code: 
  
  
  '''simple docstring'''
  
  
  
  
  import random
  import unittest
  
  import torch
  
  from diffusers import IFSuperResolutionPipeline
  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 _snake_case   ( a__    ,   a__    ,   unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     IFSuperResolutionPipeline
  							__a												=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
  							__a												=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  							__a												=     PipelineTesterMixin.required_optional_params - {'''latents'''}
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->str:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self._get_superresolution_dummy_components()
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A=0	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if str(__A	).startswith("mps"	):
  															__lowerCamelCase		:      Optional[Any]											=					torch.manual_seed(__A	)
  											else:
  															__lowerCamelCase		:      Any											=					torch.Generator(device=__A	).manual_seed(__A	)
  
  											__lowerCamelCase		:      Union[str, Any]											=					floats_tensor((1, 3, 32, 32)			,				rng=random.Random(__A	)	).to(__A	)
  
  											__lowerCamelCase		:      int											=					{
  											    "prompt": "A painting of a squirrel eating a burger",
  											    "image": 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 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											self._test_save_load_optional_components()
  
  
  
  
  
  
  
  							@unittest.skipIf(torch_device != "cuda"			,				reason="float16 requires CUDA"	)
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->str:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											super().test_save_load_floataa(expected_max_diff=1E-1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											self._test_save_load_local()
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											self._test_inference_batch_single_identical(
  											    expected_max_diff=1E-2			,				)
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  def 							a_       (						lowercase    )			->str:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				return " ".join(sorted(set(sentence.split()    )    )    )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  