





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	'ClapFeatureExtractor'
					lowerCAmelCase_							=	('RobertaTokenizer', 'RobertaTokenizerFast')




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Any:
										super().__init__(lowerCAmelCase_				,						lowerCAmelCase_  )




					def __call__(		self				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						**lowerCAmelCase_  )		->							Union[str, Any]:
										_snake_case									=				kwargs.pop('sampling_rate'				,						lowerCAmelCase_  )

										if text is None and audios is None:
															raise ValueError('You have to specify either text or audios. Both cannot be none.'  )

										if text is not None:
															_snake_case									=				self.tokenizer(lowerCAmelCase_				,						return_tensors=lowerCAmelCase_				,						**lowerCAmelCase_  )

										if audios is not None:
															_snake_case									=				self.feature_extractor(
															    lowerCAmelCase_				,						sampling_rate=lowerCAmelCase_				,						return_tensors=lowerCAmelCase_				,						**lowerCAmelCase_  )

										if text is not None and audios is not None:
															_snake_case									=				audio_features.input_features
															return encoding
										elif text is not None:
															return encoding
										else:
															return BatchEncoding(data=dict(**lowerCAmelCase_  )				,						tensor_type=lowerCAmelCase_  )




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




					def 	lowerCAmelCase			(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Dict:
										return self.tokenizer.decode(*lowerCAmelCase_				,						**lowerCAmelCase_  )






					@property
					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										_snake_case									=				self.tokenizer.model_input_names
										_snake_case									=				self.feature_extractor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names  )  )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LukeForEntityClassification""",
					    """LukeForEntityPairClassification""",
					    """LukeForEntitySpanClassification""",
					    """LukeForMultipleChoice""",
					    """LukeForQuestionAnswering""",
					    """LukeForSequenceClassification""",
					    """LukeForTokenClassification""",
					    """LukeForMaskedLM""",
					    """LukeModel""",
					    """LukePreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
					from .tokenization_luke import LukeTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_luke import (
										    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LukeForEntityClassification,
										    LukeForEntityPairClassification,
										    LukeForEntitySpanClassification,
										    LukeForMaskedLM,
										    LukeForMultipleChoice,
										    LukeForQuestionAnswering,
										    LukeForSequenceClassification,
										    LukeForTokenClassification,
										    LukeModel,
										    LukePreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



