

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				a  (	snake_case_				):
						_lowercase    						=			"""ClapFeatureExtractor"""
						_lowercase    						=			("""RobertaTokenizer""", """RobertaTokenizerFast""")


						def __init__(       self						,					A_						,					A_	):







										'''simple docstring'''


										super().__init__(A_						,					A_	)


						def __call__(       self						,					A_=None						,					A_=None						,					A_=None						,					**A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    kwargs.pop("sampling_rate"						,					A_	)

										if text is None and audios is None:
														raise ValueError("You have to specify either text or audios. Both cannot be none."	)

										if text is not None:
														_UpperCAmelCase       :		str 		=    self.tokenizer(A_						,					return_tensors=A_						,					**A_	)

										if audios is not None:
														_UpperCAmelCase       :		Optional[Any] 		=    self.feature_extractor(
														    A_						,					sampling_rate=A_						,					return_tensors=A_						,					**A_	)

										if text is not None and audios is not None:
														_UpperCAmelCase       :		List[Any] 		=    audio_features.input_features
														return encoding
										elif text is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**A_	)						,					tensor_type=A_	)


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


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





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    self.tokenizer.model_input_names
										_UpperCAmelCase       :		int 		=    self.feature_extractor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names	)	)





import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use DeformableDetrImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


