


from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def        UpperCamelCase_(				)->		Dict:
			UpperCamelCase__					=       HfArgumentParser(_A  )
			UpperCamelCase__					=       parser.parse_args_into_dataclasses()[0]
			UpperCamelCase__					=       TensorFlowBenchmark(args=_A  )
			try:
						UpperCamelCase__					=       parser.parse_args_into_dataclasses()[0]
			except ValueError as e:
						UpperCamelCase__					=       "Arg --no_{0} is no longer used, please use --no-{0} instead."
						UpperCamelCase__					=       " ".join(str(_A  ).split(" "  )[:-1]  )
						UpperCamelCase__					=       ""
						UpperCamelCase__					=       eval(str(_A  ).split(" "  )[-1]  )
						UpperCamelCase__					=       []
						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(_A  )
						if len(_A  ) > 0:
									UpperCamelCase__					=       full_error_msg + begin_error_msg + str(_A  )
						raise ValueError(_A  )
			benchmark.run()


if __name__ == "__main__":
						main()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__UpperCamelCase    		=       {
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TrOCRForCausalLM',
						    'TrOCRPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

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


