




import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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       a__   ( snake_case__			,    snake_case__			,    unittest.TestCase				):
						_a							:   List[str] =   IFInpaintingPipeline
						_a							:   Tuple =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
						_a							:   int =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_a							:   List[Any] =   PipelineTesterMixin.required_optional_params - {"""latents"""}






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return self._get_dummy_components()






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A=0      ):




											"""simple docstring"""



											if str(_A      ).startswith("mps"      ):
																__lowerCAmelCase           =	torch.manual_seed(_A      )
											else:
																__lowerCAmelCase           =	torch.Generator(device=_A      ).manual_seed(_A      )

											__lowerCAmelCase           =	floats_tensor((1, 3, 3_2, 3_2)		,      rng=random.Random(_A      )      ).to(_A      )
											__lowerCAmelCase           =	floats_tensor((1, 3, 3_2, 3_2)		,      rng=random.Random(_A      )      ).to(_A      )

											__lowerCAmelCase           =	{
											    "prompt": "A painting of a squirrel eating a burger",
											    "image": 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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self._test_save_load_optional_components()






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




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self._test_save_load_local()






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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










def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase       ,							__lowerCAmelCase           =	[], []
					while len(SCREAMING_SNAKE_CASE_					) > 1:
										__lowerCAmelCase       ,							__lowerCAmelCase           =	min(SCREAMING_SNAKE_CASE_					), max(SCREAMING_SNAKE_CASE_					)
										start.append(SCREAMING_SNAKE_CASE_					)
										end.append(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
					end.reverse()
					return start + collection + end


if __name__ == "__main__":
					UpperCamelCase__         =      input("""Enter numbers separated by a comma:\n""").strip()
					UpperCamelCase__         =      [int(item) for item in user_input.split(""",""")]
					print(*merge_sort(unsorted), sep=""",""")




