | j                   fS r   )r   rI   s    r   _natural_paramszDirichlet._natural_paramsv   s    ""$$r   c                     |j                         j                  d      t        j                   |j                  d            z
  S )Nr
   )rE   r   r   )r1   r   s     r   _log_normalizerzDirichlet._log_normalizerz   s-    xxz~~b!ELLr$;;;r   r   )r&   )r"   r#   r$   __doc__r   independentpositivearg_constraintssimplexsupporthas_rsampler0   r8   r@   rG   propertyrJ   rV   r\   rb   rd   rf   __classcell__)r4   s   @r   r   r      s     	0001E1EqIO !!GKP/

 E E   
 

 % %<r   )r   torch.autogradr   torch.autograd.functionr   torch.distributionsr   torch.distributions.exp_familyr   __all__r   r   r   r&   r   r   <module>ru      s>     # 7 + <-BB B\<! \<r   