
"""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					(       _lowercase  ,					_lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







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




			def   lowerCamelCase__  (					self    :       Dict						)   ->			List[str]:







								"""simple docstring"""
								return self._get_superresolution_dummy_components()




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Dict							, _snake_case    :       str=0						)   ->			Dict:







								"""simple docstring"""
								if str(_snake_case						).startswith("mps"						):
													A_								=    torch.manual_seed(_snake_case						)
								else:
													A_								=    torch.Generator(device=_snake_case						).manual_seed(_snake_case						)

								A_								=    floats_tensor((1, 3, 16, 16)							, rng=random.Random(_snake_case						)						).to(_snake_case						)
								A_								=    floats_tensor((1, 3, 32, 32)							, rng=random.Random(_snake_case						)						).to(_snake_case						)
								A_								=    floats_tensor((1, 3, 32, 32)							, rng=random.Random(_snake_case						)						).to(_snake_case						)

								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   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)




			def   lowerCamelCase__  (					self    :       int						)   ->			int:







								"""simple docstring"""
								self._test_save_load_optional_components()




			@unittest.skipIf(torch_device != "cuda"							, reason="float16 requires CUDA"						)
			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Tuple:







								"""simple docstring"""
								# 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   lowerCamelCase__  (					self    :       Dict						)   ->			str:







								"""simple docstring"""
								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2						)




			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			int:







								"""simple docstring"""
								self._test_save_load_local()






			def   lowerCamelCase__  (					self    :       str						)   ->			Dict:







								"""simple docstring"""
								self._test_inference_batch_single_identical(
								    expected_max_diff=1e-2							, )






"""simple docstring"""





def 				A_  (__a					):

					'''simple docstring'''





					A_								=    len(__a					)
					while cur > 1:
										# Find the maximum number in arr
										A_								=    arr.index(max(arr[0:cur]					)					)
										# Reverse from 0 to mi
										A_								=    arr[mi::-1] + arr[mi + 1 : len(__a					)]
										# Reverse whole list
										A_								=    arr[cur - 1 :: -1] + arr[cur : len(__a					)]
										cur -= 1
					return arr


if __name__ == "__main__":
		UpperCamelCase_       :    Optional[Any]						      =			input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase_       :    Any						      =			[int(item) for item in user_input.split(''',''')]
		print(pancake_sort(unsorted))




