

from ...processing_utils import ProcessorMixin

class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       'SpeechT5FeatureExtractor'
			__lowerCamelCase        =       'SpeechT5Tokenizer'


			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							super().__init__(lowercase      ,      lowercase       )


			def __call__(    self      ,      *lowercase      ,      **lowercase       )  ->						Dict:
							'''simple docstring'''






							A__        =			kwargs.pop("audio"      ,      lowercase       )
							A__        =			kwargs.pop("text"      ,      lowercase       )
							A__        =			kwargs.pop("text_target"      ,      lowercase       )
							A__        =			kwargs.pop("audio_target"      ,      lowercase       )
							A__        =			kwargs.pop("sampling_rate"      ,      lowercase       )

							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:
											A__        =			self.feature_extractor(lowercase      ,      *lowercase      ,      sampling_rate=lowercase      ,      **lowercase       )
							elif text is not None:
											A__        =			self.tokenizer(lowercase      ,      **lowercase       )
							else:
											A__        =			None

							if audio_target is not None:
											A__        =			self.feature_extractor(audio_target=lowercase      ,      *lowercase      ,      sampling_rate=lowercase      ,      **lowercase       )
											A__        =			targets["input_values"]
							elif text_target is not None:
											A__        =			self.tokenizer(lowercase      ,      **lowercase       )
											A__        =			targets["input_ids"]
							else:
											A__        =			None

							if inputs is None:
											return targets

							if targets is not None:
											A__        =			labels

											A__        =			targets.get("attention_mask"       )
											if decoder_attention_mask is not None:
															A__        =			decoder_attention_mask

							return inputs


			def      UpperCamelCase		(    self      ,      *lowercase      ,      **lowercase       )  ->						str:
							'''simple docstring'''






							A__        =			kwargs.pop("input_values"      ,      lowercase       )
							A__        =			kwargs.pop("input_ids"      ,      lowercase       )
							A__        =			kwargs.pop("labels"      ,      lowercase       )

							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:
											A__        =			self.feature_extractor.pad(lowercase      ,      *lowercase      ,      **lowercase       )
							elif input_ids is not None:
											A__        =			self.tokenizer.pad(lowercase      ,      **lowercase       )
							else:
											A__        =			None

							if labels is not None:
											if "input_ids" in labels or (isinstance(lowercase      ,      lowercase       ) and "input_ids" in labels[0]):
															A__        =			self.tokenizer.pad(lowercase      ,      **lowercase       )
															A__        =			targets["input_ids"]
											else:
															A__        =			self.feature_extractor.feature_size
															A__        =			self.feature_extractor.num_mel_bins
															A__        =			self.feature_extractor.pad(lowercase      ,      *lowercase      ,      **lowercase       )
															A__        =			feature_size_hack
															A__        =			targets["input_values"]
							else:
											A__        =			None

							if inputs is None:
											return targets

							if targets is not None:
											A__        =			labels

											A__        =			targets.get("attention_mask"       )
											if decoder_attention_mask is not None:
															A__        =			decoder_attention_mask

							return inputs


			def      UpperCamelCase		(    self      ,      *lowercase      ,      **lowercase       )  ->						Dict:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      *lowercase      ,      **lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






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




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
	from .autoencoder_kl import AutoencoderKL
	from .controlnet import ControlNetModel
	from .dual_transformer_ad import DualTransformeraDModel
	from .modeling_utils import ModelMixin
	from .prior_transformer import PriorTransformer
	from .ta_film_transformer import TaFilmDecoder
	from .transformer_ad import TransformeraDModel
	from .unet_ad import UNetaDModel
	from .unet_ad import UNetaDModel
	from .unet_ad_condition import UNetaDConditionModel
	from .unet_ad_condition import UNetaDConditionModel
	from .vq_model import VQModel

if is_flax_available():
	from .controlnet_flax import FlaxControlNetModel
	from .unet_ad_condition_flax import FlaxUNetaDConditionModel
	from .vae_flax import FlaxAutoencoderKL

