






'''simple docstring'''




import argparse
import copy







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		UpperCamelCase__				:					List[str]					=					{}

		with open(lowerCamelCase_) as f:
				for line in f:
						if line.split()[0] not in dict_of_neighbours:
								UpperCamelCase__				:					Any					=					[]
								_list.append([line.split()[1], line.split()[2]])
								UpperCamelCase__				:					int					=					_list
						else:
								dict_of_neighbours[line.split()[0]].append(
								    [line.split()[1], line.split()[2]])
						if line.split()[1] not in dict_of_neighbours:
								UpperCamelCase__				:					Union[str, Any]					=					[]
								_list.append([line.split()[0], line.split()[2]])
								UpperCamelCase__				:					Optional[Any]					=					_list
						else:
								dict_of_neighbours[line.split()[1]].append(
								    [line.split()[0], line.split()[2]])

		return dict_of_neighbours







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     List[str]:

		with open(lowerCamelCase_) as f:
				UpperCamelCase__				:					str					=					f.read(1)
		UpperCamelCase__				:					Dict					=					start_node

		UpperCamelCase__				:					Optional[int]					=					[]

		UpperCamelCase__				:					Tuple					=					start_node

		UpperCamelCase__				:					Optional[Any]					=					0
		while visiting not in first_solution:
				UpperCamelCase__				:					Dict					=					10_000
				for k in dict_of_neighbours[visiting]:
						if int(k[1]) < int(lowerCamelCase_) and k[0] not in first_solution:
								UpperCamelCase__				:					Optional[int]					=					k[1]
								UpperCamelCase__				:					Optional[Any]					=					k[0]

				first_solution.append(lowerCamelCase_)
				UpperCamelCase__				:					Tuple					=					distance_of_first_solution + int(lowerCamelCase_)
				UpperCamelCase__				:					Tuple					=					best_node

		first_solution.append(lowerCamelCase_)

		UpperCamelCase__				:					List[Any]					=					0
		for k in dict_of_neighbours[first_solution[-2]]:
				if k[0] == start_node:
						break
				position += 1

		UpperCamelCase__				:					int					=					(
		    distance_of_first_solution
		    + int(dict_of_neighbours[first_solution[-2]][position][1])
		    - 10_000
		)
		return first_solution, distance_of_first_solution







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Dict:
		UpperCamelCase__				:					Optional[Any]					=					[]

		for n in solution[1:-1]:
				UpperCamelCase__				:					int					=					solution.index(lowerCamelCase_)
				for kn in solution[1:-1]:
						UpperCamelCase__				:					List[Any]					=					solution.index(lowerCamelCase_)
						if n == kn:
								continue

						UpperCamelCase__				:					Optional[int]					=					copy.deepcopy(lowerCamelCase_)
						UpperCamelCase__				:					Dict					=					kn
						UpperCamelCase__				:					Tuple					=					n

						UpperCamelCase__				:					int					=					0

						for k in _tmp[:-1]:
								UpperCamelCase__				:					Union[str, Any]					=					_tmp[_tmp.index(lowerCamelCase_) + 1]
								for i in dict_of_neighbours[k]:
										if i[0] == next_node:
												UpperCamelCase__				:					Optional[int]					=					distance + int(i[1])
						_tmp.append(lowerCamelCase_)

						if _tmp not in neighborhood_of_solution:
								neighborhood_of_solution.append(_tmp)

		UpperCamelCase__				:					Dict					=					len(neighborhood_of_solution[0]) - 1

		neighborhood_of_solution.sort(key=lambda lowerCamelCase_: x[index_of_last_item_in_the_list])
		return neighborhood_of_solution







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					Optional[Any]					=					1
		UpperCamelCase__				:					Tuple					=					first_solution
		UpperCamelCase__				:					Dict					=					[]
		UpperCamelCase__				:					int					=					distance_of_first_solution
		UpperCamelCase__				:					List[str]					=					solution

		while count <= iters:
				UpperCamelCase__				:					List[str]					=					find_neighborhood(lowerCamelCase_     ,      lowerCamelCase_)
				UpperCamelCase__				:					Tuple					=					0
				UpperCamelCase__				:					Any					=					neighborhood[index_of_best_solution]
				UpperCamelCase__				:					Any					=					len(lowerCamelCase_) - 1

				UpperCamelCase__				:					int					=					False
				while not found:
						UpperCamelCase__				:					Any					=					0
						while i < len(lowerCamelCase_):
								if best_solution[i] != solution[i]:
										UpperCamelCase__				:					Optional[Any]					=					best_solution[i]
										UpperCamelCase__				:					Union[str, Any]					=					solution[i]
										break
								UpperCamelCase__				:					List[Any]					=					i + 1

						if [first_exchange_node, second_exchange_node] not in tabu_list and [
						    second_exchange_node,
						    first_exchange_node,
						] not in tabu_list:
								tabu_list.append([first_exchange_node, second_exchange_node])
								UpperCamelCase__				:					Union[str, Any]					=					True
								UpperCamelCase__				:					Optional[int]					=					best_solution[:-1]
								UpperCamelCase__				:					List[Any]					=					neighborhood[index_of_best_solution][best_cost_index]
								if cost < best_cost:
										UpperCamelCase__				:					Optional[Any]					=					cost
										UpperCamelCase__				:					Any					=					solution
						else:
								UpperCamelCase__				:					List[str]					=					index_of_best_solution + 1
								UpperCamelCase__				:					List[str]					=					neighborhood[index_of_best_solution]

				if len(lowerCamelCase_) >= size:
						tabu_list.pop(0)

				UpperCamelCase__				:					List[str]					=					count + 1

		return best_solution_ever, best_cost







def       __UpperCAmelCase						(  lowerCamelCase_=None)							->     Dict:
		UpperCamelCase__				:					List[str]					=					generate_neighbours(args.File)

		UpperCamelCase__,	UpperCamelCase__				:					Optional[Any]					=					generate_first_solution(
		    args.File     ,      lowerCamelCase_)

		UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					tabu_search(
		    lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      args.Iterations     ,      args.Size     ,      )

		print(f'Best solution: {best_sol}, with total distance: {best_cost}.')


if __name__ == "__main__":
		lowerCAmelCase__          =						argparse.ArgumentParser(description='Tabu Search')
		parser.add_argument(
		    '-f',
		    '--File',
		    type=str,
		    help='Path to the file containing the data',
		    required=True,
		)
		parser.add_argument(
		    '-i',
		    '--Iterations',
		    type=int,
		    help='How many iterations the algorithm should perform',
		    required=True,
		)
		parser.add_argument(
		    '-s', '--Size', type=int, help='Size of the tabu list', required=True
		)

		# Pass the arguments to main method
		main(parser.parse_args())












'''simple docstring'''




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu







class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						List[str]):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Union[str, Any]					=					1
			UpperCamelCase__				:					Union[str, Any]					=					3
			UpperCamelCase__				:					Dict					=					(32, 32)

			UpperCamelCase__				:					int					=					floats_tensor((batch_size, num_channels) + sizes  ,					rng=random.Random(0)).to(UpperCAmelCase_)
			return image

	@property
	def      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					UNetaDConditionModel(
			    block_out_channels=(32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=4  ,					out_channels=4  ,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,					cross_attention_dim=32  ,					)
			return model

	@property
	def      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					AutoencoderKL(
			    block_out_channels=[32, 64]  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,					latent_channels=4  ,					)
			return model

	@property
	def      __UpperCamelCase							(      self   :						str):
			torch.manual_seed(0)
			UpperCamelCase__				:					Tuple					=					CLIPTextConfig(
			    bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=32  ,					intermediate_size=37  ,					layer_norm_eps=1e-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=1_000  ,					)
			return CLIPTextModel(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			def extract(*UpperCAmelCase_   :						List[Any]  ,					**UpperCAmelCase_   :						Dict):







					class       __lowercase					:

						def __init__(      self   :						List[Any]):
								UpperCamelCase__				:					Optional[Any]					=					torch.ones([0])





						def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						int):
								self.pixel_values.to(UpperCAmelCase_)
								return self


					return Out()

			return extract

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Optional[Any]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
			UpperCamelCase__				:					Any					=					self.dummy_cond_unet
			UpperCamelCase__				:					Any					=					DDIMScheduler(
			    beta_start=0.0_00_85  ,					beta_end=0.0_12  ,					beta_schedule='scaled_linear'  ,					clip_sample=UpperCAmelCase_  ,					set_alpha_to_one=UpperCAmelCase_  ,					)

			UpperCamelCase__				:					List[str]					=					self.dummy_vae
			UpperCamelCase__				:					str					=					self.dummy_text_encoder
			UpperCamelCase__				:					Tuple					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

			# make sure here that pndm scheduler skips prk
			UpperCamelCase__				:					Optional[Any]					=					StableDiffusionPipeline(
			    unet=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					vae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					safety_checker=UpperCAmelCase_  ,					feature_extractor=self.dummy_extractor  ,					)
			UpperCamelCase__				:					Optional[Any]					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					'A painting of a squirrel eating a burger'

			UpperCamelCase__				:					Dict					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					sd_pipe([prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=6.0  ,					num_inference_steps=2  ,					output_type='np')
			UpperCamelCase__				:					Tuple					=					output.images

			UpperCamelCase__				:					List[Any]					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Tuple					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=6.0  ,					num_inference_steps=2  ,					output_type='np'  ,					return_dict=UpperCAmelCase_  ,					)[0]

			UpperCamelCase__				:					List[str]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Optional[int]					=					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			UpperCamelCase__				:					List[Any]					=					np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64])

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

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					List[Any]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
			UpperCamelCase__				:					int					=					self.dummy_cond_unet
			UpperCamelCase__				:					Dict					=					PNDMScheduler(skip_prk_steps=UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					self.dummy_vae
			UpperCamelCase__				:					Optional[int]					=					self.dummy_text_encoder
			UpperCamelCase__				:					Union[str, Any]					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

			# make sure here that pndm scheduler skips prk
			UpperCamelCase__				:					Dict					=					StableDiffusionPipeline(
			    unet=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					vae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					safety_checker=UpperCAmelCase_  ,					feature_extractor=self.dummy_extractor  ,					)
			UpperCamelCase__				:					Tuple					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					'A painting of a squirrel eating a burger'
			UpperCamelCase__				:					Union[str, Any]					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					str					=					sd_pipe([prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=6.0  ,					num_inference_steps=2  ,					output_type='np')

			UpperCamelCase__				:					List[str]					=					output.images

			UpperCamelCase__				:					Any					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=6.0  ,					num_inference_steps=2  ,					output_type='np'  ,					return_dict=UpperCAmelCase_  ,					)[0]

			UpperCamelCase__				:					Tuple					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			UpperCamelCase__				:					List[Any]					=					np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93])

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

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Dict					=					StableDiffusionPipeline.from_pretrained(
			    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'  ,					safety_checker=UpperCAmelCase_)
			assert isinstance(UpperCAmelCase_  ,					UpperCAmelCase_)
			assert isinstance(pipe.scheduler  ,					UpperCAmelCase_)
			assert pipe.safety_checker is None

			UpperCamelCase__				:					List[Any]					=					pipe('example prompt'  ,					num_inference_steps=2).images[0]
			assert image is not None

			# check that there's no error when saving a pipeline with one of the models being None
			with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					List[str]					=					StableDiffusionPipeline.from_pretrained(UpperCAmelCase_)

			# sanity check that the pipeline still works
			assert pipe.safety_checker is None
			UpperCamelCase__				:					Optional[Any]					=					pipe('example prompt'  ,					num_inference_steps=2).images[0]
			assert image is not None





	@unittest.skipIf(torch_device != 'cuda'  ,					'This test requires a GPU')
	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Dict					=					self.dummy_cond_unet
			UpperCamelCase__				:					str					=					PNDMScheduler(skip_prk_steps=UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					self.dummy_vae
			UpperCamelCase__				:					Optional[Any]					=					self.dummy_text_encoder
			UpperCamelCase__				:					str					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

			# put models in fp16
			UpperCamelCase__				:					Any					=					unet.half()
			UpperCamelCase__				:					Tuple					=					vae.half()
			UpperCamelCase__				:					Optional[int]					=					bert.half()

			# make sure here that pndm scheduler skips prk
			UpperCamelCase__				:					Optional[int]					=					StableDiffusionPipeline(
			    unet=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					vae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					safety_checker=UpperCAmelCase_  ,					feature_extractor=self.dummy_extractor  ,					)
			UpperCamelCase__				:					Dict					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					'A painting of a squirrel eating a burger'
			UpperCamelCase__				:					int					=					sd_pipe([prompt]  ,					num_inference_steps=2  ,					output_type='np').images

			assert image.shape == (1, 64, 64, 3)







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

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

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Optional[int]					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'  ,					safety_checker=UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
			UpperCamelCase__				:					Optional[Any]					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					(
			    'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
			    ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
			    ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
			    ' children from bahnhof zoo, detailed '
			)
			UpperCamelCase__				:					Any					=					4_003_660_346
			UpperCamelCase__				:					Any					=					7

			# without safety guidance (sld_guidance_scale = 0)
			UpperCamelCase__				:					int					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=0  ,					)

			UpperCamelCase__				:					str					=					output.images
			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Tuple					=					[0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

			assert image.shape == (1, 512, 512, 3)

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

			# without safety guidance (strong configuration)
			UpperCamelCase__				:					Tuple					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					str					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Dict					=					output.images
			UpperCamelCase__				:					str					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Tuple					=					[0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

			assert image.shape == (1, 512, 512, 3)

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

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					Dict					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'  ,					safety_checker=UpperCAmelCase_)
			UpperCamelCase__				:					str					=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
			UpperCamelCase__				:					Dict					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					str					=					'padme amidala taking a bath artwork, safe for work, no nudity'
			UpperCamelCase__				:					Tuple					=					2_734_971_755
			UpperCamelCase__				:					Tuple					=					7

			UpperCamelCase__				:					Tuple					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					int					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=0  ,					)

			UpperCamelCase__				:					int					=					output.images
			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Any					=					[0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

			assert image.shape == (1, 512, 512, 3)

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

			UpperCamelCase__				:					List[str]					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Tuple					=					output.images
			UpperCamelCase__				:					List[str]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Union[str, Any]					=					[0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

			assert image.shape == (1, 512, 512, 3)

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





	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Optional[Any]					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5')
			UpperCamelCase__				:					Optional[Any]					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					(
			    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
			    ' leyendecker'
			)
			UpperCamelCase__				:					Any					=					1_044_355_234
			UpperCamelCase__				:					Optional[int]					=					12

			UpperCamelCase__				:					Optional[int]					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					str					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=0  ,					)

			UpperCamelCase__				:					List[str]					=					output.images
			UpperCamelCase__				:					Any					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					str					=					np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

			assert image.shape == (1, 512, 512, 3)

			assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-7

			UpperCamelCase__				:					int					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					List[str]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Optional[Any]					=					output.images
			UpperCamelCase__				:					List[Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					str					=					np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61])
			assert image.shape == (1, 512, 512, 3)

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




