

import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       '''MCTCTFeatureExtractor'''
					UpperCAmelCase__:   Union[str, Any]  							=       '''AutoTokenizer'''






					def __init__( self    ,       A__    ,       A__			):
						super().__init__(A__    ,       A__			)
						A__							:    List[Any]        =		self.feature_extractor
						A__							:    Optional[int]        =		False






					def __call__( self    ,       *A__    ,       **A__			):
						# For backward compatibility
						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__							:    List[Any]        =		kwargs.pop("""raw_speech"""			)
						else:
							A__							:    str        =		kwargs.pop("""audio"""    ,       A__			)
						A__							:    Any        =		kwargs.pop("""sampling_rate"""    ,       A__			)
						A__							:    int        =		kwargs.pop("""text"""    ,       A__			)
						if len(A__			) > 0:
							A__							:    Optional[int]        =		args[0]
							A__							:    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:
							A__							:    List[Any]        =		self.feature_extractor(A__    ,       *A__    ,       sampling_rate=A__    ,       **A__			)
						if text is not None:
							A__							:    Dict        =		self.tokenizer(A__    ,       **A__			)

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






					def   __A			( self    ,       *A__    ,       **A__			):
						return self.tokenizer.batch_decode(*A__    ,       **A__			)






					def   __A			( self    ,       *A__    ,       **A__			):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor.pad(*A__    ,       **A__			)

						A__							:    Any        =		kwargs.pop("""input_features"""    ,       A__			)
						A__							:    Tuple        =		kwargs.pop("""labels"""    ,       A__			)
						if len(A__			) > 0:
							A__							:    Optional[Any]        =		args[0]
							A__							:    Dict        =		args[1:]

						if input_features is not None:
							A__							:    str        =		self.feature_extractor.pad(A__    ,       *A__    ,       **A__			)
						if labels is not None:
							A__							:    Optional[int]        =		self.tokenizer.pad(A__    ,       **A__			)

						if labels is None:
							return input_features
						elif input_features is None:
							return labels
						else:
							A__							:    List[Any]        =		labels["""input_ids"""]
							return input_features






					def   __A			( self    ,       *A__    ,       **A__			):
						return self.tokenizer.decode(*A__    ,       **A__			)






					@contextmanager
					def   __A			( self			):
						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__							:    Any        =		True
						A__							:    Optional[int]        =		self.tokenizer
						yield
						A__							:    Tuple        =		self.feature_extractor
						A__							:    int        =		False






def 						UpperCamelCase					(lowercase_: int = 10					)     ->      str:
	if not isinstance(lowercase_    ,      lowercase_					) or n < 0:
		raise ValueError("""Invalid input"""					)
	A__							:    List[str]        =		10**n
	A__							:    Any        =		28433 * (pow(2    ,      7830457    ,      lowercase_					)) + 1
	return str(number % modulus					)


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

	testmod()
	print(f'''{solution(10) = }''')



