
def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					# base case
					if len(lowerCAmelCase__						) == 1:
										return a[0], a[0]
					else:
										# split A into half.
										lowercase		      =		a[: len(lowerCAmelCase__						) // 2]
										lowercase		      =		a[len(lowerCAmelCase__						) // 2 :]

										# 2 sub problems, 1/2 of original size.
										lowercase      ,		lowercase		      =		max_difference(lowerCAmelCase__						)
										lowercase      ,		lowercase		      =		max_difference(lowerCAmelCase__						)

										# get min of first and max of second
										# linear time
										lowercase		      =		min(lowerCAmelCase__						)
										lowercase		      =		max(lowerCAmelCase__						)

										# 3 cases, either (small1, big1),
										# (min_first, max_second), (small2, big2)
										# constant comparisons
										if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
															return smalla, biga
										elif biga - smalla > max_second - min_first:
															return smalla, biga
										else:
															return min_first, max_second


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




