






"""simple docstring"""
from ...processing_utils import ProcessorMixin







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''SpeechT5FeatureExtractor'''
	__magic_name__	      =				'''SpeechT5Tokenizer'''





	def __init__(      self					: Any	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: List[Any]						)						->			Tuple:
				super().__init__(lowerCAmelCase_	,	lowerCAmelCase_						)





	def __call__(      self					: List[Any]	,	*lowerCAmelCase_					: str	,	**lowerCAmelCase_					: Dict						)						->			Tuple:
				UpperCAmelCase_     : List[Any]			     =					kwargs.pop("audio"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					kwargs.pop("text"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					kwargs.pop("text_target"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : List[str]			     =					kwargs.pop("audio_target"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Any			     =					kwargs.pop("sampling_rate"	,	lowerCAmelCase_						)

				if audio is not None and text is not None:
							raise ValueError(
							    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"						)
				if audio_target is not None and text_target is not None:
							raise ValueError(
							    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"						)
				if audio is None and audio_target is None and text is None and text_target is None:
							raise ValueError(
							    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."						)

				if audio is not None:
							UpperCAmelCase_     : List[Any]			     =					self.feature_extractor(lowerCAmelCase_	,	*lowerCAmelCase_	,	sampling_rate=lowerCAmelCase_	,	**lowerCAmelCase_						)
				elif text is not None:
							UpperCAmelCase_     : List[Any]			     =					self.tokenizer(lowerCAmelCase_	,	**lowerCAmelCase_						)
				else:
							UpperCAmelCase_     : Any			     =					None

				if audio_target is not None:
							UpperCAmelCase_     : List[Any]			     =					self.feature_extractor(audio_target=lowerCAmelCase_	,	*lowerCAmelCase_	,	sampling_rate=lowerCAmelCase_	,	**lowerCAmelCase_						)
							UpperCAmelCase_     : Optional[Any]			     =					targets["input_values"]
				elif text_target is not None:
							UpperCAmelCase_     : Any			     =					self.tokenizer(lowerCAmelCase_	,	**lowerCAmelCase_						)
							UpperCAmelCase_     : Any			     =					targets["input_ids"]
				else:
							UpperCAmelCase_     : List[str]			     =					None

				if inputs is None:
							return targets

				if targets is not None:
							UpperCAmelCase_     : Optional[Any]			     =					labels

							UpperCAmelCase_     : Any			     =					targets.get("attention_mask"						)
							if decoder_attention_mask is not None:
										UpperCAmelCase_     : List[str]			     =					decoder_attention_mask

				return inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	*lowerCAmelCase_					: Union[str, Any]	,	**lowerCAmelCase_					: Dict						)						->			Any:
				UpperCAmelCase_     : Any			     =					kwargs.pop("input_values"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[int]			     =					kwargs.pop("input_ids"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : List[Any]			     =					kwargs.pop("labels"	,	lowerCAmelCase_						)

				if input_values is not None and input_ids is not None:
							raise ValueError("Cannot process both `input_values` and `input_ids` inputs."						)
				if input_values is None and input_ids is None and labels is None:
							raise ValueError(
							    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."						)

				if input_values is not None:
							UpperCAmelCase_     : str			     =					self.feature_extractor.pad(lowerCAmelCase_	,	*lowerCAmelCase_	,	**lowerCAmelCase_						)
				elif input_ids is not None:
							UpperCAmelCase_     : Tuple			     =					self.tokenizer.pad(lowerCAmelCase_	,	**lowerCAmelCase_						)
				else:
							UpperCAmelCase_     : Tuple			     =					None

				if labels is not None:
							if "input_ids" in labels or (isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) and "input_ids" in labels[0]):
										UpperCAmelCase_     : Union[str, Any]			     =					self.tokenizer.pad(lowerCAmelCase_	,	**lowerCAmelCase_						)
										UpperCAmelCase_     : Dict			     =					targets["input_ids"]
							else:
										UpperCAmelCase_     : Any			     =					self.feature_extractor.feature_size
										UpperCAmelCase_     : Tuple			     =					self.feature_extractor.num_mel_bins
										UpperCAmelCase_     : Union[str, Any]			     =					self.feature_extractor.pad(lowerCAmelCase_	,	*lowerCAmelCase_	,	**lowerCAmelCase_						)
										UpperCAmelCase_     : List[str]			     =					feature_size_hack
										UpperCAmelCase_     : Tuple			     =					targets["input_values"]
				else:
							UpperCAmelCase_     : Tuple			     =					None

				if inputs is None:
							return targets

				if targets is not None:
							UpperCAmelCase_     : Union[str, Any]			     =					labels

							UpperCAmelCase_     : Optional[Any]			     =					targets.get("attention_mask"						)
							if decoder_attention_mask is not None:
										UpperCAmelCase_     : Any			     =					decoder_attention_mask

				return inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	*lowerCAmelCase_					: Any	,	**lowerCAmelCase_					: Any						)						->			int:
				return self.tokenizer.batch_decode(*lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	*lowerCAmelCase_					: List[Any]	,	**lowerCAmelCase_					: Tuple						)						->			List[Any]:
				return self.tokenizer.decode(*lowerCAmelCase_	,	**lowerCAmelCase_						)











"""simple docstring"""
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.'''
)



