
    Ǆg                     p    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 dlm	Z	 d dl
mZ dgZ G d	 de      Zy)
    )NumberN)nan)constraints)Distribution)broadcast_all)_sizeUniformc                   X    e Zd ZdZ ej
                  dd       ej
                  dd      dZdZed        Z	ed        Z
ed	        Zed
        Zd fd	Zd fd	Z ej                  dd      d        Z ej$                         fdedej(                  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).
    Fr   )is_discrete	event_dim)lowhighTc                 :    | j                   | j                  z   dz  S )N   r   r   selfs    c/home/mcse/projects/flask_80/flask-venv/lib/python3.12/site-packages/torch/distributions/uniform.pymeanzUniform.mean&   s    		DHH$))    c                 (    t         | j                  z  S N)r   r   r   s    r   modezUniform.mode*   s    TYYr   c                 :    | j                   | j                  z
  dz  S )NgLXz@r   r   s    r   stddevzUniform.stddev.   s    		DHH$//r   c                 X    | j                   | j                  z
  j                  d      dz  S )Nr      )r   r   powr   s    r   variancezUniform.variance2   s%    		DHH$))!,r11r   c                    t        ||      \  | _        | _        t        |t              r%t        |t              rt        j                         }n| j                  j                         }t        | %  ||       | j                  rDt        j                  | j                  | j                        j                         st        d      y y )Nvalidate_argsz&Uniform is not defined when low>= high)r   r   r   
isinstancer   torchSizesizesuper__init___validate_argsltall
ValueError)r   r   r   r"   batch_shape	__class__s        r   r(   zUniform.__init__6   s    +C6$)c6"z$'?**,K((--/KMBuxx$))'D'H'H'JEFF (Kr   c                 *   | j                  t        |      }t        j                  |      }| j                  j                  |      |_        | j                  j                  |      |_        t        t        |#  |d       | j                  |_	        |S )NFr!   )
_get_checked_instancer	   r$   r%   r   expandr   r'   r(   r)   )r   r-   	_instancenewr.   s       r   r1   zUniform.expandB   st    (()<jj-((//+.99##K0gs$[$F!00
r   c                 V    t        j                  | j                  | j                        S r   )r   intervalr   r   r   s    r   supportzUniform.supportK   s    ##DHHdii88r   sample_shapereturnc                     | j                  |      }t        j                  || j                  j                  | j                  j
                        }| j                  || j                  | j                  z
  z  z   S )N)dtypedevice)_extended_shaper$   randr   r:   r;   r   )r   r7   shaper=   s       r   rsamplezUniform.rsampleO   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   )
r)   _validate_sampler   letype_asr   gtr$   logmul)r   valuelbubs       r   log_probzUniform.log_probT   s    !!%(XX[[''1YY\\% ((2yy$uyyTXX1E'FFFr   c                     | j                   r| j                  |       || j                  z
  | j                  | j                  z
  z  }|j	                  dd      S )Nr      )minmax)r)   rA   r   r   clampr   rG   results      r   cdfzUniform.cdf[   sL    !!%($(("tyy488';<||q|))r   c                 X    || j                   | j                  z
  z  | j                  z   }|S r   r   rP   s      r   icdfzUniform.icdfa   s'    $))dhh./$((:r   c                 Z    t        j                  | j                  | j                  z
        S r   )r$   rE   r   r   r   s    r   entropyzUniform.entropye   s    yyTXX-..r   r   )__name__
__module____qualname____doc__r   	dependentarg_constraintshas_rsamplepropertyr   r   r   r   r(   r1   dependent_propertyr6   r$   r%   r   Tensorr?   rJ   rR   rT   rV   __classcell__)r.   s   @r   r	   r	      s    " %{$$!D%%%%1EO K* *   0 0 2 2
G $[##C9 D9 -7EJJL 8E 8U\\ 8
G*/r   )numbersr   r$   r   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr   torch.typesr   __all__r	    r   r   <module>ri      s1       + 9 3  +W/l W/r   