
from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     HfArgumentParser(_lowerCAmelCase    )
			try:
						lowerCAmelCase_      =     parser.parse_args_into_dataclasses()[0]
			except ValueError as e:
						lowerCAmelCase_      =     "Arg --no_{0} is no longer used, please use --no-{0} instead."
						lowerCAmelCase_      =     " ".join(str(_lowerCAmelCase    ).split(" "    )[:-1]    )
						lowerCAmelCase_      =     ""
						lowerCAmelCase_      =     eval(str(_lowerCAmelCase    ).split(" "    )[-1]    )
						lowerCAmelCase_      =     []
						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(_lowerCAmelCase    )
						if len(_lowerCAmelCase    ) > 0:
									lowerCAmelCase_      =     full_error_msg + begin_error_msg + str(_lowerCAmelCase    )
						raise ValueError(_lowerCAmelCase    )

			lowerCAmelCase_      =     PyTorchBenchmark(args=_lowerCAmelCase    )
			benchmark.run()


if __name__ == "__main__":
			main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NatForImageClassification",
			    "NatModel",
			    "NatPreTrainedModel",
			    "NatBackbone",
			]

if TYPE_CHECKING:
			from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nat import (
						    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NatBackbone,
						    NatForImageClassification,
						    NatModel,
						    NatPreTrainedModel,
						)

else:
			import sys

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



