


'''simple docstring'''






from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments


def A_					(				)     ->  List[str]:
					__SCREAMING_SNAKE_CASE					:							int			   = HfArgumentParser(__snake_case       )
					try:
										__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
										__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = ''' '''.join(str(__snake_case       ).split(''' '''       )[:-1]       )
										__SCREAMING_SNAKE_CASE					:							List[str]			   = ''''''
										__SCREAMING_SNAKE_CASE					:							Tuple			   = eval(str(__snake_case       ).split(''' '''       )[-1]       )
										__SCREAMING_SNAKE_CASE					:							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(__snake_case       )
										if len(__snake_case       ) > 0:
															__SCREAMING_SNAKE_CASE					:							str			   = full_error_msg + begin_error_msg + str(__snake_case       )
										raise ValueError(__snake_case       )

					__SCREAMING_SNAKE_CASE					:							str			   = PyTorchBenchmark(args=__snake_case       )
					benchmark.run()


if __name__ == "__main__":
						main()










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A      =     {
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A      =     [
						    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DecisionTransformerGPT2Model""",
						    """DecisionTransformerGPT2PreTrainedModel""",
						    """DecisionTransformerModel""",
						    """DecisionTransformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_decision_transformer import (
						    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DecisionTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_decision_transformer import (
												    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DecisionTransformerGPTaModel,
												    DecisionTransformerGPTaPreTrainedModel,
												    DecisionTransformerModel,
												    DecisionTransformerPreTrainedModel,
												)


else:
						import sys

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


