





def   __lowerCamelCase   (				__a  :str       )						->							str:






		"""simple docstring"""

		A__     =  0

		# if input_string is "aba" than new_input_string become "a|b|a"
		A__     =  """"""
		A__     =  """"""

		# append each character + "|" in new_string for range(0, length-1)
		for i in input_string[: len(__a       ) - 1]:
				new_input_string += i + "|"
		# append last character
		new_input_string += input_string[-1]

		# we will store the starting and ending of previous furthest ending palindromic
		# substring
		A__ ,     A__     =  0, 0

		# length[i] shows the length of palindromic substring with center i
		A__     =  [1 for i in range(len(__a       )       )]

		# for each character in new_string find corresponding palindromic string
		A__     =  0
		for j in range(len(__a       )       ):
				A__     =  1 if j > r else min(length[l + r - j] // 2       ,	r - j + 1       )
				while (
				    j - k >= 0
				    and j + k < len(__a       )
				    and new_input_string[k + j] == new_input_string[j - k]
				):
						k += 1

				A__     =  2 * k - 1

				# does this string is ending after the previously explored end (that is r) ?
				# if yes the update the new r to the last index of this
				if j + k - 1 > r:
						A__     =  j - k + 1  # noqa: E741
						A__     =  j + k - 1

				# update max_length and start position
				if max_length < length[j]:
						A__     =  length[j]
						A__     =  j

    # create that string
		A__     =  new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
		for i in s:
				if i != "|":
						output_string += i

		return output_string


if __name__ == "__main__":
					import doctest

					doctest.testmod()










import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Dict							     =       '''Wav2Vec2FeatureExtractor'''
			__lowerCamelCase      :			str							     =       '''AutoTokenizer'''






			def __init__(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       List[Any]				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Tuple:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase				,      __lowerCAmelCase  )
					A__     =  self.feature_extractor
					A__     =  False






			@classmethod
			def   a_			(     cls  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      int:







					"""simple docstring"""






					try:
							return super().from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )
					except OSError:
							warnings.warn(
							    f'Loading a tokenizer inside {cls.__name__} from a config that does not'
							    """ include a `tokenizer_class` attribute is deprecated and will be """
							    """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
							    """ attribute to either your `config.json` or `tokenizer_config.json` """
							    """file to suppress this warning: """				,      __lowerCAmelCase				,      )

							A__     =  WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )
							A__     =  WavaVecaCTCTokenizer.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )

							return cls(feature_extractor=__lowerCAmelCase				,      tokenizer=__lowerCAmelCase  )






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







					"""simple docstring"""






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

					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"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""sampling_rate"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""text"""				,      __lowerCAmelCase  )
					if len(__lowerCAmelCase  ) > 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(__lowerCAmelCase				,      *__lowerCAmelCase				,      sampling_rate=__lowerCAmelCase				,      **__lowerCAmelCase  )
					if text is not None:
							A__     =  self.tokenizer(__lowerCAmelCase				,      **__lowerCAmelCase  )

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






			def   a_			(     self  :       List[str]				,      *__lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Tuple  )      ->      List[str]:







					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor.pad(*__lowerCAmelCase				,      **__lowerCAmelCase  )

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

					if input_features is not None:
							A__     =  self.feature_extractor.pad(__lowerCAmelCase				,      *__lowerCAmelCase				,      **__lowerCAmelCase  )
					if labels is not None:
							A__     =  self.tokenizer.pad(__lowerCAmelCase				,      **__lowerCAmelCase  )

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






			def   a_			(     self  :       Optional[int]				,      *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Dict:







					"""simple docstring"""






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






			def   a_			(     self  :       Any				,      *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      List[str]:







					"""simple docstring"""






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







			@contextmanager
			def   a_			(     self  :       Union[str, Any]  )      ->      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



