






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					@property
					def  _lowercase  (      self  :						str ):
						torch.manual_seed(0 )
						snake_case__				:   Dict     =						UNetaDModel(
						    block_out_channels=(3_2, 6_4)							,	layers_per_block=2							,	sample_size=3_2							,	in_channels=3							,	out_channels=3							,	down_block_types=("DownBlock2D", "AttnDownBlock2D")							,	up_block_types=("AttnUpBlock2D", "UpBlock2D")							,	)
						return model






					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Union[str, Any]     =						self.dummy_uncond_unet
						snake_case__				:   Any     =						KarrasVeScheduler()

						snake_case__				:   int     =						KarrasVePipeline(unet=__A							,	scheduler=__A )
						pipe.to(__A )
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   List[Any]     =						torch.manual_seed(0 )
						snake_case__				:   str     =						pipe(num_inference_steps=2							,	generator=__A							,	output_type="numpy" ).images

						snake_case__				:   Union[str, Any]     =						torch.manual_seed(0 )
						snake_case__				:   int     =						pipe(num_inference_steps=2							,	generator=__A							,	output_type="numpy"							,	return_dict=__A )[0]

						snake_case__				:   Dict     =						image[0, -3:, -3:, -1]
						snake_case__				:   str     =						image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__				:   Tuple     =						np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )

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



@slow
@require_torch
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   int     =						"google/ncsnpp-celebahq-256"
						snake_case__				:   List[str]     =						UNetaDModel.from_pretrained(__A )
						snake_case__				:   int     =						KarrasVeScheduler()

						snake_case__				:   Any     =						KarrasVePipeline(unet=__A							,	scheduler=__A )
						pipe.to(__A )
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   int     =						torch.manual_seed(0 )
						snake_case__				:   Tuple     =						pipe(num_inference_steps=2_0							,	generator=__A							,	output_type="numpy" ).images

						snake_case__				:   Optional[int]     =						image[0, -3:, -3:, -1]
						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						snake_case__				:   Any     =						np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )

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











def   SCREAMING_SNAKE_CASE       (				snake_case_      :							list     ):
	if len(snake_case_     ) <= 1:
		return lst

	snake_case__				:   List[Any]     =						1

	while i < len(snake_case_     ):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			snake_case__,		snake_case__				:   Tuple     =						lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				snake_case__				:   Union[str, Any]     =						1

	return lst


if __name__ == "__main__":
	__lowerCamelCase      :    Dict   		=   input("""Enter numbers separated by a comma:\n""").strip()
	__lowerCamelCase      :    Tuple   		=   [int(item) for item in user_input.split(""",""")]
	print(gnome_sort(unsorted))



