"""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.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 							__snake_case(					unittest.TestCase	):


    '''simple docstring'''





    def 						_a							(  self	):




         '''simple docstring'''



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		1
         __A					:					Any								=		3
         __A					:					Any								=		(32, 32)

         __A					:					int								=		floats_tensor((batch_size, num_channels) + sizes					,       rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE_	)
         return image
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         torch.manual_seed(0	)
         __A					:					int								=		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 						_a							(  self	):




         '''simple docstring'''



         torch.manual_seed(0	)
         __A					:					int								=		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 						_a							(  self	):




         '''simple docstring'''



         torch.manual_seed(0	)
         __A					:					str								=		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=1000					,       )
         return CLIPTextModel(SCREAMING_SNAKE_CASE_	)
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         def extract(*__lowerCamelCase					,       **__lowerCamelCase	):






              class 							__snake_case:


                  '''simple docstring'''





                  def __init__(  self	):




                       '''simple docstring'''



                       __A					:					List[str]								=		torch.ones([0]	)

                  def 						_a							(  self					,       __lowerCamelCase	):




                       '''simple docstring'''



                       self.pixel_values.to(SCREAMING_SNAKE_CASE_	)
                       return self







              return Out()

         return extract
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __A					:					Union[str, Any]								=		self.dummy_cond_unet
         __A					:					List[Any]								=		DDIMScheduler(
             beta_start=0.0_00_85					,       beta_end=0.0_12					,       beta_schedule='scaled_linear'					,       clip_sample=SCREAMING_SNAKE_CASE_					,       set_alpha_to_one=SCREAMING_SNAKE_CASE_					,       )

         __A					:					Union[str, Any]								=		self.dummy_vae
         __A					:					Union[str, Any]								=		self.dummy_text_encoder
         __A					:					Union[str, Any]								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

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

         __A					:					Union[str, Any]								=		'''A painting of a squirrel eating a burger'''

         __A					:					int								=		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(0	)
         __A					:					List[Any]								=		sd_pipe([prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=6.0					,       num_inference_steps=2					,       output_type='np'	)
         __A					:					List[str]								=		output.images

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

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

         assert image.shape == (1, 64, 64, 3)
         __A					:					int								=		np.array([0.57_53, 0.61_14, 0.50_01, 0.50_34, 0.54_70, 0.47_29, 0.49_71, 0.48_67, 0.48_67]	)

         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 						_a							(  self	):




         '''simple docstring'''



         __A					:					str								=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __A					:					Optional[Any]								=		self.dummy_cond_unet
         __A					:					Optional[Any]								=		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_	)
         __A					:					Optional[Any]								=		self.dummy_vae
         __A					:					Any								=		self.dummy_text_encoder
         __A					:					str								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

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

         __A					:					Dict								=		'''A painting of a squirrel eating a burger'''
         __A					:					int								=		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(0	)
         __A					:					Optional[int]								=		sd_pipe([prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=6.0					,       num_inference_steps=2					,       output_type='np'	)

         __A					:					int								=		output.images

         __A					:					Dict								=		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(0	)
         __A					:					Optional[int]								=		sd_pipe(
             [prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=6.0					,       num_inference_steps=2					,       output_type='np'					,       return_dict=SCREAMING_SNAKE_CASE_					,       )[0]

         __A					:					str								=		image[0, -3:, -3:, -1]
         __A					:					int								=		image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         __A					:					Optional[int]								=		np.array([0.51_22, 0.57_12, 0.48_25, 0.50_53, 0.56_46, 0.47_69, 0.51_79, 0.48_94, 0.49_94]	)

         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 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		StableDiffusionPipeline.from_pretrained(
             'hf-internal-testing/tiny-stable-diffusion-lms-pipe'					,       safety_checker=SCREAMING_SNAKE_CASE_	)
         assert isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_	)
         assert isinstance(pipe.scheduler					,       SCREAMING_SNAKE_CASE_	)
         assert pipe.safety_checker is None

         __A					:					Optional[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(SCREAMING_SNAKE_CASE_	)
              __A					:					int								=		StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_	)

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         __A					:					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 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		self.dummy_cond_unet
         __A					:					int								=		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_	)
         __A					:					Dict								=		self.dummy_vae
         __A					:					Optional[Any]								=		self.dummy_text_encoder
         __A					:					Union[str, Any]								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         # put models in fp16
         __A					:					int								=		unet.half()
         __A					:					Any								=		vae.half()
         __A					:					int								=		bert.half()

         # make sure here that pndm scheduler skips prk
         __A					:					Union[str, Any]								=		StableDiffusionPipeline(
             unet=SCREAMING_SNAKE_CASE_					,       scheduler=SCREAMING_SNAKE_CASE_					,       vae=SCREAMING_SNAKE_CASE_					,       text_encoder=SCREAMING_SNAKE_CASE_					,       tokenizer=SCREAMING_SNAKE_CASE_					,       safety_checker=SCREAMING_SNAKE_CASE_					,       feature_extractor=self.dummy_extractor					,       )
         __A					:					Tuple								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

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







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


    '''simple docstring'''





    def 						_a							(  self	):




         '''simple docstring'''



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		'''cuda'''
         __A					:					str								=		StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
         __A					:					Optional[int]								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __A					:					Union[str, Any]								=		'''a photo of a cat'''
         __A					:					int								=		{
             '''editing_prompt''': ['''sunglasses'''],
             '''reverse_editing_direction''': [False],
             '''edit_warmup_steps''': 10,
             '''edit_guidance_scale''': 6,
             '''edit_threshold''': 0.95,
             '''edit_momentum_scale''': 0.5,
             '''edit_mom_beta''': 0.6,
         }

         __A					:					Optional[Any]								=		3
         __A					:					Any								=		7

         # no sega enabled
         __A					:					str								=		torch.Generator(SCREAMING_SNAKE_CASE_	)
         generator.manual_seed(SCREAMING_SNAKE_CASE_	)
         __A					:					Optional[Any]								=		pipe(
             [prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=SCREAMING_SNAKE_CASE_					,       num_inference_steps=50					,       output_type='np'					,       width=512					,       height=512					,       )

         __A					:					Tuple								=		output.images
         __A					:					Optional[Any]								=		image[0, -3:, -3:, -1]
         __A					:					Union[str, Any]								=		[
             0.34_67_31_13,
             0.38_49_27_33,
             0.37_59_73_52,
             0.34_08_63_35,
             0.35_65_07_48,
             0.35_57_92_05,
             0.3_38_47_63,
             0.34_34_02_36,
             0.3_57_32_71,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(SCREAMING_SNAKE_CASE_	)
         __A					:					Tuple								=		pipe(
             [prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=SCREAMING_SNAKE_CASE_					,       num_inference_steps=50					,       output_type='np'					,       width=512					,       height=512					,       **SCREAMING_SNAKE_CASE_					,       )

         __A					:					int								=		output.images
         __A					:					int								=		image[0, -3:, -3:, -1]
         __A					:					Union[str, Any]								=		[
             0.41_88_78_26,
             0.37_72_87_66,
             0.30_13_82_72,
             0.41_41_63_35,
             0.41_66_49_85,
             0.36_28_33_92,
             0.36_19_12_46,
             0.43_36_44_65,
             0.43_00_17_32,
         ]

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

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		'''cuda'''
         __A					:					Optional[Any]								=		StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
         __A					:					Optional[int]								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __A					:					int								=		'''an image of a crowded boulevard, realistic, 4k'''
         __A					:					Tuple								=		{
             '''editing_prompt''': '''crowd, crowded, people''',
             '''reverse_editing_direction''': True,
             '''edit_warmup_steps''': 10,
             '''edit_guidance_scale''': 8.3,
             '''edit_threshold''': 0.9,
             '''edit_momentum_scale''': 0.5,
             '''edit_mom_beta''': 0.6,
         }

         __A					:					Optional[Any]								=		9
         __A					:					Union[str, Any]								=		7

         # no sega enabled
         __A					:					Any								=		torch.Generator(SCREAMING_SNAKE_CASE_	)
         generator.manual_seed(SCREAMING_SNAKE_CASE_	)
         __A					:					Optional[int]								=		pipe(
             [prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=SCREAMING_SNAKE_CASE_					,       num_inference_steps=50					,       output_type='np'					,       width=512					,       height=512					,       )

         __A					:					int								=		output.images
         __A					:					Union[str, Any]								=		image[0, -3:, -3:, -1]
         __A					:					Union[str, Any]								=		[
             0.43_49_79_98,
             0.91_81_40_65,
             0.7_54_07_39,
             0.55_58_02_05,
             0.8_46_72_65,
             0.5_38_96_91,
             0.62_57_45_06,
             0.58_89_77_63,
             0.50_92_67_57,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(SCREAMING_SNAKE_CASE_	)
         __A					:					Union[str, Any]								=		pipe(
             [prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=SCREAMING_SNAKE_CASE_					,       num_inference_steps=50					,       output_type='np'					,       width=512					,       height=512					,       **SCREAMING_SNAKE_CASE_					,       )

         __A					:					int								=		output.images
         __A					:					int								=		image[0, -3:, -3:, -1]
         __A					:					Any								=		[
             0.3_08_97_19,
             0.30_50_01_44,
             0.29_01_60_42,
             0.30_63_09_64,
             0.32_56_87,
             0.29_41_92_25,
             0.2_90_80_91,
             0.28_72_35_98,
             0.27_69_62_94,
         ]

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

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[int]								=		'''cuda'''
         __A					:					Union[str, Any]								=		StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
         __A					:					Tuple								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __A					:					List[str]								=		'''a castle next to a river'''
         __A					:					Dict								=		{
             '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
             '''reverse_editing_direction''': False,
             '''edit_warmup_steps''': [15, 18],
             '''edit_guidance_scale''': 6,
             '''edit_threshold''': [0.9, 0.8],
             '''edit_momentum_scale''': 0.5,
             '''edit_mom_beta''': 0.6,
         }

         __A					:					Dict								=		48
         __A					:					int								=		7

         # no sega enabled
         __A					:					Optional[int]								=		torch.Generator(SCREAMING_SNAKE_CASE_	)
         generator.manual_seed(SCREAMING_SNAKE_CASE_	)
         __A					:					List[str]								=		pipe(
             [prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=SCREAMING_SNAKE_CASE_					,       num_inference_steps=50					,       output_type='np'					,       width=512					,       height=512					,       )

         __A					:					str								=		output.images
         __A					:					int								=		image[0, -3:, -3:, -1]
         __A					:					List[str]								=		[
             0.75_16_35_55,
             0.76_03_71_45,
             0.6_17_85,
             0.9_18_96_73,
             0.8_62_77_01,
             0.85_18_96_94,
             0.8_51_28_13,
             0.87_01_20_76,
             0.8_31_28_57,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(SCREAMING_SNAKE_CASE_	)
         __A					:					List[str]								=		pipe(
             [prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=SCREAMING_SNAKE_CASE_					,       num_inference_steps=50					,       output_type='np'					,       width=512					,       height=512					,       **SCREAMING_SNAKE_CASE_					,       )

         __A					:					Union[str, Any]								=		output.images
         __A					:					Any								=		image[0, -3:, -3:, -1]
         __A					:					List[str]								=		[
             0.73_55_33_65,
             0.7_53_72_71,
             0.74_34_19_05,
             0.66_48_03_56,
             0.6_47_29_25,
             0.63_03_94_16,
             0.64_81_29_05,
             0.6_74_97_17,
             0.6_51_71_02,
         ]

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

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

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Any								=		'''cuda'''
         __A					:					Any								=		StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'					,       torch_dtype=torch.floataa	)
         __A					:					Dict								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __A					:					List[Any]								=		'''a photo of a cat'''
         __A					:					Dict								=		{
             '''editing_prompt''': ['''sunglasses'''],
             '''reverse_editing_direction''': [False],
             '''edit_warmup_steps''': 10,
             '''edit_guidance_scale''': 6,
             '''edit_threshold''': 0.95,
             '''edit_momentum_scale''': 0.5,
             '''edit_mom_beta''': 0.6,
         }

         __A					:					Dict								=		3
         __A					:					Optional[int]								=		7

         # no sega enabled
         __A					:					Dict								=		torch.Generator(SCREAMING_SNAKE_CASE_	)
         generator.manual_seed(SCREAMING_SNAKE_CASE_	)
         __A					:					List[str]								=		pipe(
             [prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=SCREAMING_SNAKE_CASE_					,       num_inference_steps=50					,       output_type='np'					,       width=512					,       height=512					,       )

         __A					:					int								=		output.images
         __A					:					int								=		image[0, -3:, -3:, -1]
         __A					:					List[str]								=		[
             0.34_88_76_95,
             0.3_87_69_53,
             0.3_75,
             0.34_42_38_28,
             0.3_58_15_43,
             0.35_71_77_73,
             0.3_38_37_89,
             0.34_57_03_12,
             0.35_93_75,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(SCREAMING_SNAKE_CASE_	)
         __A					:					Tuple								=		pipe(
             [prompt]					,       generator=SCREAMING_SNAKE_CASE_					,       guidance_scale=SCREAMING_SNAKE_CASE_					,       num_inference_steps=50					,       output_type='np'					,       width=512					,       height=512					,       **SCREAMING_SNAKE_CASE_					,       )

         __A					:					Union[str, Any]								=		output.images
         __A					:					Tuple								=		image[0, -3:, -3:, -1]
         __A					:					Tuple								=		[
             0.42_28_51_56,
             0.36_91_40_62,
             0.29_07_71_48,
             0.42_04_10_16,
             0.41_91_89_45,
             0.35_49_80_47,
             0.3_61_81_64,
             0.4_42_38_28,
             0.43_11_52_34,
         ]

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

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









"""simple docstring"""





_lowercase						   =     {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

_lowercase						   =     {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Any				      =       True
						snake_case__							:       int				      =       []

						for neighbour in graph[vert]:
												if not visited[neighbour]:
																		connected_verts += dfs(lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		)

						return [vert, *connected_verts]

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Optional[int]				      =       len(lowerCamelCase		)
						snake_case__							:       str				      =       graph_size * [False]
						snake_case__							:       List[str]				      =       []

						for i in range(lowerCamelCase		):
												if not visited[i]:
																		snake_case__							:       Dict				      =       dfs(lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		)
																		components_list.append(lowerCamelCase		)

						return components_list


if __name__ == "__main__":
							import doctest

							doctest.testmod()


