o
    ,h	                     @   sr   d dl mZ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 dlmZmZ dgZG d	d deZdS )
    )OptionalUnionN)Tensor)constraints)ExponentialFamily)broadcast_all)_NumberNumberPoissonc                       s   e Zd ZdZdejiZejZe	de
fddZe	de
fddZe	de
fdd	Z	
ddee
ef dee dd
f fddZd fdd	Ze fddZdd Ze	dee
 fddZdd Z  ZS )r
   a  
    Creates a Poisson distribution parameterized by :attr:`rate`, the rate parameter.

    Samples are nonnegative integers, with a pmf given by

    .. math::
      \mathrm{rate}^k \frac{e^{-\mathrm{rate}}}{k!}

    Example::

        >>> # xdoctest: +SKIP("poisson_cpu not implemented for 'Long'")
        >>> m = Poisson(torch.tensor([4]))
        >>> m.sample()
        tensor([ 3.])

    Args:
        rate (Number, Tensor): the rate parameter
    ratereturnc                 C      | j S Nr   self r   V/var/www/html/scripts/venv/lib/python3.10/site-packages/torch/distributions/poisson.pymean&      zPoisson.meanc                 C   s
   | j  S r   )r   floorr   r   r   r   mode*   s   
zPoisson.modec                 C   r   r   r   r   r   r   r   variance.   r   zPoisson.varianceNvalidate_argsc                    s>   t |\| _t|trt }n| j }t j||d d S )Nr   )	r   r   
isinstancer   torchSizesizesuper__init__)r   r   r   batch_shape	__class__r   r   r    2   s
   


zPoisson.__init__c                    sD   |  t|}t|}| j||_tt|j|dd | j|_|S )NFr   )	_get_checked_instancer
   r   r   r   expandr   r    _validate_args)r   r!   	_instancenewr"   r   r   r%   >   s   
zPoisson.expandc                 C   sH   |  |}t  t| j|W  d    S 1 sw   Y  d S r   )_extended_shaper   no_gradpoissonr   r%   )r   sample_shapeshaper   r   r   sampleF   s   

$zPoisson.samplec                 C   s:   | j r| | t| j|\}}||| |d   S )N   )r&   _validate_sampler   r   xlogylgamma)r   valuer   r   r   r   log_probK   s   
zPoisson.log_probc                 C   s   t | jfS r   )r   logr   r   r   r   r   _natural_paramsQ   s   zPoisson._natural_paramsc                 C   s
   t |S r   )r   exp)r   xr   r   r   _log_normalizerU   s   
zPoisson._log_normalizerr   )__name__
__module____qualname____doc__r   nonnegativearg_constraintsnonnegative_integersupportpropertyr   r   r   r   r   r	   r   boolr    r%   r   r   r.   r4   tupler6   r9   __classcell__r   r   r"   r   r
      s0    

)typingr   r   r   r   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   torch.typesr   r	   __all__r
   r   r   r   r   <module>   s   