






import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)






class 			_UpperCamelCase(       __lowerCamelCase		):

		def __init__(					self		:    Dict    ,       *SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ,       **SCREAMING_SNAKE_CASE__		:    List[Any]    ):



					'''simple docstring'''



					warnings.warn(
					    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use DeiTImageProcessor instead.'    ,       SCREAMING_SNAKE_CASE__    ,       )
					super().__init__(*SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )









'''simple docstring'''






from __future__ import annotations

from random import choice


def    _snake_case  (	A			)		->       int:
							return choice(A			)


def    _snake_case  (	A					,       A			)		->       int:
							lowerCAmelCase__			  =      random_pivot(A			)

							# partition based on pivot
							# linear time
							lowerCAmelCase__			  =      [e for e in lst if e < pivot]
							lowerCAmelCase__			  =      [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(A			) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(A			) < k - 1:
														return kth_number(A					,       k - len(A			) - 1			)
							# pivot is in elements smaller than k
							else:
														return kth_number(A					,       A			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()