

import warnings
from contextlib import contextmanager

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






class        A_		(       __A				):
							_UpperCAmelCase    :  List[Any]						      =      "Wav2Vec2FeatureExtractor"
							_UpperCAmelCase    :  Dict						      =      "AutoTokenizer"
							def __init__(		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Dict   ,SCREAMING_SNAKE_CASE__		:			str):
								super().__init__(_UpperCamelCase   ,_UpperCamelCase)
								__lowerCamelCase				:				List[str]					       = self.feature_extractor
								__lowerCamelCase				:				List[Any]					       = False
							@classmethod
							def     lowerCAmelCase     (		cls		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			str   ,**SCREAMING_SNAKE_CASE__		:			List[str]):
								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   ,)

									__lowerCamelCase				:				Any					       = WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase   ,**_UpperCamelCase)
									__lowerCamelCase				:				int					       = WavaVecaCTCTokenizer.from_pretrained(_UpperCamelCase   ,**_UpperCamelCase)

									return cls(feature_extractor=_UpperCamelCase   ,tokenizer=_UpperCamelCase)
							def __call__(		self		:			Dict   ,*SCREAMING_SNAKE_CASE__		:			int   ,**SCREAMING_SNAKE_CASE__		:			Dict):
								# 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.')
									__lowerCamelCase				:				List[Any]					       = kwargs.pop('raw_speech')
								else:
									__lowerCamelCase				:				int					       = kwargs.pop('audio'   ,_UpperCamelCase)
								__lowerCamelCase				:				List[Any]					       = kwargs.pop('sampling_rate'   ,_UpperCamelCase)
								__lowerCamelCase				:				int					       = kwargs.pop('text'   ,_UpperCamelCase)
								if len(_UpperCamelCase) > 0:
									__lowerCamelCase				:				str					       = args[0]
									__lowerCamelCase				:				str					       = 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:
									__lowerCamelCase				:				Any					       = self.feature_extractor(_UpperCamelCase   ,*_UpperCamelCase   ,sampling_rate=_UpperCamelCase   ,**_UpperCamelCase)
								if text is not None:
									__lowerCamelCase				:				Dict					       = self.tokenizer(_UpperCamelCase   ,**_UpperCamelCase)

								if text is None:
									return inputs
								elif audio is None:
									return encodings
								else:
									__lowerCamelCase				:				Any					       = encodings['input_ids']
									return inputs
							def     lowerCAmelCase     (		self		:			Any   ,*SCREAMING_SNAKE_CASE__		:			List[str]   ,**SCREAMING_SNAKE_CASE__		:			Optional[int]):
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor.pad(*_UpperCamelCase   ,**_UpperCamelCase)

								__lowerCamelCase				:				str					       = kwargs.pop('input_features'   ,_UpperCamelCase)
								__lowerCamelCase				:				Dict					       = kwargs.pop('labels'   ,_UpperCamelCase)
								if len(_UpperCamelCase) > 0:
									__lowerCamelCase				:				List[Any]					       = args[0]
									__lowerCamelCase				:				Optional[int]					       = args[1:]

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

								if labels is None:
									return input_features
								elif input_features is None:
									return labels
								else:
									__lowerCamelCase				:				Tuple					       = labels['input_ids']
									return input_features
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,*SCREAMING_SNAKE_CASE__		:			str   ,**SCREAMING_SNAKE_CASE__		:			Optional[int]):
								return self.tokenizer.batch_decode(*_UpperCamelCase   ,**_UpperCamelCase)
							def     lowerCAmelCase     (		self		:			List[str]   ,*SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,**SCREAMING_SNAKE_CASE__		:			str):
								return self.tokenizer.decode(*_UpperCamelCase   ,**_UpperCamelCase)


							@contextmanager
							def     lowerCAmelCase     (		self		:			str):
								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.')
								__lowerCamelCase				:				Optional[int]					       = True
								__lowerCamelCase				:				Any					       = self.tokenizer
								yield
								__lowerCamelCase				:				Optional[int]					       = self.feature_extractor
								__lowerCamelCase				:				List[str]					       = False







from ..utils import DummyObject, requires_backends





class 	snake_case_   (		metaclass=__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				["note_seq"]







			def __init__(     self :				Optional[int]							,   *_UpperCamelCase :				str							,   **_UpperCamelCase :				Optional[int]							)      ->Any:
									requires_backends(self							,   ['''note_seq''']							)







			@classmethod
			def 				snake_case__(     cls :				int							,   *_UpperCamelCase :				Any							,   **_UpperCamelCase :				List[Any]							)      ->int:
									requires_backends(cls							,   ['''note_seq''']							)







			@classmethod
			def 				snake_case__(     cls :				Dict							,   *_UpperCamelCase :				Optional[int]							,   **_UpperCamelCase :				Union[str, Any]							)      ->List[str]:
									requires_backends(cls							,   ['''note_seq''']							)