



from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
	from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      List[Any]	     =							c_float




	@property
	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				str:
							return sizeof(self.value  )



def 		__a  (					lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      int = 0       )   ->				Dict:






						'''simple docstring'''



						if dimension <= 0:
												UpperCAmelCase_=     fixed_dimension

						dimension -= num_token_to_add
						return dimension



def 		__a  (					lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      ParameterFormat       )   ->				Union[str, Any]:






						'''simple docstring'''



						return num_parameters * dtype.size



def 		__a  (					lowerCAmelCase_     :      str       )   ->				Tuple:






						'''simple docstring'''



						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

						try:
												return AutoProcessor.from_pretrained(lowercase__       )
						except (ValueError, OSError, KeyError):
												UpperCAmelCase_,     UpperCAmelCase_=     None, None
												try:
																		UpperCAmelCase_=     AutoTokenizer.from_pretrained(lowercase__       )
												except (OSError, KeyError):
																		pass
												try:
																		UpperCAmelCase_=     AutoFeatureExtractor.from_pretrained(lowercase__       )
												except (OSError, KeyError):
																		pass

												if tokenizer is not None and feature_extractor is not None:
																		raise ValueError(
																		    F"""Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""       )
												elif tokenizer is None and feature_extractor is None:
																		return None
												elif tokenizer is not None:
																		return tokenizer
												else:
																		return feature_extractor









from __future__ import annotations



def  __a  (					lowerCAmelCase_     :      int       )   ->				int | None:






						'''simple docstring'''



						for r in range(1    ,lowerCAmelCase_       ):
												UpperCAmelCase_=     []
												for x in range(n - 1       ):
																		UpperCAmelCase_=     pow(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_       )
																		if val in li:
																								break
																		li.append(lowerCAmelCase_       )
												else:
																		return r
						return None


if __name__ == "__main__":
	__A       					=		int(input('''Enter a prime number q: '''))
	__A       					=		find_primitive(q)
	if a is None:
		print(f'Cannot find the primitive for the value: {a!r}')
	else:
		__A       					=		int(input('''Enter private key of A: '''))
		__A       					=		pow(a, a_private, q)
		__A       					=		int(input('''Enter private key of B: '''))
		__A       					=		pow(a, b_private, q)

		__A       					=		pow(b_public, a_private, q)
		__A       					=		pow(a_public, b_private, q)

		print('''The key value generated by A is: ''', a_secret)
		print('''The key value generated by B is: ''', b_secret)




