






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__UpperCAmelCase							:   Tuple							=  False


class 					_snake_case						(			unittest.TestCase ):
	pass




@nightly
@require_torch_gpu
class 					_snake_case						(			unittest.TestCase ):






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






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :Optional[Any]         =   VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"       )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(UpperCamelCase       )
					pipe.set_progress_bar_config(disable=UpperCamelCase       )

					snake_case__      :int         =   "A painting of a squirrel eating a burger "
					snake_case__      :int         =   torch.manual_seed(0       )
					snake_case__      :List[str]         =   pipe(
					    prompt=UpperCamelCase					,generator=UpperCamelCase					,guidance_scale=7.5					,num_inference_steps=2					,output_type="numpy"       ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(UpperCamelCase       )
									snake_case__      :Dict         =   VersatileDiffusionTextToImagePipeline.from_pretrained(UpperCamelCase       )
					pipe.to(UpperCamelCase       )
					pipe.set_progress_bar_config(disable=UpperCamelCase       )

					snake_case__      :List[str]         =   generator.manual_seed(0       )
					snake_case__      :int         =   pipe(
					    prompt=UpperCamelCase					,generator=UpperCamelCase					,guidance_scale=7.5					,num_inference_steps=2					,output_type="numpy"       ).images

					assert np.abs(image - new_image       ).sum() < 1E-5, "Models don't have the same forward pass"






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Dict         =   VersatileDiffusionTextToImagePipeline.from_pretrained(
					    "shi-labs/versatile-diffusion"					,torch_dtype=torch.floataa       )
					pipe.to(UpperCamelCase       )
					pipe.set_progress_bar_config(disable=UpperCamelCase       )

					snake_case__      :Optional[Any]         =   "A painting of a squirrel eating a burger "
					snake_case__      :Optional[Any]         =   torch.manual_seed(0       )
					snake_case__      :Any         =   pipe(
					    prompt=UpperCamelCase					,generator=UpperCamelCase					,guidance_scale=7.5					,num_inference_steps=50					,output_type="numpy"       ).images

					snake_case__      :Any         =   image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					snake_case__      :int         =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]       )

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







def 					lowercase_ (    __snake_case     :							list      )     -> list:





				'''simple docstring'''


				if any(not isinstance(__snake_case	,	__snake_case      ) or x < 0 for x in sequence      ):
								raise TypeError("Sequence must be list of non-negative integers"      )
				for _ in range(len(__snake_case      )      ):
								for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case	,	sequence[1:]      )      ):
												if rod_upper > rod_lower:
																sequence[i] -= rod_upper - rod_lower
																sequence[i + 1] += rod_upper - rod_lower
				return sequence


if __name__ == "__main__":
	assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]