o
    hd                     @   s   d dl Z d dlmZmZ d dlZd dlmZmZ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 )
    N)OptionalUnion)infnanTensor)constraints)Distribution)broadcast_all)_Number_sizeCauchyc                	       s   e Zd ZdZejejdZejZdZ		dde
eef de
eef dee ddf fd	d
Zd fdd	ZedefddZedefddZedefddZe fdedefddZdd Zdd Zdd Zdd Z  ZS )r   aC  
    Samples from a Cauchy (Lorentz) distribution. The distribution of the ratio of
    independent normally distributed random variables with means `0` follows a
    Cauchy distribution.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Cauchy(torch.tensor([0.0]), torch.tensor([1.0]))
        >>> m.sample()  # sample from a Cauchy distribution with loc=0 and scale=1
        tensor([ 2.3214])

    Args:
        loc (float or Tensor): mode or median of the distribution.
        scale (float or Tensor): half width at half maximum.
    )locscaleTNr   r   validate_argsreturnc                    sN   t ||\| _| _t|trt|trt }n| j }t j	||d d S )Nr   )
r	   r   r   
isinstancer
   torchSizesizesuper__init__)selfr   r   r   batch_shape	__class__ `/home/www/facesmatcher.com/frenv_anti/lib/python3.10/site-packages/torch/distributions/cauchy.pyr   &   s
   

zCauchy.__init__c                    sR   |  t|}t|}| j||_| j||_tt|j|dd | j	|_	|S )NFr   )
Z_get_checked_instancer   r   r   r   expandr   r   r   _validate_args)r   r   Z	_instancenewr   r   r   r   3   s   
zCauchy.expandc                 C      t j|  t| jj| jjdS N)dtypedevice)r   full_extended_shaper   r   r#   r$   r   r   r   r   mean<      zCauchy.meanc                 C   s   | j S N)r   r'   r   r   r   modeB   s   zCauchy.modec                 C   r!   r"   )r   r%   r&   r   r   r#   r$   r'   r   r   r   varianceF   r)   zCauchy.variancesample_shapec                 C   s*   |  |}| j| }| j|| j  S r*   )r&   r   r    Zcauchy_r   )r   r-   shapeepsr   r   r   rsampleL   s   
zCauchy.rsamplec                 C   s@   | j r| | ttj | j  || j | j d   S )N   )r   _validate_samplemathlogpir   r   log1pr   valuer   r   r   log_probQ   s   
zCauchy.log_probc                 C   s0   | j r| | t|| j | j tj d S Ng      ?)r   r2   r   atanr   r   r3   r5   r7   r   r   r   cdfZ   s   
 z
Cauchy.cdfc                 C   s    t tj|d  | j | j S r:   )r   tanr3   r5   r   r   r7   r   r   r   icdf_   s    zCauchy.icdfc                 C   s   t dt j | j  S )N   )r3   r4   r5   r   r'   r   r   r   entropyb   s   zCauchy.entropyr*   )__name__
__module____qualname____doc__r   realZpositiveZarg_constraintsZsupportZhas_rsampler   r   floatr   boolr   r   propertyr(   r+   r,   r   r   r   r0   r9   r<   r>   r@   __classcell__r   r   r   r   r      s6    

		)r3   typingr   r   r   r   r   r   Ztorch.distributionsr   Z torch.distributions.distributionr   Ztorch.distributions.utilsr	   Ztorch.typesr
   r   __all__r   r   r   r   r   <module>   s   