


'''simple docstring'''

from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






def __UpperCamelCase   (			)      ->     Dict:







     '''simple docstring'''






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


if __name__ == "__main__":
   main()




'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__				     =		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				     =		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

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

