


'''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  lowerCAmelCase_						(       snake_case__		,    snake_case__		,    unittest.TestCase      ):





		"""simple docstring"""



		a_       :Dict												=IFInpaintingSuperResolutionPipeline
		a_       :int												=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		a_       :Union[str, Any]												=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}      )
		a_       :Union[str, Any]												=PipelineTesterMixin.required_optional_params - {"latents"}

		def __a						(					self     :							List[str]						):





							'''simple docstring'''

							return self._get_superresolution_dummy_components()

		def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Dict=0						):





							'''simple docstring'''

							if str(UpperCamelCase__						).startswith("""mps"""						):
												__a       =   torch.manual_seed(UpperCamelCase__						)
							else:
												__a       =   torch.Generator(device=UpperCamelCase__						).manual_seed(UpperCamelCase__						)

							__a       =   floats_tensor((1, 3, 1_6, 1_6)				,							rng=random.Random(UpperCamelCase__						)						).to(UpperCamelCase__						)
							__a       =   floats_tensor((1, 3, 3_2, 3_2)				,							rng=random.Random(UpperCamelCase__						)						).to(UpperCamelCase__						)
							__a       =   floats_tensor((1, 3, 3_2, 3_2)				,							rng=random.Random(UpperCamelCase__						)						).to(UpperCamelCase__						)

							__a       =   {
							    """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 __a						(					self     :							Tuple						):





							'''simple docstring'''

							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)

		def __a						(					self     :							List[str]						):





							'''simple docstring'''

							self._test_save_load_optional_components()

		@unittest.skipIf(torch_device != """cuda"""				,							reason="""float16 requires CUDA"""						)
		def __a						(					self     :							str						):





							'''simple docstring'''

							super().test_save_load_floataa(expected_max_diff=1E-1						)

		def __a						(					self     :							Optional[int]						):





							'''simple docstring'''

							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)

		def __a						(					self     :							str						):





							'''simple docstring'''

							self._test_save_load_local()


		def __a						(					self     :							Any						):





							'''simple docstring'''

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










'''simple docstring'''







SCREAMING_SNAKE_CASE_      =			0  # The first color of the flag.
SCREAMING_SNAKE_CASE_      =			1  # The second color of the flag.
SCREAMING_SNAKE_CASE_      =			2  # The third color of the flag.
SCREAMING_SNAKE_CASE_      =			(red, white, blue)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					list:
     """simple docstring"""




     if not sequence:
          return []
     if len(__SCREAMING_SNAKE_CASE		) == 1:
          return list(__SCREAMING_SNAKE_CASE		)
     __a       =   0
     __a       =   len(__SCREAMING_SNAKE_CASE		) - 1
     __a       =   0
     while mid <= high:
          if sequence[mid] == colors[0]:
               __a ,						__a       =   sequence[mid], sequence[low]
               low += 1
               mid += 1
          elif sequence[mid] == colors[1]:
               mid += 1
          elif sequence[mid] == colors[2]:
               __a ,						__a       =   sequence[high], sequence[mid]
               high -= 1
          else:
               __a       =   F'''The elements inside the sequence must contains only {colors} values'''
               raise ValueError(__SCREAMING_SNAKE_CASE		)
     return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 SCREAMING_SNAKE_CASE_      =			input('Enter numbers separated by commas:\n').strip()
 SCREAMING_SNAKE_CASE_      =			[int(item.strip()) for item in user_input.split(',')]
 print(f"""{dutch_national_flag_sort(unsorted)}""")






