
    Ǆg                         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d Z G d	 d
e      Z G d de      Zy)    N)Function)once_differentiable)constraints)ExponentialFamily)_size	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        e/home/mcse/projects/flask_80/flask-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edej                  f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   r1   zDirichlet.__init__7   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   expandr4   r0   r1   _validate_args)r2   r3   	_instancenewr5   s       r   r9   zDirichlet.expand@   s}    ((I>jj- ..55kDDTDT6TUi&)) 	' 	
 "00
r   sample_shapereturnc                     | j                  |      }| j                  j                  |      }t        j	                  |      S r   )_extended_shaper   r9   r   apply)r2   r=   r/   r   s       r   rsamplezDirichlet.rsampleJ   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   )r:   _validate_sampler   xlogyr   r   lgamma)r2   values     r   log_probzDirichlet.log_probO   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   r2   s    r   meanzDirichlet.meanX   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)r2   concentrationm1modemasks       r   rX   zDirichlet.mode\   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)r2   con0s     r   variancezDirichlet.variancef   sT    !!%%b$/d(((*xx{dQh')	
r   c                    | j                   j                  d      }| j                   j                  d      }t        j                  | j                         j                  d      t        j                  |      z
  ||z
  t        j
                  |      z  z
  | j                   dz
  t        j
                  | j                         z  j                  d      z
  S )Nr   rD   )r   sizer   r   rG   digamma)r2   ka0s      r   entropyzDirichlet.entropyo   s    ##B'##B'LL++,004ll22vr**+ ""S(EMM$:L:L,MMRRSUVW	
r   c                     | j                   fS r   )r   rK   s    r   _natural_paramszDirichlet._natural_paramsy   s    ""$$r   c                     |j                         j                  d      t        j                   |j                  d            z
  S )Nr   )rG   r   r   )r2   r   s     r   _log_normalizerzDirichlet._log_normalizer}   s-    xxz~~b!ELLr$;;;r   r   )r'   )r#   r$   r%   __doc__r   independentpositivearg_constraintssimplexsupporthas_rsampler1   r9   r   r   TensorrB   rI   propertyrL   rX   r^   rd   rf   rh   __classcell__)r5   s   @r   r   r   "   s     	0001E1EqIO !!GKP/E /5<< /

 E E   
 

 % %<r   )r   torch.autogradr   torch.autograd.functionr   torch.distributionsr   torch.distributions.exp_familyr   torch.typesr   __all__r   r   r   r'   r   r   <module>ry      sC     # 7 + <  -BB B\<! \<r   