o
    h!                     @   sv   d dl mZmZ d dlZd dl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 )
    )OptionalUnionN)nanTensor)constraints)Distribution)broadcast_all)_Number_sizeUniformc                	       s   e Zd ZdZdZe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defd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jddd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   a  
    Generates uniformly distributed random samples from the half-open interval
    ``[low, high)``.

    Example::

        >>> m = Uniform(torch.tensor([0.0]), torch.tensor([5.0]))
        >>> m.sample()  # uniformly distributed in the range [0.0, 5.0)
        >>> # xdoctest: +SKIP
        tensor([ 2.3418])

    Args:
        low (float or Tensor): lower range (inclusive).
        high (float or Tensor): upper range (exclusive).
    Tc                 C   s   t | jt | jdS )N)lowhigh)r   	less_thanr   greater_thanr   self r   a/home/www/facesmatcher.com/frenv_anti/lib/python3.10/site-packages/torch/distributions/uniform.pyarg_constraints"   s   

zUniform.arg_constraintsreturnc                 C   s   | j | j d S )N   r   r   r   r   r   r   mean*      zUniform.meanc                 C   s
   t | j S N)r   r   r   r   r   r   mode.   s   
zUniform.modec                 C   s   | j | j d S )NgLXz@r   r   r   r   r   stddev2   r   zUniform.stddevc                 C   s   | j | j dd S )Nr      )r   r   powr   r   r   r   variance6   s   zUniform.varianceNr   r   validate_argsc                    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__)r   r   r   r    batch_shape	__class__r   r   r'   :   s
   

zUniform.__init__c                    sR   |  t|}t|}| j||_| j||_tt|j|dd | j	|_	|S )NFr!   )
Z_get_checked_instancer   r#   r$   r   expandr   r&   r'   _validate_args)r   r(   Z	_instancenewr)   r   r   r+   H   s   
zUniform.expandFr   )Zis_discreteZ	event_dimc                 C   s   t | j| jS r   )r   intervalr   r   r   r   r   r   supportQ   r   zUniform.supportsample_shapec                 C   s8   |  |}tj|| jj| jjd}| j|| j| j   S )N)dtypedevice)Z_extended_shaper#   randr   r1   r2   r   )r   r0   shaper3   r   r   r   rsampleU   s   
zUniform.rsamplec                 C   sZ   | j r| | | j|| j}| j|| j}t|	|t| j| j  S r   )
r,   _validate_sampler   leZtype_asr   gtr#   logmul)r   valueZlbZubr   r   r   log_probZ   s
   
"zUniform.log_probc                 C   s4   | j r| | || j | j| j  }|jdddS )Nr      )minmax)r,   r6   r   r   clampr   r;   resultr   r   r   cdfa   s   
zUniform.cdfc                 C   s   || j | j  | j }|S r   r   rA   r   r   r   icdfg   s   zUniform.icdfc                 C   s   t | j| j S r   )r#   r9   r   r   r   r   r   r   entropyk   s   zUniform.entropyr   )__name__
__module____qualname____doc__Zhas_rsamplepropertyr   r   r   r   r   r   r   floatr   boolr'   r+   r   Zdependent_propertyr/   r#   r$   r
   r5   r<   rC   rD   rE   __classcell__r   r   r)   r   r      s>    


	
)typingr   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   