






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()














import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[Any]			=	IFImgaImgSuperResolutionPipeline
     UpperCamelCase_							:		Any			=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCamelCase_							:		Optional[Any]			=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''}					)
     UpperCamelCase_							:		str			=	PipelineTesterMixin.required_optional_params - {'''latents'''}


     def 				_A       (				self       :	List[Any]      ):
       return self._get_superresolution_dummy_components()


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	List[str]=0      ):
       if str(UpperCAmelCase_      ).startswith("mps"      ):
         SCREAMING_SNAKE_CASE       :      Any							=    torch.manual_seed(UpperCAmelCase_      )
       else:
         SCREAMING_SNAKE_CASE       :      List[str]							=    torch.Generator(device=UpperCAmelCase_      ).manual_seed(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    floats_tensor((1, 3, 32, 32)					, rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Any							=    floats_tensor((1, 3, 16, 16)					, rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    {
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "original_image": original_image,
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "numpy",
       }

       return inputs


     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()					, reason="XFormers attention is only available with CUDA and `xformers` installed"					, )
     def 				_A       (				self       :	Tuple      ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )


     def 				_A       (				self       :	Optional[Any]      ):
       self._test_save_load_optional_components()


     @unittest.skipIf(torch_device != "cuda"					, reason="float16 requires CUDA"      )
     def 				_A       (				self       :	List[Any]      ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1      )


     def 				_A       (				self       :	Optional[int]      ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )


     def 				_A       (				self       :	Tuple      ):
       self._test_save_load_local()






     def 				_A       (				self       :	List[str]      ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2					, )






