

from ..utils import DummyObject, requires_backends

class     a__  (    metaclass=snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				['transformers', 'torch', 'note_seq']


			def __init__(			self					,					*lowercase					,					**lowercase				)     ->  Any:
							'''simple docstring'''






							requires_backends(self					,					["transformers", "torch", "note_seq"]				)


			@classmethod
			def    UpperCamelCase						(			cls					,					*lowercase					,					**lowercase				)     ->  Dict:
							'''simple docstring'''






							requires_backends(cls					,					["transformers", "torch", "note_seq"]				)


			@classmethod
			def    UpperCamelCase						(			cls					,					*lowercase					,					**lowercase				)     ->  Dict:
							'''simple docstring'''






							requires_backends(cls					,					["transformers", "torch", "note_seq"]				)





from typing import TYPE_CHECKING

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


UpperCamelCase								=	{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """NezhaForNextSentencePrediction""",
         """NezhaForMaskedLM""",
         """NezhaForPreTraining""",
         """NezhaForMultipleChoice""",
         """NezhaForQuestionAnswering""",
         """NezhaForSequenceClassification""",
         """NezhaForTokenClassification""",
         """NezhaModel""",
         """NezhaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nezha import (
              NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
              NezhaModel,
              NezhaPreTrainedModel,
          )


else:
     import sys

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




