





'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class SCREAMING_SNAKE_CASE_ (					lowercase_							,       lowercase_							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    IFInpaintingSuperResolutionPipeline
					A__				 =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					A__				 =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}   )
					A__				 =    PipelineTesterMixin.required_optional_params - {"latents"}





					def 					__magic_name__ ( self	):
										return self._get_superresolution_dummy_components()





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=0	):
										if str(_lowerCAmelCase	).startswith("mps"	):
															lowerCamelCase__       =       torch.manual_seed(_lowerCAmelCase	)
										else:
															lowerCamelCase__       =       torch.Generator(device=_lowerCAmelCase	).manual_seed(_lowerCAmelCase	)

										lowerCamelCase__       =       floats_tensor((1, 3, 16, 16)	,  rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
										lowerCamelCase__       =       floats_tensor((1, 3, 32, 32)	,  rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
										lowerCamelCase__       =       floats_tensor((1, 3, 32, 32)	,  rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)

										lowerCamelCase__       =       {
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "original_image": original_image,
										    "mask_image": mask_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 					__magic_name__ ( self	):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3	)





					def 					__magic_name__ ( self	):
										self._test_save_load_optional_components()





					@unittest.skipIf(torch_device != "cuda"	,  reason="float16 requires CUDA"	)
					def 					__magic_name__ ( self	):
										# 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 					__magic_name__ ( self	):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2	)





					def 					__magic_name__ ( self	):
										self._test_save_load_local()






					def 					__magic_name__ ( self	):
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2	,  )








def    __UpperCamelCase      (    a,     a,     a=False) ->Dict:

					if isinstance(a,     a) and isinstance(a,     a):
										lowerCamelCase__       =       len(set_a.intersection(a))

										if alternative_union:
															lowerCamelCase__       =       len(a) + len(a)
										else:
															lowerCamelCase__       =       len(set_a.union(a))

										return intersection / union

					if isinstance(a,     (list, tuple)) and isinstance(a,     (list, tuple)):
										lowerCamelCase__       =       [element for element in set_a if element in set_b]

										if alternative_union:
															lowerCamelCase__       =       len(a) + len(a)
															return len(a) / union
										else:
															lowerCamelCase__       =       set_a + [element for element in set_b if element not in set_a]
															return len(a) / len(a)

										return len(a) / len(a)
					return None


if __name__ == "__main__":
						A_												=     {"a", "b", "c", "d", "e"}
						A_												=     {"c", "d", "e", "f", "h", "i"}
						print(jaccard_similarity(set_a, set_b))

