



import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowerCAmelCase__	:  Optional[int] 		=datasets.utils.logging.get_logger(__name__)

@dataclass
class      UpperCAmelCase_						(     datasets.BuilderConfig ):

						'''simple docstring'''




						UpperCamelCase__    :				Optional[datasets.Features]       						=						None







class      UpperCAmelCase_						(     datasets.ArrowBasedBuilder ):

						'''simple docstring'''




						UpperCamelCase__    :				List[str]       						=						ArrowConfig






						def  _A			(					self	):







													'''simple docstring'''

													return datasets.DatasetInfo(features=self.config.features	)






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													if not self.config.data_files:
																				raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""	)
													__SCREAMING_SNAKE_CASE								=      dl_manager.download_and_extract(self.config.data_files	)
													if isinstance(_A   ,							(str, list, tuple)	):
																				__SCREAMING_SNAKE_CASE								=      data_files
																				if isinstance(_A   ,							_A	):
																											__SCREAMING_SNAKE_CASE								=      [files]
																				# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																				__SCREAMING_SNAKE_CASE								=      [dl_manager.iter_files(_A	) for file in files]
																				return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,							gen_kwargs={'files': files}	)]
													__SCREAMING_SNAKE_CASE								=      []
													for split_name, files in data_files.items():
																				if isinstance(_A   ,							_A	):
																											__SCREAMING_SNAKE_CASE								=      [files]
																				# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																				__SCREAMING_SNAKE_CASE								=      [dl_manager.iter_files(_A	) for file in files]
																				# Infer features is they are stoed in the arrow schema
																				if self.info.features is None:
																											for file in itertools.chain.from_iterable(_A	):
																																		with open(_A   ,							'rb'	) as f:
																																									__SCREAMING_SNAKE_CASE								=      datasets.Features.from_arrow_schema(pa.ipc.open_stream(_A	).schema	)
																																		break
																				splits.append(datasets.SplitGenerator(name=_A   ,							gen_kwargs={'files': files}	)	)
													return splits






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													if self.info.features is not None:
																				# more expensive cast to support nested features with keys in a different order
																				# allows str <-> int/float or str to Audio for example
																				__SCREAMING_SNAKE_CASE								=      table_cast(_A   ,							self.info.features.arrow_schema	)
													return pa_table






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													for file_idx, file in enumerate(itertools.chain.from_iterable(_A	)	):
																				with open(_A   ,							'rb'	) as f:
																											try:
																																		for batch_idx, record_batch in enumerate(pa.ipc.open_stream(_A	)	):
																																									__SCREAMING_SNAKE_CASE								=      pa.Table.from_batches([record_batch]	)
																																									# Uncomment for debugging (will print the Arrow table size and elements)
																																									# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																									# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																									yield f"""{file_idx}_{batch_idx}""", self._cast_table(_A	)
																											except ValueError as e:
																																		logger.error(f"""Failed to read file '{file}' with error {type(_A	)}: {e}"""	)
																																		raise











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      UpperCAmelCase_						(     unittest.TestCase ):

						'''simple docstring'''




						def  _A			(					self	):







													'''simple docstring'''

													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						@property
						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      1
													__SCREAMING_SNAKE_CASE								=      3
													__SCREAMING_SNAKE_CASE								=      (32, 32)

													__SCREAMING_SNAKE_CASE								=      floats_tensor((batch_size, num_channels) + sizes   ,							rng=random.Random(0	)	).to(_A	)
													return image






						@property
						def  _A			(					self	):







													'''simple docstring'''

													torch.manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      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	)
													__SCREAMING_SNAKE_CASE								=      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	)
													__SCREAMING_SNAKE_CASE								=      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(_A	)






						@property
						def  _A			(					self	):







													'''simple docstring'''

													def extract(*_A   ,							**_A	):

																				class      UpperCAmelCase_						:

																										'''simple docstring'''




																										def __init__(					self	):







																																	'''simple docstring'''

																																	__SCREAMING_SNAKE_CASE								=      torch.ones([0]	)






																										def  _A			(					self   ,							_A	):







																																	'''simple docstring'''

																																	self.pixel_values.to(_A	)
																																	return self







																				return Out()

													return extract






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cpu'  # ensure determinism for the device-dependent torch.Generator
													__SCREAMING_SNAKE_CASE								=      self.dummy_cond_unet
													__SCREAMING_SNAKE_CASE								=      DDIMScheduler(
													    beta_start=0.0_0_0_8_5   ,							beta_end=0.0_1_2   ,							beta_schedule='scaled_linear'   ,							clip_sample=_A   ,							set_alpha_to_one=_A   ,							)

													__SCREAMING_SNAKE_CASE								=      self.dummy_vae
													__SCREAMING_SNAKE_CASE								=      self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE								=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

													# make sure here that pndm scheduler skips prk
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline(
													    unet=_A   ,							scheduler=_A   ,							vae=_A   ,							text_encoder=_A   ,							tokenizer=_A   ,							safety_checker=_A   ,							feature_extractor=self.dummy_extractor   ,							)
													__SCREAMING_SNAKE_CASE								=      sd_pipe.to(_A	)
													sd_pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'A painting of a squirrel eating a burger'

													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      sd_pipe([prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'	)
													__SCREAMING_SNAKE_CASE								=      output.images

													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      sd_pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'   ,							return_dict=_A   ,							)[0]

													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cpu'  # ensure determinism for the device-dependent torch.Generator
													__SCREAMING_SNAKE_CASE								=      self.dummy_cond_unet
													__SCREAMING_SNAKE_CASE								=      PNDMScheduler(skip_prk_steps=_A	)
													__SCREAMING_SNAKE_CASE								=      self.dummy_vae
													__SCREAMING_SNAKE_CASE								=      self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE								=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

													# make sure here that pndm scheduler skips prk
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline(
													    unet=_A   ,							scheduler=_A   ,							vae=_A   ,							text_encoder=_A   ,							tokenizer=_A   ,							safety_checker=_A   ,							feature_extractor=self.dummy_extractor   ,							)
													__SCREAMING_SNAKE_CASE								=      sd_pipe.to(_A	)
													sd_pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'A painting of a squirrel eating a burger'
													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      sd_pipe([prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'	)

													__SCREAMING_SNAKE_CASE								=      output.images

													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      sd_pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'   ,							return_dict=_A   ,							)[0]

													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'   ,							safety_checker=_A	)
													assert isinstance(_A   ,							_A	)
													assert isinstance(pipe.scheduler   ,							_A	)
													assert pipe.safety_checker is None

													__SCREAMING_SNAKE_CASE								=      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(_A	)
																				__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained(_A	)

													# sanity check that the pipeline still works
													assert pipe.safety_checker is None
													__SCREAMING_SNAKE_CASE								=      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'''

													__SCREAMING_SNAKE_CASE								=      self.dummy_cond_unet
													__SCREAMING_SNAKE_CASE								=      PNDMScheduler(skip_prk_steps=_A	)
													__SCREAMING_SNAKE_CASE								=      self.dummy_vae
													__SCREAMING_SNAKE_CASE								=      self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE								=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

													# put models in fp16
													__SCREAMING_SNAKE_CASE								=      unet.half()
													__SCREAMING_SNAKE_CASE								=      vae.half()
													__SCREAMING_SNAKE_CASE								=      bert.half()

													# make sure here that pndm scheduler skips prk
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline(
													    unet=_A   ,							scheduler=_A   ,							vae=_A   ,							text_encoder=_A   ,							tokenizer=_A   ,							safety_checker=_A   ,							feature_extractor=self.dummy_extractor   ,							)
													__SCREAMING_SNAKE_CASE								=      sd_pipe.to(_A	)
													sd_pipe.set_progress_bar_config(disable=_A	)

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

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







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

						'''simple docstring'''




						def  _A			(					self	):







													'''simple docstring'''

													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cuda'
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
													pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'a photo of a cat'
													__SCREAMING_SNAKE_CASE								=      {
													    '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,
													}

													__SCREAMING_SNAKE_CASE								=      3
													__SCREAMING_SNAKE_CASE								=      7

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

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cuda'
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
													pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'an image of a crowded boulevard, realistic, 4k'
													__SCREAMING_SNAKE_CASE								=      {
													    '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,
													}

													__SCREAMING_SNAKE_CASE								=      9
													__SCREAMING_SNAKE_CASE								=      7

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

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cuda'
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
													pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'a castle next to a river'
													__SCREAMING_SNAKE_CASE								=      {
													    '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,
													}

													__SCREAMING_SNAKE_CASE								=      48
													__SCREAMING_SNAKE_CASE								=      7

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

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cuda'
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'   ,							torch_dtype=torch.floataa	)
													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
													pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'a photo of a cat'
													__SCREAMING_SNAKE_CASE								=      {
													    '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,
													}

													__SCREAMING_SNAKE_CASE								=      3
													__SCREAMING_SNAKE_CASE								=      7

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

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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






