



from ...processing_utils import ProcessorMixin

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			int      =  '''SpeechT5FeatureExtractor'''
						UpperCamelCase__      :			Union[str, Any]      =  '''SpeechT5Tokenizer'''






						def __init__(   self				,							_A				,							_A		):







													'''simple docstring'''

													super().__init__(_A				,							_A		)






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







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= kwargs.pop('audio'				,							_A		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('text'				,							_A		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('text_target'				,							_A		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('audio_target'				,							_A		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('sampling_rate'				,							_A		)

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

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

													if inputs is None:
																				return targets

													if targets is not None:
																				__SCREAMING_SNAKE_CASE									= labels

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

													return inputs






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= kwargs.pop('input_values'				,							_A		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('input_ids'				,							_A		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('labels'				,							_A		)

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

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

													if inputs is None:
																				return targets

													if targets is not None:
																				__SCREAMING_SNAKE_CASE									= labels

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

													return inputs






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__		:					List[str]							={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TimesformerModel''',
			    '''TimesformerForVideoClassification''',
			    '''TimesformerPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_timesformer import (
						    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TimesformerForVideoClassification,
						    TimesformerModel,
						    TimesformerPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase__		:					Union[str, Any]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






