
    rh!                     |    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e	      Zy)
    )OptionalUnionN)nanTensor)constraints)Distribution)broadcast_all)_Number_sizeUniformc            	       H    e Zd ZdZdZed        Zedefd       Zedefd       Z	edefd       Z
edefd       Z	 dd
eeef   deeef   dee   dd	f fdZd fd	Z ej&                  dd      d        Z ej,                         fdedefdZd Zd Zd Zd Z x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                     t        j                  | j                        t        j                  | j                        dS )N)lowhigh)r   	less_thanr   greater_thanr   selfs    n/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/torch/distributions/uniform.pyarg_constraintszUniform.arg_constraints"   s2     ((3,,TXX6
 	
    returnc                 :    | j                   | j                  z   dz  S )N   r   r   r   s    r   meanzUniform.mean*   s    		DHH$))r   c                 (    t         | j                  z  S N)r   r   r   s    r   modezUniform.mode.   s    TYYr   c                 :    | j                   | j                  z
  dz  S )NgLXz@r   r   s    r   stddevzUniform.stddev2   s    		DHH$//r   c                 X    | j                   | j                  z
  j                  d      dz  S )Nr      )r   r   powr   s    r   variancezUniform.variance6   s%    		DHH$))!,r11r   Nr   r   validate_argsc                     t        ||      \  | _        | _        t        |t              r%t        |t              rt        j                         }n| j                  j                         }t        | %  ||       y )Nr&   )
r	   r   r   
isinstancer
   torchSizesizesuper__init__)r   r   r   r&   batch_shape	__class__s        r   r.   zUniform.__init__:   sY     ,C6$)c7#
4(A**,K((--/KMBr   c                 *   | j                  t        |      }t        j                  |      }| j                  j                  |      |_        | j                  j                  |      |_        t        t        |#  |d       | j                  |_	        |S )NFr(   )
_get_checked_instancer   r*   r+   r   expandr   r-   r.   _validate_args)r   r/   	_instancenewr0   s       r   r3   zUniform.expandH   st    (()<jj-((//+.99##K0gs$[$F!00
r   Fr   )is_discrete	event_dimc                 V    t        j                  | j                  | j                        S r   )r   intervalr   r   r   s    r   supportzUniform.supportQ   s    ##DHHdii88r   sample_shapec                     | j                  |      }t        j                  || j                  j                  | j                  j
                        }| j                  || j                  | j                  z
  z  z   S )N)dtypedevice)_extended_shaper*   randr   r>   r?   r   )r   r<   shaperA   s       r   rsamplezUniform.rsampleU   sU    $$\2zz%txx~~dhhooNxx$$))dhh"6777r   c                    | j                   r| j                  |       | j                  j                  |      j	                  | j                        }| j
                  j                  |      j	                  | j                        }t        j                  |j                  |            t        j                  | j
                  | j                  z
        z
  S r   )
r4   _validate_sampler   letype_asr   gtr*   logmul)r   valuelbubs       r   log_probzUniform.log_probZ   s    !!%(XX[[''1YY\\% ((2yy$uyyTXX1E'FFFr   c                     | j                   r| j                  |       || j                  z
  | j                  | j                  z
  z  }|j	                  dd      S )Nr      )minmax)r4   rE   r   r   clampr   rK   results      r   cdfzUniform.cdfa   sL    !!%($(("tyy488';<||q|))r   c                 X    || j                   | j                  z
  z  | j                  z   }|S r   r   rT   s      r   icdfzUniform.icdfg   s'    $))dhh./$((:r   c                 Z    t        j                  | j                  | j                  z
        S r   )r*   rI   r   r   r   s    r   entropyzUniform.entropyk   s    yyTXX-..r   r   )__name__
__module____qualname____doc__has_rsamplepropertyr   r   r   r   r!   r%   r   floatr   boolr.   r3   r   dependent_propertyr;   r*   r+   r   rC   rN   rV   rX   rZ   __classcell__)r0   s   @r   r   r      s6     K
 
 *f * * f   0 0 0 2& 2 2 )-	C65=!C FEM"C  ~	C
 
C $[##C9 D9 -7EJJL 8E 8V 8
G*/r   )typingr   r   r*   r   r   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr	   torch.typesr
   r   __all__r    r   r   <module>rl      s1    "   + 9 3 & +]/l ]/r   