




"""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    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Tuple   ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				@property
				def    _lowerCamelCase       ( self :  List[Any]   ):
										snake_case__							:       int				      =       1
										snake_case__							:       Any				      =       3
										snake_case__							:       Any				      =       (3_2, 3_2)

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

				@property
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										torch.manual_seed(0   )
										snake_case__							:       int				      =       UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)   ,layers_per_block=2   ,sample_size=3_2   ,in_channels=4   ,out_channels=4   ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,cross_attention_dim=3_2   ,)
										return model

				@property
				def    _lowerCamelCase       ( self :  List[str]   ):
										torch.manual_seed(0   )
										snake_case__							:       int				      =       AutoencoderKL(
										    block_out_channels=[3_2, 6_4]   ,in_channels=3   ,out_channels=3   ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,latent_channels=4   ,)
										return model

				@property
				def    _lowerCamelCase       ( self :  Optional[int]   ):
										torch.manual_seed(0   )
										snake_case__							:       str				      =       CLIPTextConfig(
										    bos_token_id=0   ,eos_token_id=2   ,hidden_size=3_2   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,)
										return CLIPTextModel(SCREAMING_SNAKE_CASE_   )

				@property
				def    _lowerCamelCase       ( self :  Optional[int]   ):
										def extract(*SCREAMING_SNAKE_CASE_ :  Optional[int]   ,**SCREAMING_SNAKE_CASE_ :  Optional[Any]   ):

																class    __lowercase							:


																				'''simple docstring'''


																				def __init__( self :  Optional[int]   ):
																										snake_case__							:       List[str]				      =       torch.ones([0]   )





																				def    _lowerCamelCase       ( self :  Optional[int]   ,SCREAMING_SNAKE_CASE_ :  str   ):
																										self.pixel_values.to(SCREAMING_SNAKE_CASE_   )
																										return self







																return Out()

										return extract

				def    _lowerCamelCase       ( self :  Dict   ):
										snake_case__							:       Tuple				      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case__							:       Union[str, Any]				      =       self.dummy_cond_unet
										snake_case__							:       List[Any]				      =       DDIMScheduler(
										    beta_start=0.00085   ,beta_end=0.012   ,beta_schedule='''scaled_linear'''   ,clip_sample=SCREAMING_SNAKE_CASE_   ,set_alpha_to_one=SCREAMING_SNAKE_CASE_   ,)

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

										# make sure here that pndm scheduler skips prk
										snake_case__							:       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   ,)
										snake_case__							:       Any				      =       sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

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

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

										snake_case__							:       Any				      =       torch.Generator(device=SCREAMING_SNAKE_CASE_   ).manual_seed(0   )
										snake_case__							:       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]

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

										assert image.shape == (1, 6_4, 6_4, 3)
										snake_case__							:       int				      =       np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]   )

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

										# make sure here that pndm scheduler skips prk
										snake_case__							:       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   ,)
										snake_case__							:       Any				      =       sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

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

										snake_case__							:       int				      =       output.images

										snake_case__							:       Dict				      =       torch.Generator(device=SCREAMING_SNAKE_CASE_   ).manual_seed(0   )
										snake_case__							:       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]

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

										assert image.shape == (1, 6_4, 6_4, 3)
										snake_case__							:       Optional[int]				      =       np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]   )

										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    _lowerCamelCase       ( self :  List[str]   ):
										snake_case__							:       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

										snake_case__							:       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_   )
																snake_case__							:       int				      =       StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_   )

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										snake_case__							:       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    _lowerCamelCase       ( self :  List[Any]   ):
										snake_case__							:       Optional[Any]				      =       self.dummy_cond_unet
										snake_case__							:       int				      =       PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Dict				      =       self.dummy_vae
										snake_case__							:       Optional[Any]				      =       self.dummy_text_encoder
										snake_case__							:       Union[str, Any]				      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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

										# make sure here that pndm scheduler skips prk
										snake_case__							:       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   ,)
										snake_case__							:       Tuple				      =       sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

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

										assert image.shape == (1, 6_4, 6_4, 3)







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


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Any   ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

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

										snake_case__							:       Union[str, Any]				      =       '''a photo of a cat'''
										snake_case__							:       int				      =       {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

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

										# no sega enabled
										snake_case__							:       str				      =       torch.Generator(SCREAMING_SNAKE_CASE_   )
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Optional[Any]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,)

										snake_case__							:       Tuple				      =       output.images
										snake_case__							:       Optional[Any]				      =       image[0, -3:, -3:, -1]
										snake_case__							:       Union[str, Any]				      =       [
										    0.34673113,
										    0.38492733,
										    0.37597352,
										    0.34086335,
										    0.35650748,
										    0.35579205,
										    0.3384763,
										    0.34340236,
										    0.3573271,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 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_   )
										snake_case__							:       Tuple				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,**SCREAMING_SNAKE_CASE_   ,)

										snake_case__							:       int				      =       output.images
										snake_case__							:       int				      =       image[0, -3:, -3:, -1]
										snake_case__							:       Union[str, Any]				      =       [
										    0.41887826,
										    0.37728766,
										    0.30138272,
										    0.41416335,
										    0.41664985,
										    0.36283392,
										    0.36191246,
										    0.43364465,
										    0.43001732,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

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

										snake_case__							:       int				      =       '''an image of a crowded boulevard, realistic, 4k'''
										snake_case__							:       Tuple				      =       {
										    '''editing_prompt''': '''crowd, crowded, people''',
										    '''reverse_editing_direction''': True,
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 8.3,
										    '''edit_threshold''': 0.9,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

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

										# no sega enabled
										snake_case__							:       Any				      =       torch.Generator(SCREAMING_SNAKE_CASE_   )
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Optional[int]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,)

										snake_case__							:       int				      =       output.images
										snake_case__							:       Union[str, Any]				      =       image[0, -3:, -3:, -1]
										snake_case__							:       Union[str, Any]				      =       [
										    0.43497998,
										    0.91814065,
										    0.7540739,
										    0.55580205,
										    0.8467265,
										    0.5389691,
										    0.62574506,
										    0.58897763,
										    0.50926757,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 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_   )
										snake_case__							:       Union[str, Any]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,**SCREAMING_SNAKE_CASE_   ,)

										snake_case__							:       int				      =       output.images
										snake_case__							:       int				      =       image[0, -3:, -3:, -1]
										snake_case__							:       Any				      =       [
										    0.3089719,
										    0.30500144,
										    0.29016042,
										    0.30630964,
										    0.325687,
										    0.29419225,
										    0.2908091,
										    0.28723598,
										    0.27696294,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

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

										snake_case__							:       List[str]				      =       '''a castle next to a river'''
										snake_case__							:       Dict				      =       {
										    '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
										    '''reverse_editing_direction''': False,
										    '''edit_warmup_steps''': [1_5, 1_8],
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': [0.9, 0.8],
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										snake_case__							:       Dict				      =       4_8
										snake_case__							:       int				      =       7

										# no sega enabled
										snake_case__							:       Optional[int]				      =       torch.Generator(SCREAMING_SNAKE_CASE_   )
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[str]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,)

										snake_case__							:       str				      =       output.images
										snake_case__							:       int				      =       image[0, -3:, -3:, -1]
										snake_case__							:       List[str]				      =       [
										    0.75163555,
										    0.76037145,
										    0.61785,
										    0.9189673,
										    0.8627701,
										    0.85189694,
										    0.8512813,
										    0.87012076,
										    0.8312857,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 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_   )
										snake_case__							:       List[str]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,**SCREAMING_SNAKE_CASE_   ,)

										snake_case__							:       Union[str, Any]				      =       output.images
										snake_case__							:       Any				      =       image[0, -3:, -3:, -1]
										snake_case__							:       List[str]				      =       [
										    0.73553365,
										    0.7537271,
										    0.74341905,
										    0.66480356,
										    0.6472925,
										    0.63039416,
										    0.64812905,
										    0.6749717,
										    0.6517102,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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





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

										snake_case__							:       List[Any]				      =       '''a photo of a cat'''
										snake_case__							:       Dict				      =       {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

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

										# no sega enabled
										snake_case__							:       Dict				      =       torch.Generator(SCREAMING_SNAKE_CASE_   )
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[str]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,)

										snake_case__							:       int				      =       output.images
										snake_case__							:       int				      =       image[0, -3:, -3:, -1]
										snake_case__							:       List[str]				      =       [
										    0.34887695,
										    0.3876953,
										    0.375,
										    0.34423828,
										    0.3581543,
										    0.35717773,
										    0.3383789,
										    0.34570312,
										    0.359375,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 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_   )
										snake_case__							:       Tuple				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,**SCREAMING_SNAKE_CASE_   ,)

										snake_case__							:       Union[str, Any]				      =       output.images
										snake_case__							:       Tuple				      =       image[0, -3:, -3:, -1]
										snake_case__							:       Tuple				      =       [
										    0.42285156,
										    0.36914062,
										    0.29077148,
										    0.42041016,
										    0.41918945,
										    0.35498047,
										    0.3618164,
										    0.4423828,
										    0.43115234,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 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()


