


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "Speech2TextFeatureExtractor"
     __SCREAMING_SNAKE_CASE  			= "Speech2TextTokenizer"


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				int:
            super().__init__(__lowerCamelCase ,						__lowerCamelCase)
            _A      :       Any				    = self.feature_extractor
            _A      :       int				    = False


     def __call__(						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Union[str, Any]:
            # For backward compatibility
            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      :       Optional[int]				    = kwargs.pop("raw_speech")
            else:
                   _A      :       Optional[int]				    = kwargs.pop("audio" ,						__lowerCamelCase)
            _A      :       Optional[Any]				    = kwargs.pop("sampling_rate" ,						__lowerCamelCase)
            _A      :       List[Any]				    = kwargs.pop("text" ,						__lowerCamelCase)
            if len(__lowerCamelCase) > 0:
                   _A      :       int				    = args[0]
                   _A      :       Tuple				    = 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      :       int				    = self.feature_extractor(__lowerCamelCase ,						*__lowerCamelCase ,						sampling_rate=__lowerCamelCase ,						**__lowerCamelCase)
            if text is not None:
                   _A      :       int				    = self.tokenizer(__lowerCamelCase ,						**__lowerCamelCase)

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


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Any:
            return self.tokenizer.batch_decode(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Tuple:
            return self.tokenizer.decode(*__lowerCamelCase ,						**__lowerCamelCase)


     @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.")
            _A      :       Optional[int]				    = True
            _A      :       str				    = self.tokenizer
            yield
            _A      :       Union[str, Any]				    = self.feature_extractor
            _A      :       List[Any]				    = False




import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase__      		= logging.get_logger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				None:
            warnings.warn(
                "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                " use BeitImageProcessor instead." ,						__lowerCamelCase ,						)
            super().__init__(*__lowerCamelCase ,						**__lowerCamelCase)
