


from ...processing_utils import ProcessorMixin






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'SpeechT5FeatureExtractor'
							_snake_case     		=  'SpeechT5Tokenizer'



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													super().__init__(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)



							def __call__(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			kwargs.pop('''audio'''     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			kwargs.pop('''text'''     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			kwargs.pop('''text_target'''     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			kwargs.pop('''audio_target'''     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			kwargs.pop('''sampling_rate'''     ,	SCREAMING_SNAKE_CASE_			)

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

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

													if inputs is None:
																			return targets

													if targets is not None:
																			__UpperCamelCase       =			labels

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

													return inputs



							def     A__		(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			kwargs.pop('''input_values'''     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			kwargs.pop('''input_ids'''     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			kwargs.pop('''labels'''     ,	SCREAMING_SNAKE_CASE_			)

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

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

													if inputs is None:
																			return targets

													if targets is not None:
																			__UpperCamelCase       =			labels

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

													return inputs



							def     A__		(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






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



							def     A__		(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






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






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax






@require_flax
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  FlaxAutoencoderKL



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			4
													__UpperCamelCase       =			3
													__UpperCamelCase       =			(32, 32)

													__UpperCamelCase       =			jax.random.PRNGKey(0			)
													__UpperCamelCase       =			jax.random.uniform(SCREAMING_SNAKE_CASE_     ,	((batch_size, num_channels) + sizes)			)

													return {"sample": image, "prng_key": prng_key}



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			{
													    '''block_out_channels''': [32, 64],
													    '''in_channels''': 3,
													    '''out_channels''': 3,
													    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
													    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
													    '''latent_channels''': 4,
													}
													__UpperCamelCase       =			self.dummy_input
													return init_dict, inputs_dict




