


import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Dict						      =	"Speech2TextFeatureExtractor"
     UpperCAmelCase__ :  str						      =	"Speech2TextTokenizer"





     def __init__(    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        super().__init__(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	self.feature_extractor
        __lowerCamelCase:				List[Any]     =	False





     def __call__(    self					:				List[str]    ,	*SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]							):
        # For backward compatibility
        if self._in_target_context_manager:
           return self.current_processor(*SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

        if "raw_speech" in kwargs:
           warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""							)
           __lowerCamelCase:				Dict     =	kwargs.pop("""raw_speech"""							)
        else:
           __lowerCamelCase:				int     =	kwargs.pop("""audio"""    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	kwargs.pop("""sampling_rate"""    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	kwargs.pop("""text"""    ,	SCREAMING_SNAKE_CASE_							)
        if len(SCREAMING_SNAKE_CASE_							) > 0:
           __lowerCamelCase:				Dict     =	args[0]
           __lowerCamelCase:				Optional[Any]     =	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:				List[str]     =	self.feature_extractor(SCREAMING_SNAKE_CASE_    ,	*SCREAMING_SNAKE_CASE_    ,	sampling_rate=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)
        if text is not None:
           __lowerCamelCase:				Any     =	self.tokenizer(SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	*SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	**SCREAMING_SNAKE_CASE_					:				str							):
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	*SCREAMING_SNAKE_CASE_					:				Any    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]							):
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)







     @contextmanager
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        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:				Any     =	True
        __lowerCamelCase:				Any     =	self.tokenizer
        yield
        __lowerCamelCase:				Any     =	self.feature_extractor
        __lowerCamelCase:				Optional[Any]     =	False


