
    sg                     X    d dl mZ d dlZd dlmZ d dlmZ d dlmZ dgZ	 G d de      Z
y)    )NumberN)constraints)ExponentialFamily)broadcast_allPoissonc                        e Zd ZdZdej
                  iZej                  Ze	d        Z
e	d        Ze	d        Zd fd	Zd fd	Z ej                          fdZd	 Ze	d
        Zd Z x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
    ratec                     | j                   S Nr	   selfs    N/var/www/html/venv/lib/python3.12/site-packages/torch/distributions/poisson.pymeanzPoisson.mean#       yy    c                 6    | j                   j                         S r   )r	   floorr   s    r   modezPoisson.mode'   s    yy  r   c                     | j                   S r   r   r   s    r   variancezPoisson.variance+   r   r   c                     t        |      \  | _        t        |t              rt	        j
                         }n| j                  j                         }t        | !  ||       y )Nvalidate_args)	r   r	   
isinstancer   torchSizesizesuper__init__)r   r	   r   batch_shape	__class__s       r   r    zPoisson.__init__/   sH    $T*dF#**,K))..*KMBr   c                     | j                  t        |      }t        j                  |      }| j                  j                  |      |_        t        t        |  |d       | j                  |_        |S )NFr   )	_get_checked_instancer   r   r   r	   expandr   r    _validate_args)r   r!   	_instancenewr"   s       r   r%   zPoisson.expand7   s`    (()<jj-99##K0gs$[$F!00
r   c                     | j                  |      }t        j                         5  t        j                  | j                  j                  |            cd d d        S # 1 sw Y   y xY wr   )_extended_shaper   no_gradpoissonr	   r%   )r   sample_shapeshapes      r   samplezPoisson.sample?   sK    $$\2]]_ 	:==!1!1%!89	: 	: 	:s   .AA'c                     | j                   r| j                  |       t        | j                  |      \  }}|j	                  |      |z
  |dz   j                         z
  S )N   )r&   _validate_sampler   r	   xlogylgamma)r   valuer	   s      r   log_probzPoisson.log_probD   sS    !!%(#DIIu5e{{4 4'519*<*<*>>>r   c                 B    t        j                  | j                        fS r   )r   logr	   r   s    r   _natural_paramszPoisson._natural_paramsJ   s    		$))$&&r   c                 ,    t        j                  |      S r   )r   exp)r   xs     r   _log_normalizerzPoisson._log_normalizerN   s    yy|r   r   )__name__
__module____qualname____doc__r   nonnegativearg_constraintsnonnegative_integersupportpropertyr   r   r   r    r%   r   r   r/   r6   r9   r=   __classcell__)r"   s   @r   r   r      s    $ {667O--G  ! !  C #-%**, :
? ' 'r   )numbersr   r   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   __all__r    r   r   <module>rN      s,      + < 3 +B Br   