


'''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 			lowerCAmelCase					(					unittest.TestCase				):



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



	@property
	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Optional[int]   				=			1
		lowerCAmelCase       :     Optional[int]   				=			3
		lowerCAmelCase       :     Optional[int]   				=			(32, 32)

		lowerCAmelCase       :     Tuple   				=			floats_tensor((batch_size, num_channels) + sizes			,       rng=random.Random(0     )     ).to(__UpperCAmelCase     )
		return image



	@property
	def 					lowercase  (       self     ):
		torch.manual_seed(0     )
		lowerCAmelCase       :     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 					lowercase  (       self     ):
		torch.manual_seed(0     )
		lowerCAmelCase       :     Tuple   				=			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 					lowercase  (       self     ):
		torch.manual_seed(0     )
		lowerCAmelCase       :     Optional[Any]   				=			CLIPTextConfig(
		    bos_token_id=0			,       eos_token_id=2			,       hidden_size=32			,       intermediate_size=37			,       layer_norm_eps=1e-0_5			,       num_attention_heads=4			,       num_hidden_layers=5			,       pad_token_id=1			,       vocab_size=1000			,       )
		return CLIPTextModel(__UpperCAmelCase     )



	@property
	def 					lowercase  (       self     ):
		def extract(*snake_case__			,       **snake_case__     ):



			class 			lowerCAmelCase					:



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



				def 					lowercase  (       self			,       snake_case__     ):
					self.pixel_values.to(__UpperCAmelCase     )
					return self


			return Out()

		return extract



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     List[str]   				=			"cpu"  # ensure determinism for the device-dependent torch.Generator
		lowerCAmelCase       :     Any   				=			self.dummy_cond_unet
		lowerCAmelCase       :     Optional[Any]   				=			DDIMScheduler(
		    beta_start=0.0_0_0_8_5			,       beta_end=0.0_1_2			,       beta_schedule='scaled_linear'			,       clip_sample=__UpperCAmelCase			,       set_alpha_to_one=__UpperCAmelCase			,       )

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

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

		lowerCAmelCase       :     int   				=			"A painting of a squirrel eating a burger"

		lowerCAmelCase       :     Optional[int]   				=			torch.Generator(device=__UpperCAmelCase     ).manual_seed(0     )
		lowerCAmelCase       :     str   				=			sd_pipe([prompt]			,       generator=__UpperCAmelCase			,       guidance_scale=6.0			,       num_inference_steps=2			,       output_type='np'     )
		lowerCAmelCase       :     str   				=			output.images

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

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

		assert image.shape == (1, 64, 64, 3)
		lowerCAmelCase       :     List[str]   				=			np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]     )

		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 					lowercase  (       self     ):
		lowerCAmelCase       :     Union[str, Any]   				=			"cpu"  # ensure determinism for the device-dependent torch.Generator
		lowerCAmelCase       :     Union[str, Any]   				=			self.dummy_cond_unet
		lowerCAmelCase       :     List[str]   				=			PNDMScheduler(skip_prk_steps=__UpperCAmelCase     )
		lowerCAmelCase       :     List[Any]   				=			self.dummy_vae
		lowerCAmelCase       :     List[Any]   				=			self.dummy_text_encoder
		lowerCAmelCase       :     Tuple   				=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

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

		lowerCAmelCase       :     Tuple   				=			"A painting of a squirrel eating a burger"
		lowerCAmelCase       :     Tuple   				=			torch.Generator(device=__UpperCAmelCase     ).manual_seed(0     )
		lowerCAmelCase       :     Optional[Any]   				=			sd_pipe([prompt]			,       generator=__UpperCAmelCase			,       guidance_scale=6.0			,       num_inference_steps=2			,       output_type='np'     )

		lowerCAmelCase       :     Dict   				=			output.images

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

		lowerCAmelCase       :     Optional[int]   				=			image[0, -3:, -3:, -1]
		lowerCAmelCase       :     Any   				=			image_from_tuple[0, -3:, -3:, -1]

		assert image.shape == (1, 64, 64, 3)
		lowerCAmelCase       :     List[Any]   				=			np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]     )

		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 					lowercase  (       self     ):
		lowerCAmelCase       :     Tuple   				=			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

		lowerCAmelCase       :     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(__UpperCAmelCase     )
			lowerCAmelCase       :     Tuple   				=			StableDiffusionPipeline.from_pretrained(__UpperCAmelCase     )

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

		# put models in fp16
		lowerCAmelCase       :     Union[str, Any]   				=			unet.half()
		lowerCAmelCase       :     Union[str, Any]   				=			vae.half()
		lowerCAmelCase       :     Union[str, Any]   				=			bert.half()

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

		lowerCAmelCase       :     List[str]   				=			"A painting of a squirrel eating a burger"
		lowerCAmelCase       :     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 			lowerCAmelCase					(					unittest.TestCase				):



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



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Dict   				=			"cuda"
		lowerCAmelCase       :     Tuple   				=			StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'     )
		lowerCAmelCase       :     int   				=			pipe.to(__UpperCAmelCase     )
		pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

		lowerCAmelCase       :     List[Any]   				=			"a photo of a cat"
		lowerCAmelCase       :     List[str]   				=			{
		    "editing_prompt": ["sunglasses"],
		    "reverse_editing_direction": [False],
		    "edit_warmup_steps": 10,
		    "edit_guidance_scale": 6,
		    "edit_threshold": 0.9_5,
		    "edit_momentum_scale": 0.5,
		    "edit_mom_beta": 0.6,
		}

		lowerCAmelCase       :     int   				=			3
		lowerCAmelCase       :     Union[str, Any]   				=			7

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

		lowerCAmelCase       :     Union[str, Any]   				=			output.images
		lowerCAmelCase       :     str   				=			image[0, -3:, -3:, -1]
		lowerCAmelCase       :     List[str]   				=			[
		    0.3_4_6_7_3_1_1_3,
		    0.3_8_4_9_2_7_3_3,
		    0.3_7_5_9_7_3_5_2,
		    0.3_4_0_8_6_3_3_5,
		    0.3_5_6_5_0_7_4_8,
		    0.3_5_5_7_9_2_0_5,
		    0.3_3_8_4_7_6_3,
		    0.3_4_3_4_0_2_3_6,
		    0.3_5_7_3_2_7_1,
		]

		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(__UpperCAmelCase     )
		lowerCAmelCase       :     Union[str, Any]   				=			pipe(
		    [prompt]			,       generator=__UpperCAmelCase			,       guidance_scale=__UpperCAmelCase			,       num_inference_steps=50			,       output_type='np'			,       width=512			,       height=512			,       **__UpperCAmelCase			,       )

		lowerCAmelCase       :     List[str]   				=			output.images
		lowerCAmelCase       :     str   				=			image[0, -3:, -3:, -1]
		lowerCAmelCase       :     str   				=			[
		    0.4_1_8_8_7_8_2_6,
		    0.3_7_7_2_8_7_6_6,
		    0.3_0_1_3_8_2_7_2,
		    0.4_1_4_1_6_3_3_5,
		    0.4_1_6_6_4_9_8_5,
		    0.3_6_2_8_3_3_9_2,
		    0.3_6_1_9_1_2_4_6,
		    0.4_3_3_6_4_4_6_5,
		    0.4_3_0_0_1_7_3_2,
		]

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

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



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Union[str, Any]   				=			"cuda"
		lowerCAmelCase       :     Dict   				=			StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'     )
		lowerCAmelCase       :     Union[str, Any]   				=			pipe.to(__UpperCAmelCase     )
		pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

		lowerCAmelCase       :     List[str]   				=			"an image of a crowded boulevard, realistic, 4k"
		lowerCAmelCase       :     int   				=			{
		    "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,
		}

		lowerCAmelCase       :     str   				=			9
		lowerCAmelCase       :     int   				=			7

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

		lowerCAmelCase       :     List[str]   				=			output.images
		lowerCAmelCase       :     Union[str, Any]   				=			image[0, -3:, -3:, -1]
		lowerCAmelCase       :     Union[str, Any]   				=			[
		    0.4_3_4_9_7_9_9_8,
		    0.9_1_8_1_4_0_6_5,
		    0.7_5_4_0_7_3_9,
		    0.5_5_5_8_0_2_0_5,
		    0.8_4_6_7_2_6_5,
		    0.5_3_8_9_6_9_1,
		    0.6_2_5_7_4_5_0_6,
		    0.5_8_8_9_7_7_6_3,
		    0.5_0_9_2_6_7_5_7,
		]

		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(__UpperCAmelCase     )
		lowerCAmelCase       :     str   				=			pipe(
		    [prompt]			,       generator=__UpperCAmelCase			,       guidance_scale=__UpperCAmelCase			,       num_inference_steps=50			,       output_type='np'			,       width=512			,       height=512			,       **__UpperCAmelCase			,       )

		lowerCAmelCase       :     Dict   				=			output.images
		lowerCAmelCase       :     List[str]   				=			image[0, -3:, -3:, -1]
		lowerCAmelCase       :     List[str]   				=			[
		    0.3_0_8_9_7_1_9,
		    0.3_0_5_0_0_1_4_4,
		    0.2_9_0_1_6_0_4_2,
		    0.3_0_6_3_0_9_6_4,
		    0.3_2_5_6_8_7,
		    0.2_9_4_1_9_2_2_5,
		    0.2_9_0_8_0_9_1,
		    0.2_8_7_2_3_5_9_8,
		    0.2_7_6_9_6_2_9_4,
		]

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

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



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     int   				=			"cuda"
		lowerCAmelCase       :     str   				=			StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'     )
		lowerCAmelCase       :     Tuple   				=			pipe.to(__UpperCAmelCase     )
		pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

		lowerCAmelCase       :     int   				=			"a castle next to a river"
		lowerCAmelCase       :     Optional[Any]   				=			{
		    "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,
		}

		lowerCAmelCase       :     Union[str, Any]   				=			48
		lowerCAmelCase       :     Tuple   				=			7

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

		lowerCAmelCase       :     str   				=			output.images
		lowerCAmelCase       :     Union[str, Any]   				=			image[0, -3:, -3:, -1]
		lowerCAmelCase       :     Optional[Any]   				=			[
		    0.7_5_1_6_3_5_5_5,
		    0.7_6_0_3_7_1_4_5,
		    0.6_1_7_8_5,
		    0.9_1_8_9_6_7_3,
		    0.8_6_2_7_7_0_1,
		    0.8_5_1_8_9_6_9_4,
		    0.8_5_1_2_8_1_3,
		    0.8_7_0_1_2_0_7_6,
		    0.8_3_1_2_8_5_7,
		]

		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(__UpperCAmelCase     )
		lowerCAmelCase       :     int   				=			pipe(
		    [prompt]			,       generator=__UpperCAmelCase			,       guidance_scale=__UpperCAmelCase			,       num_inference_steps=50			,       output_type='np'			,       width=512			,       height=512			,       **__UpperCAmelCase			,       )

		lowerCAmelCase       :     Optional[Any]   				=			output.images
		lowerCAmelCase       :     int   				=			image[0, -3:, -3:, -1]
		lowerCAmelCase       :     int   				=			[
		    0.7_3_5_5_3_3_6_5,
		    0.7_5_3_7_2_7_1,
		    0.7_4_3_4_1_9_0_5,
		    0.6_6_4_8_0_3_5_6,
		    0.6_4_7_2_9_2_5,
		    0.6_3_0_3_9_4_1_6,
		    0.6_4_8_1_2_9_0_5,
		    0.6_7_4_9_7_1_7,
		    0.6_5_1_7_1_0_2,
		]

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

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



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     str   				=			"cuda"
		lowerCAmelCase       :     List[str]   				=			StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'			,       torch_dtype=torch.floataa     )
		lowerCAmelCase       :     Optional[Any]   				=			pipe.to(__UpperCAmelCase     )
		pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

		lowerCAmelCase       :     List[Any]   				=			"a photo of a cat"
		lowerCAmelCase       :     List[str]   				=			{
		    "editing_prompt": ["sunglasses"],
		    "reverse_editing_direction": [False],
		    "edit_warmup_steps": 10,
		    "edit_guidance_scale": 6,
		    "edit_threshold": 0.9_5,
		    "edit_momentum_scale": 0.5,
		    "edit_mom_beta": 0.6,
		}

		lowerCAmelCase       :     Optional[Any]   				=			3
		lowerCAmelCase       :     List[str]   				=			7

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

		lowerCAmelCase       :     Optional[Any]   				=			output.images
		lowerCAmelCase       :     Dict   				=			image[0, -3:, -3:, -1]
		lowerCAmelCase       :     int   				=			[
		    0.3_4_8_8_7_6_9_5,
		    0.3_8_7_6_9_5_3,
		    0.3_7_5,
		    0.3_4_4_2_3_8_2_8,
		    0.3_5_8_1_5_4_3,
		    0.3_5_7_1_7_7_7_3,
		    0.3_3_8_3_7_8_9,
		    0.3_4_5_7_0_3_1_2,
		    0.3_5_9_3_7_5,
		]

		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(__UpperCAmelCase     )
		lowerCAmelCase       :     Optional[Any]   				=			pipe(
		    [prompt]			,       generator=__UpperCAmelCase			,       guidance_scale=__UpperCAmelCase			,       num_inference_steps=50			,       output_type='np'			,       width=512			,       height=512			,       **__UpperCAmelCase			,       )

		lowerCAmelCase       :     Optional[int]   				=			output.images
		lowerCAmelCase       :     str   				=			image[0, -3:, -3:, -1]
		lowerCAmelCase       :     Tuple   				=			[
		    0.4_2_2_8_5_1_5_6,
		    0.3_6_9_1_4_0_6_2,
		    0.2_9_0_7_7_1_4_8,
		    0.4_2_0_4_1_0_1_6,
		    0.4_1_9_1_8_9_4_5,
		    0.3_5_4_9_8_0_4_7,
		    0.3_6_1_8_1_6_4,
		    0.4_4_2_3_8_2_8,
		    0.4_3_1_1_5_2_3_4,
		]

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

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




"""simple docstring"""







def 	lowercase     (							A_ = 100    ,  A_ = 100			)-> int:
  '''simple docstring'''







  return max(
      sum(int(A_			) for x in str(base**power			)			)
      for base in range(A_			)
      for power in range(A_			)			)


# Tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()






