


"""simple docstring"""






from __future__ import annotations

import math
import random



def 			A__		(					_UpperCAmelCase    :      int				,    _UpperCAmelCase    :      int				,    _UpperCAmelCase    :      int	)							->	list[int]:

	'''simple docstring'''


	snake_case__  :						List[Any]		=      d * e - 1
	snake_case__  :						Dict		=      0
	snake_case__  :						int		=      0
	while p == 0:
		snake_case__  :						Union[str, Any]		=      random.randint(2				,    n - 1	)
		snake_case__  :						int		=      k
		while True:
			if t % 2 == 0:
				snake_case__  :						Optional[int]		=      t // 2
				snake_case__  :						str		=      (g**t) % n
				snake_case__  :						Any		=      math.gcd(x - 1				,    _UpperCAmelCase	)
				if x > 1 and y > 1:
					snake_case__  :						List[str]		=      y
					snake_case__  :						Tuple		=      n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()












import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_UpperCAmelCase										=					logging.get_logger(__name__)



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def __init__(		self:			Tuple       , *_SCREAMING_SNAKE_CASE:			List[Any]       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			None:




              """simple docstring"""


              warnings.warn(
                  "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
                  " Please use ConvNextImageProcessor instead."       , _SCREAMING_SNAKE_CASE       , )
              super().__init__(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

