"""simple docstring"""







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class      _lowercase						(				__lowerCamelCase					):
				lowerCamelCase__									=    'AutoFeatureExtractor'
				lowerCamelCase__									=    'Speech2Text2Tokenizer'

				def __init__(       self    :      Optional[int]    ,      _lowercase    :      Tuple    ,      _lowercase    :      Dict       ):


										'''simple docstring'''
										super().__init__(_lowercase    ,      _lowercase       )
										UpperCAmelCase__   		=						self.feature_extractor
										UpperCAmelCase__   		=						False

				def __call__(       self    :      Optional[int]    ,      *_lowercase    :      List[str]    ,      **_lowercase    :      int       ):


										'''simple docstring'''
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor(*_lowercase    ,      **_lowercase       )

										if "raw_speech" in kwargs:
																warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""       )
																UpperCAmelCase__   		=						kwargs.pop("""raw_speech"""       )
										else:
																UpperCAmelCase__   		=						kwargs.pop("""audio"""    ,      _lowercase       )
										UpperCAmelCase__   		=						kwargs.pop("""sampling_rate"""    ,      _lowercase       )
										UpperCAmelCase__   		=						kwargs.pop("""text"""    ,      _lowercase       )
										if len(_lowercase       ) > 0:
																UpperCAmelCase__   		=						args[0]
																UpperCAmelCase__   		=						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:
																UpperCAmelCase__   		=						self.feature_extractor(_lowercase    ,      *_lowercase    ,      sampling_rate=_lowercase    ,      **_lowercase       )
										if text is not None:
																UpperCAmelCase__   		=						self.tokenizer(_lowercase    ,      **_lowercase       )

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

				def     A	(       self    :      List[str]    ,      *_lowercase    :      Union[str, Any]    ,      **_lowercase    :      Tuple       ):


										'''simple docstring'''
										return self.tokenizer.batch_decode(*_lowercase    ,      **_lowercase       )

				def     A	(       self    :      List[str]    ,      *_lowercase    :      Dict    ,      **_lowercase    :      Union[str, Any]       ):


										'''simple docstring'''
										return self.tokenizer.decode(*_lowercase    ,      **_lowercase       )

				@contextmanager
				def     A	(       self    :      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."""       )
										UpperCAmelCase__   		=						True
										UpperCAmelCase__   		=						self.tokenizer
										yield
										UpperCAmelCase__   		=						self.feature_extractor
										UpperCAmelCase__   		=						False


"""simple docstring"""







def 					a__ (						UpperCAmelCase__    :     List[Any]     )->		Dict:
						UpperCAmelCase__   		=						[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
						for gap in gaps:
												for i in range(UpperCAmelCase__						,     len(UpperCAmelCase__     )     ):
																		UpperCAmelCase__   		=						collection[i]
																		UpperCAmelCase__   		=						i
																		while j >= gap and collection[j - gap] > insert_value:
																								UpperCAmelCase__   		=						collection[j - gap]
																								j -= gap
																		if j != i:
																								UpperCAmelCase__   		=						insert_value
						return collection


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						UpperCamelCase_    :    Union[str, Any]											=						input("""Enter numbers separated by a comma:\n""").strip()
						UpperCamelCase_    :    int											=						[int(item) for item in user_input.split(""",""")]
						print(shell_sort(unsorted))

