


"""simple docstring"""


from __future__ import annotations






def    a__       ( __lowercase   ,	__lowercase      )		->		str:
				_A         =	0
				_A         =	len(__lowercase      ) - 1
				while left <= right:
								_A         =	(left + right) // 2
								if left == right:
												if sorted_list[middle] < item:
																_A         =	middle + 1
												break
								elif sorted_list[middle] < item:
												_A         =	middle + 1
								else:
												_A         =	middle - 1
				sorted_list.insert(__lowercase   ,	__lowercase      )
				return sorted_list






def    a__       ( __lowercase   ,	__lowercase      )		->		Any:
				_A         =	[]
				while left and right:
								if left[0][0] < right[0][0]:
												result.append(left.pop(0      )      )
								else:
												result.append(right.pop(0      )      )
				return result + left + right






def    a__       ( __lowercase      )		->		Any:
				_A         =	len(__lowercase      )
				if length <= 1:
								return list_ad
				_A         =	length // 2
				return merge(sortlist_ad(list_ad[:middle]      )   ,	sortlist_ad(list_ad[middle:]      )      )






def    a__       ( __lowercase      )		->		list[int]:

				if len(__lowercase      ) <= 1:
								return collection
				_A         =	[]
				_A         =	False
				for i in range(0   ,	len(__lowercase      )   ,	2      ):
								if i == len(__lowercase      ) - 1:
												_A         =	True
								else:
												if collection[i] < collection[i + 1]:
																two_paired_list.append([collection[i], collection[i + 1]]      )
												else:
																two_paired_list.append([collection[i + 1], collection[i]]      )
				_A         =	sortlist_ad(__lowercase      )
				_A         =	[i[0] for i in sorted_list_ad]
				result.append(sorted_list_ad[-1][1]      )
				if has_last_odd_item:
								_A         =	collection[-1]
								_A         =	binary_search_insertion(__lowercase   ,	__lowercase      )
				_A         =	False
				for i in range(len(__lowercase      ) - 1      ):
								if result[i] == collection[-1] and has_last_odd_item:
												_A         =	True
								_A         =	sorted_list_ad[i][1]
								# If last_odd_item is inserted before the item's index,
								# you should forward index one more.
								if is_last_odd_item_inserted_before_this_index:
												_A         =	result[: i + 2] + binary_search_insertion(result[i + 2 :]   ,	__lowercase      )
								else:
												_A         =	result[: i + 1] + binary_search_insertion(result[i + 1 :]   ,	__lowercase      )

				return result


if __name__ == "__main__":
		a_											=			input("Enter numbers separated by a comma:\n").strip()
		a_											=			[int(item) for item in user_input.split(",")]
		print(merge_insertion_sort(unsorted))



"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	'AutoFeatureExtractor'
		__UpperCamelCase							=	'Speech2Text2Tokenizer'







		def __init__(    self					:				int			,      a__					:				Tuple			,      a__					:				Optional[Any]						)				->  str:





						'''simple docstring'''







						super().__init__(a__			,      a__						)
						_A         =	self.feature_extractor
						_A         =	False







		def __call__(    self					:				Optional[int]			,      *a__					:				List[str]			,      **a__					:				Any						)				->  List[Any]:





						'''simple docstring'''







						if self._in_target_context_manager:
										return self.current_processor(*a__			,      **a__						)

						if "raw_speech" in kwargs:
										warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."						)
										_A         =	kwargs.pop("raw_speech"						)
						else:
										_A         =	kwargs.pop("audio"			,      a__						)
						_A         =	kwargs.pop("sampling_rate"			,      a__						)
						_A         =	kwargs.pop("text"			,      a__						)
						if len(a__						) > 0:
										_A         =	args[0]
										_A         =	args[1:]

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

						if audio is not None:
										_A         =	self.feature_extractor(a__			,      *a__			,      sampling_rate=a__			,      **a__						)
						if text is not None:
										_A         =	self.tokenizer(a__			,      **a__						)

						if text is None:
										return inputs
						elif audio is None:
										return encodings
						else:
										_A         =	encodings["input_ids"]
										return inputs







		def        a_       (    self					:				List[Any]			,      *a__					:				Tuple			,      **a__					:				List[Any]						)				->  List[Any]:





						'''simple docstring'''







						return self.tokenizer.batch_decode(*a__			,      **a__						)







		def        a_       (    self					:				Any			,      *a__					:				Dict			,      **a__					:				Tuple						)				->  Union[str, Any]:





						'''simple docstring'''







						return self.tokenizer.decode(*a__			,      **a__						)







		@contextmanager
		def        a_       (    self					:				Optional[int]						)				->  Optional[int]:





						'''simple docstring'''







						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 audio inputs, or in a separate call."						)
						_A         =	True
						_A         =	self.tokenizer
						yield
						_A         =	self.feature_extractor
						_A         =	False