"""simple docstring"""






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					["image_processor", "tokenizer"]
					snake_case__							       =					"AutoImageProcessor"
					snake_case__							       =					"AutoTokenizer"







					def __init__(							self  :      List[Any]       ,lowerCamelCase__  :      Tuple=None       ,lowerCamelCase__  :      Optional[int]=None       ,**lowerCamelCase__  :      List[Any]     ):
									UpperCAmelCase__			 =				None
									if "feature_extractor" in kwargs:
													warnings.warn(
													    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
													    ' instead.'       ,lowerCamelCase__       ,)
													UpperCAmelCase__			 =				kwargs.pop('feature_extractor'     )

									UpperCAmelCase__			 =				image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
													raise ValueError('You need to specify an `image_processor`.'     )
									if tokenizer is None:
													raise ValueError('You need to specify a `tokenizer`.'     )

									super().__init__(lowerCamelCase__       ,lowerCamelCase__     )
									UpperCAmelCase__			 =				self.image_processor
									UpperCAmelCase__			 =				False







					def __call__(							self  :      Union[str, Any]       ,*lowerCamelCase__  :      Any       ,**lowerCamelCase__  :      Any     ):
									# For backward compatibility
									if self._in_target_context_manager:
													return self.current_processor(*lowerCamelCase__       ,**lowerCamelCase__     )

									UpperCAmelCase__			 =				kwargs.pop('images'       ,lowerCamelCase__     )
									UpperCAmelCase__			 =				kwargs.pop('text'       ,lowerCamelCase__     )
									if len(lowerCamelCase__     ) > 0:
													UpperCAmelCase__			 =				args[0]
													UpperCAmelCase__			 =				args[1:]

									if images is None and text is None:
													raise ValueError('You need to specify either an `images` or `text` input to process.'     )

									if images is not None:
													UpperCAmelCase__			 =				self.image_processor(lowerCamelCase__       ,*lowerCamelCase__       ,**lowerCamelCase__     )
									if text is not None:
													UpperCAmelCase__			 =				self.tokenizer(lowerCamelCase__       ,**lowerCamelCase__     )

									if text is None:
													return inputs
									elif images is None:
													return encodings
									else:
													UpperCAmelCase__			 =				encodings['input_ids']
													return inputs







					def 						__lowerCAmelCase		(							self  :      int       ,*lowerCamelCase__  :      Optional[int]       ,**lowerCamelCase__  :      Union[str, Any]     ):
									return self.tokenizer.batch_decode(*lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Tuple       ,*lowerCamelCase__  :      Optional[int]       ,**lowerCamelCase__  :      List[Any]     ):
									return self.tokenizer.decode(*lowerCamelCase__       ,**lowerCamelCase__     )







					@contextmanager
					def 						__lowerCAmelCase		(							self  :      Union[str, Any]     ):
									warnings.warn(
									    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
									    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
									    'your images inputs, or in a separate call.'     )
									UpperCAmelCase__			 =				True
									UpperCAmelCase__			 =				self.tokenizer
									yield
									UpperCAmelCase__			 =				self.image_processor
									UpperCAmelCase__			 =				False







					@property
					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,lowerCamelCase__       ,)
									return self.image_processor_class







					@property
					def 						__lowerCAmelCase		(							self  :      Tuple     ):
									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,lowerCamelCase__       ,)
									return self.image_processor




"""simple docstring"""






from __future__ import annotations



def     a_		(   lowerCamelCase			):
				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__":
						lowerCAmelCase__    :    Any			=      int(input('Enter a prime number q: '))
						lowerCAmelCase__    :    List[str]			=      find_primitive(q)
						if a is None:
												print(F"""Cannot find the primitive for the value: {a!r}""")
						else:
												lowerCAmelCase__    :    str			=      int(input('Enter private key of A: '))
												lowerCAmelCase__    :    List[Any]			=      pow(a, a_private, q)
												lowerCAmelCase__    :    List[str]			=      int(input('Enter private key of B: '))
												lowerCAmelCase__    :    Any			=      pow(a, b_private, q)

												lowerCAmelCase__    :    Union[str, Any]			=      pow(b_public, a_private, q)
												lowerCAmelCase__    :    Tuple			=      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)



