U
    9%eJ                     @   sj   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d Z
G d	d
 d
eZG dd deZdS )    N)Function)once_differentiable)constraints)ExponentialFamily	Dirichletc                 C   s8   | dd|}t| ||}||| |  dd  S NT)sumZ	expand_astorchZ_dirichlet_grad)xconcentrationgrad_outputtotalZgrad r   \/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/distributions/dirichlet.py_Dirichlet_backward   s    r   c                   @   s(   e Zd Zedd Zeedd ZdS )
_Dirichletc                 C   s   t |}| || |S N)r
   Z_sample_dirichletZsave_for_backward)ctxr   r   r   r   r   forward   s    
z_Dirichlet.forwardc                 C   s   | j \}}t|||S r   )Zsaved_tensorsr   )r   r   r   r   r   r   r   backward   s    
z_Dirichlet.backwardN)__name__
__module____qualname__staticmethodr   r   r   r   r   r   r   r      s
   
r   c                       s   e Zd ZdZdeejdiZejZ	dZ
d fdd	Zd fdd		ZdddZdd Zedd Zedd Zedd Zdd Zedd Zdd Z  ZS )r   a  
    Creates a Dirichlet distribution parameterized by concentration :attr:`concentration`.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
        >>> 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      TNc                    sN   |  dk rtd|| _|jd d |jdd   }}t j|||d d S )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__r   r   r"   4   s    zDirichlet.__init__c                    sN   |  t|}t|}| j|| j |_tt|j|| jdd | j	|_	|S )NFr   )
Z_get_checked_instancer   r
   Sizer   expandr%   r!   r"   _validate_args)r#   r$   Z	_instancenewr&   r   r   r)   =   s    

  zDirichlet.expandr   c                 C   s    |  |}| j|}t|S r   )Z_extended_shaper   r)   r   apply)r#   Zsample_shaper    r   r   r   r   rsampleG   s    
zDirichlet.rsamplec                 C   sL   | j r| | t| jd |dt| jd t| jd S )N      ?r   )r*   Z_validate_sampler
   Zxlogyr   r	   lgamma)r#   valuer   r   r   log_probL   s    
zDirichlet.log_probc                 C   s   | j | j dd S r   )r   r	   r#   r   r   r   meanU   s    zDirichlet.meanc                 C   sd   | j d jdd}||dd }| j dk jdd}tjj|| jdd|j	d 
|||< |S )Nr   g        )minr   T)Zaxis)r   clampr	   allr
   nnZ
functionalZone_hotZargmaxr    to)r#   Zconcentrationm1modemaskr   r   r   r9   Y   s     zDirichlet.modec                 C   s0   | j dd}| j || j   |d|d   S )Nr   T   r   )r   r	   pow)r#   Zcon0r   r   r   variancec   s    zDirichlet.variancec                 C   sb   | j d}| j d}t| j dt| || t|  | j d t| j  d S )Nr   r.   )r   sizer	   r
   r/   Zdigamma)r#   kZa0r   r   r   entropyl   s    zDirichlet.entropyc                 C   s   | j fS r   )r   r2   r   r   r   _natural_paramsv   s    zDirichlet._natural_paramsc                 C   s   |  dt |d S )Nr   )r/   r	   r
   )r#   r   r   r   r   _log_normalizerz   s    zDirichlet._log_normalizer)N)N)r   )r   r   r   __doc__r   ZindependentZpositiveZarg_constraintsZsimplexZsupportZhas_rsampler"   r)   r-   r1   propertyr3   r9   r=   r@   rA   rB   __classcell__r   r   r&   r   r      s(    	

	

	


)r
   Ztorch.autogradr   Ztorch.autograd.functionr   Ztorch.distributionsr   Ztorch.distributions.exp_familyr   __all__r   r   r   r   r   r   r   <module>   s   