

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	(		__snake_case				,    __snake_case				,    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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self._get_superresolution_dummy_components()


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)

				snake_case_   		=			floats_tensor((1, 3, 16, 16)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			floats_tensor((1, 3, 32, 32)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			floats_tensor((1, 3, 32, 32)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)

				snake_case_   		=			{
				    '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				):

				"""simple docstring"""


				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self._test_save_load_optional_components()


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

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self._test_save_load_local()







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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



def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			len(lowercase__						)
	for _ in range(lowercase__						):
		for i in range(_ % 2      ,			arr_size - 1      ,			2						):
			if arr[i + 1] < arr[i]:
				snake_case_		,							snake_case_   		=			arr[i + 1], arr[i]

	return arr


if __name__ == "__main__":
		A									=  list(range(10, 0, -1))
		print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
