code: 
  
  from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments
  def        __SCREAMING_SNAKE_CASE    (      )				->							List[Any]:
  	__A   : Optional[int]								= HfArgumentParser(a__					)
  	try:
  		__A   : Dict								= parser.parse_args_into_dataclasses()[0]
  	except ValueError as e:
  		__A   : int								= """Arg --no_{0} is no longer used, please use --no-{0} instead."""
  		__A   : Optional[int]								= """ """.join(str(a__					).split(""" """					)[:-1]					)
  		__A   : Any								= """"""
  		__A   : Any								= eval(str(a__					).split(""" """					)[-1]					)
  		__A   : Optional[Any]								= []
  		for arg in depreciated_args:
  			# arg[2:] removes '--'
  			if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
  				# arg[5:] removes '--no_'
  				full_error_msg += arg_error_msg.format(arg[5:]					)
  			else:
  				wrong_args.append(a__					)
  		if len(a__					) > 0:
  			__A   : int								= full_error_msg + begin_error_msg + str(a__					)
  		raise ValueError(a__					)
  
  	__A   : Optional[int]								= PyTorchBenchmark(args=a__					)
  	benchmark.run()
  
  
  if __name__ == "__main__":
  			main()
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  UpperCAmelCase_       :				Optional[Any]		   =      {
      '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
      '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
  }
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			UpperCAmelCase_       :				List[str]		   =      [
  			    '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
  			    '''ProphetNetDecoder''',
  			    '''ProphetNetEncoder''',
  			    '''ProphetNetForCausalLM''',
  			    '''ProphetNetForConditionalGeneration''',
  			    '''ProphetNetModel''',
  			    '''ProphetNetPreTrainedModel''',
  			]
  
  
  if TYPE_CHECKING:
  			from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
  			from .tokenization_prophetnet import ProphetNetTokenizer
  
  			try:
  						if not is_torch_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_prophetnet import (
  						    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    ProphetNetDecoder,
  						    ProphetNetEncoder,
  						    ProphetNetForCausalLM,
  						    ProphetNetForConditionalGeneration,
  						    ProphetNetModel,
  						    ProphetNetPreTrainedModel,
  						)
  
  else:
  			import sys
  
  			UpperCAmelCase_       :				Any		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  