





'''simple docstring'''






from __future__ import annotations





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)   ->list:
						_snake_case:   Tuple  					=		[]
						_snake_case					,						_snake_case:   str  					=		input_list[low:mid], input_list[mid : high + 1]
						while left and right:
												result.append((left if left[0] <= right[0] else right).pop(0						)						)
						_snake_case:   Any  					=		result + left + right
						return input_list





def       lowercase_      (		lowercase__						)   ->list:
						if len(lowercase__						) <= 1:
												return input_list
						_snake_case:   Any  					=		list(lowercase__						)

						# iteration for two-way merging
						_snake_case:   Optional[int]  					=		2
						while p <= len(lowercase__						):
												# getting low, high and middle value for merge-sort of single list
												for i in range(0				,  len(lowercase__						)				,  lowercase__						):
																		_snake_case:   List[str]  					=		i
																		_snake_case:   Union[str, Any]  					=		i + p - 1
																		_snake_case:   Any  					=		(low + high + 1) // 2
																		_snake_case:   Union[str, Any]  					=		merge(lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)
												# final merge of last two parts
												if p * 2 >= len(lowercase__						):
																		_snake_case:   Optional[int]  					=		i
																		_snake_case:   Tuple  					=		merge(lowercase__				,  0				,  lowercase__				,  len(lowercase__						) - 1						)
																		break
												p *= 2

						return input_list


if __name__ == "__main__":
		A   : str             =			input('Enter numbers separated by a comma:\n').strip()
		if user_input == "":
				A   : List[str]             =			[]
		else:
				A   : Optional[Any]             =			[int(item.strip()) for item in user_input.split(',')]
		print(iter_merge_sort(unsorted))












'''simple docstring'''






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    "Speech2TextFeatureExtractor"
					_SCREAMING_SNAKE_CASE							   =    "Speech2TextTokenizer"


					def __init__(					self	:  List[str]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  List[str]       ):





											'''simple docstring'''





											super().__init__(__snake_case       ,     __snake_case       )
											_snake_case:   Optional[int]  					=		self.feature_extractor
											_snake_case:   Dict  					=		False


					def __call__(					self	:  Optional[int]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





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

											if "raw_speech" in kwargs:
																	warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'       )
																	_snake_case:   int  					=		kwargs.pop('raw_speech'       )
											else:
																	_snake_case:   Tuple  					=		kwargs.pop('audio'       ,     __snake_case       )
											_snake_case:   Optional[Any]  					=		kwargs.pop('sampling_rate'       ,     __snake_case       )
											_snake_case:   Optional[Any]  					=		kwargs.pop('text'       ,     __snake_case       )
											if len(__snake_case       ) > 0:
																	_snake_case:   Optional[Any]  					=		args[0]
																	_snake_case:   Union[str, Any]  					=		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:
																	_snake_case:   List[Any]  					=		self.feature_extractor(__snake_case       ,     *__snake_case       ,     sampling_rate=__snake_case       ,     **__snake_case       )
											if text is not None:
																	_snake_case:   Tuple  					=		self.tokenizer(__snake_case       ,     **__snake_case       )

											if text is None:
																	return inputs
											elif audio is None:
																	return encodings
											else:
																	_snake_case:   List[Any]  					=		encodings['input_ids']
																	return inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     *__snake_case	:  Optional[int]       ,     **__snake_case	:  str       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     *__snake_case	:  List[str]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





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




					@contextmanager
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''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.'       )
											_snake_case:   Any  					=		True
											_snake_case:   str  					=		self.tokenizer
											yield
											_snake_case:   Union[str, Any]  					=		self.feature_extractor
											_snake_case:   Tuple  					=		False





