





'''simple docstring'''




from ..utils import DummyObject, requires_backends




class      UpperCamelCase__					(					metaclass=lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	['''flax''', '''transformers''']


					def __init__(						self				:			Tuple      ,						*lowerCamelCase_				:			Optional[Any]      ,						**lowerCamelCase_				:			str						):

							'''simple docstring'''



							requires_backends(self      ,						["""flax""", """transformers"""]						)


					@classmethod
					def lowerCamelCase_			(						cls				:			Dict      ,						*lowerCamelCase_				:			Optional[int]      ,						**lowerCamelCase_				:			str						):

							'''simple docstring'''



							requires_backends(cls      ,						["""flax""", """transformers"""]						)



					@classmethod
					def lowerCamelCase_			(						cls				:			Tuple      ,						*lowerCamelCase_				:			List[Any]      ,						**lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							requires_backends(cls      ,						["""flax""", """transformers"""]						)







class      UpperCamelCase__					(					metaclass=lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	['''flax''', '''transformers''']


					def __init__(						self				:			Any      ,						*lowerCamelCase_				:			int      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							requires_backends(self      ,						["""flax""", """transformers"""]						)


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

							'''simple docstring'''



							requires_backends(cls      ,						["""flax""", """transformers"""]						)



					@classmethod
					def lowerCamelCase_			(						cls				:			str      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							requires_backends(cls      ,						["""flax""", """transformers"""]						)







class      UpperCamelCase__					(					metaclass=lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	['''flax''', '''transformers''']


					def __init__(						self				:			Union[str, Any]      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



							requires_backends(self      ,						["""flax""", """transformers"""]						)


					@classmethod
					def lowerCamelCase_			(						cls				:			int      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							requires_backends(cls      ,						["""flax""", """transformers"""]						)



					@classmethod
					def lowerCamelCase_			(						cls				:			str      ,						*lowerCamelCase_				:			Tuple      ,						**lowerCamelCase_				:			Any						):

							'''simple docstring'''



							requires_backends(cls      ,						["""flax""", """transformers"""]						)







class      UpperCamelCase__					(					metaclass=lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	['''flax''', '''transformers''']


					def __init__(						self				:			str      ,						*lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							requires_backends(self      ,						["""flax""", """transformers"""]						)


					@classmethod
					def lowerCamelCase_			(						cls				:			str      ,						*lowerCamelCase_				:			Optional[int]      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							requires_backends(cls      ,						["""flax""", """transformers"""]						)



					@classmethod
					def lowerCamelCase_			(						cls				:			Dict      ,						*lowerCamelCase_				:			Dict      ,						**lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							requires_backends(cls      ,						["""flax""", """transformers"""]						)










'''simple docstring'''




import warnings
from contextlib import contextmanager

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




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''Wav2Vec2FeatureExtractor'''
					SCREAMING_SNAKE_CASE__				=	'''AutoTokenizer'''


					def __init__(						self				:			Optional[int]      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				List[Any]		=			False


					@classmethod
					def lowerCamelCase_			(						cls				:			int      ,						lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Any						):

							'''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_      ,						)

									SCREAMING_SNAKE_CASE :				Optional[Any]		=			WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Dict		=			WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)

									return cls(feature_extractor=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)


					def __call__(						self				:			List[Any]      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			int						):

							'''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."""						)
									SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""raw_speech"""						)
							else:
									SCREAMING_SNAKE_CASE :				Any		=			kwargs.pop("""audio"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			kwargs.pop("""sampling_rate"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			kwargs.pop("""text"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			args[0]
									SCREAMING_SNAKE_CASE :				Dict		=			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:
									SCREAMING_SNAKE_CASE :				str		=			self.feature_extractor(lowerCamelCase_      ,						*lowerCamelCase_      ,						sampling_rate=lowerCamelCase_      ,						**lowerCamelCase_						)
							if text is not None:
									SCREAMING_SNAKE_CASE :				List[Any]		=			self.tokenizer(lowerCamelCase_      ,						**lowerCamelCase_						)

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


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						*lowerCamelCase_				:			Tuple      ,						**lowerCamelCase_				:			Dict						):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE :				str		=			kwargs.pop("""input_features"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""labels"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			args[0]
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			args[1:]

							if input_features is not None:
									SCREAMING_SNAKE_CASE :				Any		=			self.feature_extractor.pad(lowerCamelCase_      ,						*lowerCamelCase_      ,						**lowerCamelCase_						)
							if labels is not None:
									SCREAMING_SNAKE_CASE :				int		=			self.tokenizer.pad(lowerCamelCase_      ,						**lowerCamelCase_						)

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


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

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[str]      ,						*lowerCamelCase_				:			Optional[int]      ,						**lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



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



					@contextmanager
					def lowerCamelCase_			(						self				:			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."""						)
							SCREAMING_SNAKE_CASE :				int		=			True
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.tokenizer
							yield
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			False



