


from __future__ import annotations
def 							UpperCamelCase_  (				__snake_case			:						list	,			__snake_case			:						list     )->      Tuple:




						'''simple docstring'''
						__magic_name__ 		=			[None] * (len(lowercase_     ) + len(lowercase_     ))

						__magic_name__ 		=			0  # pointer to current index for left Half
						__magic_name__ 		=			0  # pointer to current index for the right Half
						__magic_name__ 		=			0  # pointer to current index for the sorted array Half

						while pointera < len(lowercase_     ) and pointera < len(lowercase_     ):
												if left_half[pointera] < right_half[pointera]:
																		__magic_name__ 		=			left_half[pointera]
																		pointera += 1
																		index += 1
												else:
																		__magic_name__ 		=			right_half[pointera]
																		pointera += 1
																		index += 1
						while pointera < len(lowercase_     ):
												__magic_name__ 		=			left_half[pointera]
												pointera += 1
												index += 1

						while pointera < len(lowercase_     ):
												__magic_name__ 		=			right_half[pointera]
												pointera += 1
												index += 1

						return sorted_array
def 							UpperCamelCase_  (				__snake_case			:						list     )->      Tuple:




						'''simple docstring'''
						if len(lowercase_     ) <= 1:
												return array
						# the actual formula to calculate the middle element = left + (right - left) // 2
						# this avoids integer overflow in case of large N
						__magic_name__ 		=			0 + (len(lowercase_     ) - 0) // 2

						# Split the array into halves till the array length becomes equal to One
						# merge the arrays of single length returned by mergeSort function and
						# pass them into the merge arrays function which merges the array
						__magic_name__ 		=			array[:middle]
						__magic_name__ 		=			array[middle:]

						return merge(merge_sort(lowercase_     )	,			merge_sort(lowercase_     )     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     __snake_case							(      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:					Tuple 				= "AutoImageProcessor"
					SCREAMING_SNAKE_CASE_		:					Dict 				= "AutoTokenizer"
					def __init__(	self					:	List[str]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			**_lowerCAmelCase					:	List[str]     )       ->				Optional[int]:



											'''simple docstring'''





											__magic_name__ 		=			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  ,			)
																	__magic_name__ 		=			kwargs.pop('feature_extractor'     )

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

											super().__init__(_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			self.image_processor
					def __call__(	self					:	str  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	int=None  ,			_lowerCAmelCase					:	List[str]=None  ,			**_lowerCAmelCase					:	int     )       ->				int:



											'''simple docstring'''





											if text is None and images is None:
																	raise ValueError('You have to specify either text or images. Both cannot be none.'     )

											if text is not None:
																	__magic_name__ 		=			self.tokenizer(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if images is not None:
																	__magic_name__ 		=			self.image_processor(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if text is not None and images is not None:
																	__magic_name__ 		=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**_lowerCAmelCase     )  ,			tensor_type=_lowerCAmelCase     )
					def   a (	self					:	Optional[int]  ,			*_lowerCAmelCase					:	Optional[int]  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[int]:



											'''simple docstring'''





											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					def   a (	self					:	Union[str, Any]  ,			*_lowerCAmelCase					:	str  ,			**_lowerCAmelCase					:	Tuple     )       ->				int:



											'''simple docstring'''





											return self.tokenizer.decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					@property
					def   a (	self					:	List[Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer.model_input_names
											__magic_name__ 		=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
					@property
					def   a (	self					:	Optional[Any]     )       ->				Dict:



											'''simple docstring'''





											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   a (	self					:	Dict     )       ->				Tuple:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor

