


'''simple docstring'''






from __future__ import annotations

from random import choice






def 		_A			(lowerCAmelCase__					:Optional[int]						)				->					str:




     '''simple docstring'''



     return choice(__lowerCAmelCase						)






def 		_A			(lowerCAmelCase__					:list[int]			,  lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     _a      =							random_pivot(__lowerCAmelCase						)

     # partition based on pivot
     # linear time
     _a      =							[e for e in lst if e < pivot]
     _a      =							[e for e in lst if e > pivot]

     # if we get lucky, pivot might be the element we want.
     # we can easily see this:
     # small (elements smaller than k)
     # + pivot (kth element)
     # + big (elements larger than k)
     if len(__lowerCAmelCase						) == k - 1:
          return pivot
     # pivot is in elements bigger than k
     elif len(__lowerCAmelCase						) < k - 1:
          return kth_number(__lowerCAmelCase			,  k - len(__lowerCAmelCase						) - 1						)
     # pivot is in elements smaller than k
     else:
          return kth_number(__lowerCAmelCase			,  __lowerCAmelCase						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


a_   :      Optional[Any]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							None:
      warnings.warn(
          'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use CLIPImageProcessor instead.' ,			__magic_name__ ,			)
      super().__init__(*__magic_name__ ,			**__magic_name__  )

