




def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  if start == end:
    return start if lst[start] > item else start + 1
  if start > end:
    return start

  _lowercase: Tuple						     =    (start + end) // 2
  if lst[mid] < item:
    return binary_search(UpperCamelCase					,     UpperCamelCase					,     mid + 1					,     UpperCamelCase							)
  elif lst[mid] > item:
    return binary_search(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     mid - 1							)
  else:
    return mid


def 		_lowerCamelCase				(					UpperCamelCase							)			->		int:





  """simple docstring"""





  _lowercase: Optional[int]						     =    len(UpperCamelCase							)

  for index in range(1					,     UpperCamelCase							):
    _lowercase: Any						     =    lst[index]
    _lowercase: List[Any]						     =    binary_search(UpperCamelCase					,     UpperCamelCase					,     0					,     index - 1							)
    _lowercase: List[str]						     =    lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

  return lst


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Any:





  """simple docstring"""





  if not left:
    return right

  if not right:
    return left

  if left[0] < right[0]:
    return [left[0], *merge(left[1:]					,     UpperCamelCase							)]

  return [right[0], *merge(UpperCamelCase					,     right[1:]							)]


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    len(UpperCamelCase							)
  _lowercase     ,					_lowercase: Union[str, Any]						     =    [], []
  _lowercase: str						     =    [lst[0]]
  _lowercase: List[str]						     =    []
  _lowercase: Union[str, Any]						     =    1
  while i < length:
    if lst[i] < lst[i - 1]:
      runs.append(UpperCamelCase							)
      _lowercase: Any						     =    [lst[i]]
    else:
      new_run.append(lst[i]							)
    i += 1
  runs.append(UpperCamelCase							)

  for run in runs:
    sorted_runs.append(insertion_sort(UpperCamelCase							)							)
  for run in sorted_runs:
    _lowercase: Tuple						     =    merge(UpperCamelCase					,     UpperCamelCase							)

  return sorted_array


def 		_lowerCamelCase				(					)			->		Tuple:





  """simple docstring"""





  _lowercase: int						     =    [5, 9, 10, 3, -4, 5, 178, 92, 46, -18, 0, 7]
  _lowercase: Dict						     =    tim_sort(UpperCamelCase							)
  print(UpperCamelCase							)


if __name__ == "__main__":
   main()






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"AutoFeatureExtractor"
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"Speech2Text2Tokenizer"






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       List[Any]     )->			Dict:
     super().__init__(UpperCamelCase_						,					UpperCamelCase_     )
     _lowercase: Optional[int]						     =    self.feature_extractor
     _lowercase: int						     =    False






   def __call__(	self   :       List[Any]						,					*UpperCamelCase_   :       Dict						,					**UpperCamelCase_   :       Optional[int]     )->			str:
     # For backward compatibility
     if self._in_target_context_manager:
       return self.current_processor(*UpperCamelCase_						,					**UpperCamelCase_     )

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

     if text is None:
       return inputs
     elif audio is None:
       return encodings
     else:
       _lowercase: int						     =    encodings['input_ids']
       return inputs






   def  _a						(	self   :       Optional[int]						,					*UpperCamelCase_   :       Tuple						,					**UpperCamelCase_   :       Dict     )->			List[str]:
     return self.tokenizer.batch_decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   def  _a						(	self   :       Tuple						,					*UpperCamelCase_   :       List[Any]						,					**UpperCamelCase_   :       Union[str, Any]     )->			Optional[int]:
     return self.tokenizer.decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   @contextmanager
   def  _a						(	self   :       Tuple     )->			Union[str, Any]:
     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.'     )
     _lowercase: Any						     =    True
     _lowercase: Optional[Any]						     =    self.tokenizer
     yield
     _lowercase: Union[str, Any]						     =    self.feature_extractor
     _lowercase: str						     =    False
