






from __future__ import annotations

import math
import random







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    List[str]						)     ->      list[int]:

						"""simple docstring"""



						lowerCAmelCase__			  =  d * e - 1
						lowerCAmelCase__			  =  0
						lowerCAmelCase__			  =  0
						while p == 0:
												lowerCAmelCase__			  =  random.randint(2			,				n - 1						)
												lowerCAmelCase__			  =  k
												while True:
																		if t % 2 == 0:
																								lowerCAmelCase__			  =  t // 2
																								lowerCAmelCase__			  =  (g**t) % n
																								lowerCAmelCase__			  =  math.gcd(x - 1			,				__lowercase						)
																								if x > 1 and y > 1:
																														lowerCAmelCase__			  =  y
																														lowerCAmelCase__			  =  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()









"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


a_											=			logging.get_logger(__name__)
class snake_case					(     _UpperCamelCase):







		def __init__(    self					:				str			,      *a__					:				Union[str, Any]			,      **a__					:				Optional[Any]						)				->  None:





						'''simple docstring'''







						warnings.warn(
						    "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use MobileNetV2ImageProcessor instead."			,      a__			,      )
						super().__init__(*a__			,      **a__						)