


from typing import TYPE_CHECKING

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


__UpperCamelCase				:  Tuple      					=							{
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCamelCase				:  Dict      					=							[
         'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ErnieForCausalLM',
         'ErnieForMaskedLM',
         'ErnieForMultipleChoice',
         'ErnieForNextSentencePrediction',
         'ErnieForPreTraining',
         'ErnieForQuestionAnswering',
         'ErnieForSequenceClassification',
         'ErnieForTokenClassification',
         'ErnieModel',
         'ErniePreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ernie import (
              ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
              ErnieForCausalLM,
              ErnieForMaskedLM,
              ErnieForMultipleChoice,
              ErnieForNextSentencePrediction,
              ErnieForPreTraining,
              ErnieForQuestionAnswering,
              ErnieForSequenceClassification,
              ErnieForTokenClassification,
              ErnieModel,
              ErniePreTrainedModel,
          )

else:
     import sys

     __UpperCamelCase				:  Optional[int]      					=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def      snake_case_						( ):
   UpperCAmelCase_							:      str						=   HfArgumentParser(__lowercase       )
   UpperCAmelCase_							:      Optional[Any]						=   parser.parse_args_into_dataclasses()[0]
   UpperCAmelCase_							:      Optional[int]						=   TensorFlowBenchmark(args=__lowercase       )
   try:
      UpperCAmelCase_							:      List[Any]						=   parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      UpperCAmelCase_							:      List[Any]						=   '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
      UpperCAmelCase_							:      List[str]						=   ''' '''.join(str(__lowercase       ).split(''' '''       )[:-1]       )
      UpperCAmelCase_							:      Optional[int]						=   ''''''
      UpperCAmelCase_							:      Dict						=   eval(str(__lowercase       ).split(''' '''       )[-1]       )
      UpperCAmelCase_							:      int						=   []
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]       )
         else:
            wrong_args.append(__lowercase       )
      if len(__lowercase       ) > 0:
         UpperCAmelCase_							:      Tuple						=   full_error_msg + begin_error_msg + str(__lowercase       )
      raise ValueError(__lowercase       )
   benchmark.run()


if __name__ == "__main__":
       main()