



from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments





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

							a__:							List[str]									=PyTorchBenchmark(args=__magic_name__    )
							benchmark.run()


if __name__ == "__main__":
		main()












from typing import TYPE_CHECKING

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


snake_case__   :				Dict						=	{
    'configuration_videomae': ['VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VideoMAEConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'VideoMAEForPreTraining',
           'VideoMAEModel',
           'VideoMAEPreTrainedModel',
           'VideoMAEForVideoClassification',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				str						=	['VideoMAEFeatureExtractor']
       snake_case__   :				Tuple						=	['VideoMAEImageProcessor']

if TYPE_CHECKING:
       from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_videomae import (
                  VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VideoMAEForPreTraining,
                  VideoMAEForVideoClassification,
                  VideoMAEModel,
                  VideoMAEPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_videomae import VideoMAEFeatureExtractor
              from .image_processing_videomae import VideoMAEImageProcessor

else:
       import sys

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





