from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def       A__   (						):
 SCREAMING_SNAKE_CASE__: Tuple=   HfArgumentParser(snake_case_   )
 SCREAMING_SNAKE_CASE__: Optional[Any]=   parser.parse_args_into_dataclasses()[0]
 SCREAMING_SNAKE_CASE__: Optional[int]=   TensorFlowBenchmark(args=snake_case_   )
 try:
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  SCREAMING_SNAKE_CASE__: Dict=   '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
  SCREAMING_SNAKE_CASE__: Optional[int]=   ''' '''.join(str(snake_case_   ).split(''' '''   )[:-1]   )
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   ''''''
  SCREAMING_SNAKE_CASE__: Tuple=   eval(str(snake_case_   ).split(''' '''   )[-1]   )
  SCREAMING_SNAKE_CASE__: Tuple=   []
  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:
   SCREAMING_SNAKE_CASE__: Union[str, Any]=   full_error_msg + begin_error_msg + str(snake_case_   )
  raise ValueError(snake_case_   )
 benchmark.run()


if __name__ == "__main__":
      main()



'''simple docstring'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    ImageTextPipelineOutput,
						    UniDiffuserPipeline,
						)
else:
						from .modeling_text_decoder import UniDiffuserTextDecoder
						from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
						from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
