


'''simple docstring'''







from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments







def _UpperCamelCase			(	)				->						Dict:


      '''simple docstring'''



      UpperCamelCase__      =							HfArgumentParser(SCREAMING_SNAKE_CASE_       )
      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(SCREAMING_SNAKE_CASE_       ).split(" "       )[:-1]       )
            UpperCamelCase__      =							''
            UpperCamelCase__      =							eval(str(SCREAMING_SNAKE_CASE_       ).split(" "       )[-1]       )
            UpperCamelCase__      =							[]
            for arg in depreciated_args:
                  # arg[2:] removes '--'
                  if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                        # arg[5:] removes '--no_'
                        full_error_msg += arg_error_msg.format(arg[5:]       )
                  else:
                        wrong_args.append(SCREAMING_SNAKE_CASE_       )
            if len(SCREAMING_SNAKE_CASE_       ) > 0:
                  UpperCamelCase__      =							full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE_       )
            raise ValueError(SCREAMING_SNAKE_CASE_       )

      UpperCamelCase__      =							PyTorchBenchmark(args=SCREAMING_SNAKE_CASE_       )
      benchmark.run()


if __name__ == "__main__":
  main()






'''simple docstring'''




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[List[PIL.Image.Image], np.ndarray]
   A				:							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .modeling_roberta_series import RobertaSeriesModelWithTransformation
 from .pipeline_alt_diffusion import AltDiffusionPipeline
 from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline




