






import math







def   snake_case		(				UpperCAmelCase						:							Any ):
				if n <= 0:
								raise ValueError('Only positive integers have prime factors' )
				A									=   []
				while n % 2 == 0:
								pf.append(2 )
								A									=   int(n / 2 )
				for i in range(3,    int(math.sqrt(lowerCAmelCase__ ) ) + 1,    2 ):
								while n % i == 0:
												pf.append(lowerCAmelCase__ )
												A									=   int(n / i )
				if n > 2:
								pf.append(lowerCAmelCase__ )
				return pf







def   snake_case		(				UpperCAmelCase						:							str ):
				if n <= 0:
								raise ValueError('Only positive numbers are accepted' )
				A									=   1
				A									=   1
				while n % 2 == 0:
								temp += 1
								A									=   int(n / 2 )
				div *= temp
				for i in range(3,    int(math.sqrt(lowerCAmelCase__ ) ) + 1,    2 ):
								A									=   1
								while n % i == 0:
												temp += 1
												A									=   int(n / i )
								div *= temp
				if n > 1:
								div *= 2
				return div







def   snake_case		(				UpperCAmelCase						:							Tuple ):
				if n <= 0:
								raise ValueError('Only positive numbers are accepted' )
				A									=   1
				A									=   1
				while n % 2 == 0:
								temp += 1
								A									=   int(n / 2 )
				if temp > 1:
								s *= (2**temp - 1) / (2 - 1)
				for i in range(3,    int(math.sqrt(lowerCAmelCase__ ) ) + 1,    2 ):
								A									=   1
								while n % i == 0:
												temp += 1
												A									=   int(n / i )
								if temp > 1:
												s *= (i**temp - 1) / (i - 1)
				return int(lowerCAmelCase__ )







def   snake_case		(				UpperCAmelCase						:							Any ):
				A									=   n
				for x in set(prime_factors(lowerCAmelCase__ ) ):
								s *= (x - 1) / x
				return int(lowerCAmelCase__ )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Dict			   =['''image_processor''', '''tokenizer''']
							lowercase_			:			List[str]			   ='''AutoImageProcessor'''
							lowercase_			:			Optional[Any]			   ='''AutoTokenizer'''




							def __init__(							self  ,A__=None  ,A__=None  ,**A__):
												lowercase		      =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''  ,A__  ,)
																	lowercase		      =		kwargs.pop('''feature_extractor''')

												lowercase		      =		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__(A__  ,A__)
												lowercase		      =		self.image_processor
												lowercase		      =		False




							def __call__(							self  ,*A__  ,**A__):
												# For backward compatibility
												if self._in_target_context_manager:
																	return self.current_processor(*A__  ,**A__)

												lowercase		      =		kwargs.pop('''images'''  ,A__)
												lowercase		      =		kwargs.pop('''text'''  ,A__)
												if len(A__) > 0:
																	lowercase		      =		args[0]
																	lowercase		      =		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:
																	lowercase		      =		self.image_processor(A__  ,*A__  ,**A__)
												if text is not None:
																	lowercase		      =		self.tokenizer(A__  ,**A__)

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




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.batch_decode(*A__  ,**A__)




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.decode(*A__  ,**A__)




							@contextmanager
							def 					A__ (							self):
												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.''')
												lowercase		      =		True
												lowercase		      =		self.tokenizer
												yield
												lowercase		      =		self.image_processor
												lowercase		      =		False




							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''  ,A__  ,)
												return self.image_processor_class





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




