from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 						a__							(       __snake_case		):
			A__    :       Dict        =      'ClapFeatureExtractor'
			A__    :       Optional[int]        =      ('RobertaTokenizer', 'RobertaTokenizerFast')



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Dict:
								super().__init__(a_				,						a_   )



			def __call__(						self				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   Optional[int]:
								__a		 =       kwargs.pop('sampling_rate'				,						a_   )

								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:
													__a		 =       self.tokenizer(a_				,						return_tensors=a_				,						**a_   )

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

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



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								return self.tokenizer.batch_decode(*a_				,						**a_   )



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Tuple:
								return self.tokenizer.decode(*a_				,						**a_   )







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






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase_    :		Union[str, Any]							=	{"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	["""GPTNeoXTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		List[str]							=	[
	    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXForCausalLM""",
	    """GPTNeoXForQuestionAnswering""",
	    """GPTNeoXForSequenceClassification""",
	    """GPTNeoXForTokenClassification""",
	    """GPTNeoXLayer""",
	    """GPTNeoXModel""",
	    """GPTNeoXPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCamelCase_    :		Dict							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




