


from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)








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__)




