code: 
  
  
  
  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
  
  
  
  
  
style_context: 
  
  
  
  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)
  
  
  
  
  