



"""simple docstring"""






import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

@slow
@require_torch_gpu
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Optional[Any]  )->      Optional[int]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



					def 	snake_case ( self							:						Tuple  )->      Optional[int]:
								lowerCamelCase__	:	Optional[Any]				=StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''  )
								lowerCamelCase__	:	List[str]				=sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								sd_pipe.set_scheduler('''sample_euler'''  )

								lowerCamelCase__	:	List[str]				='''A painting of a squirrel eating a burger'''
								lowerCamelCase__	:	Optional[int]				=torch.manual_seed(0  )
								lowerCamelCase__	:	Union[str, Any]				=sd_pipe([prompt],						generator=lowerCamelCase,						guidance_scale=9.0,						num_inference_steps=20,						output_type='''np'''  )

								lowerCamelCase__	:	int				=output.images

								lowerCamelCase__	:	List[Any]				=image[0, -3:, -3:, -1]

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__	:	str				=np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2



					def 	snake_case ( self							:						List[str]  )->      Tuple:
								lowerCamelCase__	:	int				=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
								lowerCamelCase__	:	str				=sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								sd_pipe.set_scheduler('''sample_euler'''  )

								lowerCamelCase__	:	str				='''A painting of a squirrel eating a burger'''
								lowerCamelCase__	:	List[str]				=torch.manual_seed(0  )
								lowerCamelCase__	:	Union[str, Any]				=sd_pipe([prompt],						generator=lowerCamelCase,						guidance_scale=9.0,						num_inference_steps=20,						output_type='''np'''  )

								lowerCamelCase__	:	Union[str, Any]				=output.images

								lowerCamelCase__	:	str				=image[0, -3:, -3:, -1]

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__	:	List[Any]				=np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5E-1




					def 	snake_case ( self							:						Any  )->      int:
								lowerCamelCase__	:	Optional[int]				=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
								lowerCamelCase__	:	Tuple				=sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								sd_pipe.set_scheduler('''sample_dpmpp_2m'''  )

								lowerCamelCase__	:	List[Any]				='''A painting of a squirrel eating a burger'''
								lowerCamelCase__	:	Any				=torch.manual_seed(0  )
								lowerCamelCase__	:	Any				=sd_pipe(
								    [prompt],						generator=lowerCamelCase,						guidance_scale=7.5,						num_inference_steps=15,						output_type='''np''',						use_karras_sigmas=lowerCamelCase,						)

								lowerCamelCase__	:	List[str]				=output.images

								lowerCamelCase__	:	Optional[int]				=image[0, -3:, -3:, -1]

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__	:	Tuple				=np.array(
								    [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			return " ".join(
			    ''''''.join(word[::-1]			) if len(__lowerCamelCase			) > 4 else word for word in sentence.split()			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(reverse_long_words("Hey wollef sroirraw"))




