






from typing import TYPE_CHECKING

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


snake_case    				=		{"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ViTMAEForPreTraining""",
       """ViTMAELayer""",
       """ViTMAEModel""",
       """ViTMAEPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TFViTMAEForPreTraining""",
       """TFViTMAEModel""",
       """TFViTMAEPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

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














from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def   lowerCamelCase__			(     ):

  """simple docstring"""






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


if __name__ == "__main__":
   main()






