





from ...processing_utils import ProcessorMixin






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""SpeechT5FeatureExtractor"""
			lowerCAmelCase_							  =		"""SpeechT5Tokenizer"""





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase ,						__lowerCAmelCase							)





			def __call__(					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	kwargs.pop('''audio''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	kwargs.pop('''text''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	kwargs.pop('''text_target''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	kwargs.pop('''audio_target''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	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:
					lowerCamelCase__              =	self.feature_extractor(__lowerCAmelCase ,						*__lowerCAmelCase ,						sampling_rate=__lowerCAmelCase ,						**__lowerCAmelCase							)
				elif text is not None:
					lowerCamelCase__              =	self.tokenizer(__lowerCAmelCase ,						**__lowerCAmelCase							)
				else:
					lowerCamelCase__              =	None

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

				if inputs is None:
					return targets

				if targets is not None:
					lowerCamelCase__              =	labels

					lowerCamelCase__              =	targets.get('''attention_mask'''							)
					if decoder_attention_mask is not None:
						lowerCamelCase__              =	decoder_attention_mask

				return inputs





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	kwargs.pop('''input_values''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	kwargs.pop('''input_ids''' ,						__lowerCAmelCase							)
				lowerCamelCase__              =	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:
					lowerCamelCase__              =	self.feature_extractor.pad(__lowerCAmelCase ,						*__lowerCAmelCase ,						**__lowerCAmelCase							)
				elif input_ids is not None:
					lowerCamelCase__              =	self.tokenizer.pad(__lowerCAmelCase ,						**__lowerCAmelCase							)
				else:
					lowerCamelCase__              =	None

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

				if inputs is None:
					return targets

				if targets is not None:
					lowerCamelCase__              =	labels

					lowerCamelCase__              =	targets.get('''attention_mask'''							)
					if decoder_attention_mask is not None:
						lowerCamelCase__              =	decoder_attention_mask

				return inputs





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







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










# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


_a						=   open  # noqa: we just need to have a builtin inside this module to test it properly



