




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def        lowerCAmelCase_			(  ):
      _A							:  Optional[Any]    =		HfArgumentParser(snake_case_  )
      _A							:  Optional[int]    =		parser.parse_args_into_dataclasses()[0]
      _A							:  Optional[Any]    =		TensorFlowBenchmark(args=snake_case_  )
      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[Any]    =		""" """.join(str(snake_case_  ).split(""" """  )[:-1]  )
            _A							:  Union[str, Any]    =		""""""
            _A							:  Tuple    =		eval(str(snake_case_  ).split(""" """  )[-1]  )
            _A							:  Any    =		[]
            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(snake_case_  )
            if len(snake_case_  ) > 0:
                  _A							:  List[str]    =		full_error_msg + begin_error_msg + str(snake_case_  )
            raise ValueError(snake_case_  )
      benchmark.run()


if __name__ == "__main__":
  main()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_snake_case            = {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = ["EfficientFormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientFormerForImageClassification",
      "EfficientFormerForImageClassificationWithTeacher",
      "EfficientFormerModel",
      "EfficientFormerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEfficientFormerForImageClassification",
      "TFEfficientFormerForImageClassificationWithTeacher",
      "TFEfficientFormerModel",
      "TFEfficientFormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientformer import EfficientFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientformer import (
        EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientFormerForImageClassification,
        EfficientFormerForImageClassificationWithTeacher,
        EfficientFormerModel,
        EfficientFormerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_efficientformer import (
        TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEfficientFormerForImageClassification,
        TFEfficientFormerForImageClassificationWithTeacher,
        TFEfficientFormerModel,
        TFEfficientFormerPreTrainedModel,
    )

else:
  import sys

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

