


import warnings
from contextlib import contextmanager

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


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      Optional[Any]  				=			'''Wav2Vec2FeatureExtractor'''
							_lowercase     :      List[Any]  				=			'''AutoTokenizer'''

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





									"""simple docstring"""







									super().__init__(UpperCamelCase_   ,						UpperCamelCase_							)
									lowercase__						=				self.feature_extractor
									lowercase__						=				False

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





									"""simple docstring"""







									try:
											return super().from_pretrained(UpperCamelCase_   ,						**UpperCamelCase_							)
									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: '''   ,						UpperCamelCase_   ,						)

											lowercase__						=				WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_   ,						**UpperCamelCase_							)
											lowercase__						=				WavaVecaCTCTokenizer.from_pretrained(UpperCamelCase_   ,						**UpperCamelCase_							)

											return cls(feature_extractor=UpperCamelCase_   ,						tokenizer=UpperCamelCase_							)

							def __call__(							self:     Optional[int]   ,						*UpperCamelCase_:     Dict   ,						**UpperCamelCase_:     Dict							)      ->       List[Any]:





									"""simple docstring"""







									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__						=				kwargs.pop('''raw_speech'''							)
									else:
											lowercase__						=				kwargs.pop('''audio'''   ,						UpperCamelCase_							)
									lowercase__						=				kwargs.pop('''sampling_rate'''   ,						UpperCamelCase_							)
									lowercase__						=				kwargs.pop('''text'''   ,						UpperCamelCase_							)
									if len(UpperCamelCase_							) > 0:
											lowercase__						=				args[0]
											lowercase__						=				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__						=				self.feature_extractor(UpperCamelCase_   ,						*UpperCamelCase_   ,						sampling_rate=UpperCamelCase_   ,						**UpperCamelCase_							)
									if text is not None:
											lowercase__						=				self.tokenizer(UpperCamelCase_   ,						**UpperCamelCase_							)

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

							def 				lowerCamelCase_			(							self:     List[Any]   ,						*UpperCamelCase_:     str   ,						**UpperCamelCase_:     Optional[int]							)      ->       Optional[int]:





									"""simple docstring"""







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

									lowercase__						=				kwargs.pop('''input_features'''   ,						UpperCamelCase_							)
									lowercase__						=				kwargs.pop('''labels'''   ,						UpperCamelCase_							)
									if len(UpperCamelCase_							) > 0:
											lowercase__						=				args[0]
											lowercase__						=				args[1:]

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

									if labels is None:
											return input_features
									elif input_features is None:
											return labels
									else:
											lowercase__						=				labels['''input_ids''']
											return input_features

							def 				lowerCamelCase_			(							self:     List[str]   ,						*UpperCamelCase_:     Optional[Any]   ,						**UpperCamelCase_:     Union[str, Any]							)      ->       Optional[int]:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     List[Any]   ,						*UpperCamelCase_:     List[Any]   ,						**UpperCamelCase_:     Dict							)      ->       str:





									"""simple docstring"""







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


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





									"""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.'''							)
									lowercase__						=				True
									lowercase__						=				self.tokenizer
									yield
									lowercase__						=				self.feature_extractor
									lowercase__						=				False






def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				len(SCREAMING_SNAKE_CASE					)
		lowercase__						=				[]
		for i in range(len(SCREAMING_SNAKE_CASE					) - pat_len + 1					):
				lowercase__						=				True
				for j in range(SCREAMING_SNAKE_CASE					):
						if s[i + j] != pattern[j]:
								lowercase__						=				False
								break
				if match_found:
						position.append(SCREAMING_SNAKE_CASE					)
		return position


if __name__ == "__main__":
			assert naive_pattern_search('ABCDEFG', 'DE') == [3]
			print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))


