


import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


a					  =     re.compile(R'\s+')


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					return {"hash": hashlib.mda(re.sub(SCREAMING_SNAKE_CASE_						,							""""""						,							example["""content"""]  ).encode("""utf-8"""  )  ).hexdigest()}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			[len(SCREAMING_SNAKE_CASE_  ) for line in example["""content"""].splitlines()]
					return {"line_mean": np.mean(SCREAMING_SNAKE_CASE_  ), "line_max": max(SCREAMING_SNAKE_CASE_  )}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			np.mean([c.isalnum() for c in example["""content"""]]  )
					return {"alpha_frac": alpha_frac}


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					if example["hash"] in uniques:
										uniques.remove(example["""hash"""]  )
										return True
					else:
										return False


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=5  ):
					lowercase_  							=			["""auto-generated""", """autogenerated""", """automatically generated"""]
					lowercase_  							=			example["""content"""].splitlines()
					for _, line in zip(range(SCREAMING_SNAKE_CASE_  )						,							SCREAMING_SNAKE_CASE_  ):
										for keyword in keywords:
															if keyword in line.lower():
																				return {"autogenerated": True}
					else:
										return {"autogenerated": False}


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=5						,							UpperCAmelCase__=0.05  ):
					lowercase_  							=			["""unit tests""", """test file""", """configuration file"""]
					lowercase_  							=			example["""content"""].splitlines()
					lowercase_  							=			0
					lowercase_  							=			0
					# first test
					for _, line in zip(range(SCREAMING_SNAKE_CASE_  )						,							SCREAMING_SNAKE_CASE_  ):
										for keyword in keywords:
															if keyword in line.lower():
																				return {"config_or_test": True}
    # second test
					lowercase_  							=			example["""content"""].count("""\n"""  )
					lowercase_  							=			int(coeff * nlines  )
					for line in lines:
										count_config += line.lower().count("""config"""  )
										count_test += line.lower().count("""test"""  )
										if count_config > threshold or count_test > threshold:
															return {"config_or_test": True}
					return {"config_or_test": False}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			["""def """, """class """, """for """, """while """]
					lowercase_  							=			example["""content"""].splitlines()
					for line in lines:
										for keyword in keywords:
															if keyword in line.lower():
																				return {"has_no_keywords": False}
					return {"has_no_keywords": True}


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=4  ):
					lowercase_  							=			example["""content"""].splitlines()
					lowercase_  							=			0
					for line in lines:
										counter += line.lower().count("""="""  )
										if counter > minimum:
															return {"has_few_assignments": False}
					return {"has_few_assignments": True}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			tokenizer(example["""content"""]						,							truncation=SCREAMING_SNAKE_CASE_  )["""input_ids"""]
					lowercase_  							=			len(example["""content"""]  ) / len(SCREAMING_SNAKE_CASE_  )
					return {"ratio": ratio}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			{}
					results.update(get_hash(SCREAMING_SNAKE_CASE_  )  )
					results.update(line_stats(SCREAMING_SNAKE_CASE_  )  )
					results.update(alpha_stats(SCREAMING_SNAKE_CASE_  )  )
					results.update(char_token_ratio(SCREAMING_SNAKE_CASE_  )  )
					results.update(is_autogenerated(SCREAMING_SNAKE_CASE_  )  )
					results.update(is_config_or_test(SCREAMING_SNAKE_CASE_  )  )
					results.update(has_no_keywords(SCREAMING_SNAKE_CASE_  )  )
					results.update(has_few_assignments(SCREAMING_SNAKE_CASE_  )  )
					return results


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					if not check_uniques(SCREAMING_SNAKE_CASE_						,							SCREAMING_SNAKE_CASE_  ):
										return False
					elif example["autogenerated"]:
										return False
					elif example["line_max"] > args.line_max:
										return False
					elif example["line_mean"] > args.line_mean:
										return False
					elif example["alpha_frac"] < args.alpha_frac:
										return False
					elif example["ratio"] < args.min_token_ratio:
										return False
					elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
										return False
					elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
										return False
					elif example["has_few_assignments"]:
										return False
					else:
										return True


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					with open(SCREAMING_SNAKE_CASE_						,							"""rb"""  ) as f_in:
										with gzip.open(str(SCREAMING_SNAKE_CASE_  ) + """.gz"""						,							"""wb"""						,							compresslevel=6  ) as f_out:
															shutil.copyfileobj(SCREAMING_SNAKE_CASE_						,							SCREAMING_SNAKE_CASE_  )
					os.unlink(SCREAMING_SNAKE_CASE_  )


# Settings
a					  =     HfArgumentParser(PreprocessingArguments)
a					  =     parser.parse_args()
if args.num_workers is None:
							a					  =     multiprocessing.cpu_count()
a					  =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
a					  =     time.time()
a					  =     load_dataset(args.dataset_name, split='train')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
a					  =     time.time()
a					  =     ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
a					  =     set(ds.unique('hash'))
a					  =     len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
a					  =     time.time()
a					  =     ds.filter(filter, fn_kwargs={'uniques': uniques, 'args': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
							a					  =     time.time()
							a					  =     deduplicate_dataset(ds_filter, args.jaccard_threshold)
							print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
							print(F'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
a					  =     Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
							with open(output_dir / 'duplicate_clusters.json', 'w') as f:
														json.dump(duplicate_clusters, f)

a					  =     output_dir / "data"
data_dir.mkdir(exist_ok=True)

a					  =     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
							a					  =     str(data_dir / F'''file-{file_number+1:012}.json''')
							a					  =     min(len(ds_filter), index + args.samples_per_file)
							ds_filter.select(list(range(index, end_index))).to_json(file_path)
							compress_file(file_path)



print(F'''Time to save dataset: {time.time()-t_start:.2f}''')








import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   StableDiffusionDiffEditPipeline
					__SCREAMING_SNAKE_CASE       :  Optional[Any]						=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
					__SCREAMING_SNAKE_CASE       :  List[str]						=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
					__SCREAMING_SNAKE_CASE       :  int						=   frozenset(
					    []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					__SCREAMING_SNAKE_CASE       :  Any						=   frozenset([]   )

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										torch.manual_seed(0       )
										lowercase_  							=			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							,					attention_head_dim=(2, 4)							,					use_linear_projection=UpperCamelCase__							,					)
										lowercase_  							=			DDIMScheduler(
										    beta_start=0.00_085							,					beta_end=0.012							,					beta_schedule="""scaled_linear"""							,					clip_sample=UpperCamelCase__							,					set_alpha_to_one=UpperCamelCase__							,					)
										lowercase_  							=			DDIMInverseScheduler(
										    beta_start=0.00_085							,					beta_end=0.012							,					beta_schedule="""scaled_linear"""							,					clip_sample=UpperCamelCase__							,					set_alpha_to_zero=UpperCamelCase__							,					)
										torch.manual_seed(0       )
										lowercase_  							=			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							,					sample_size=128							,					)
										torch.manual_seed(0       )
										lowercase_  							=			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							,					hidden_act="""gelu"""							,					projection_dim=512							,					)
										lowercase_  							=			CLIPTextModel(UpperCamelCase__       )
										lowercase_  							=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

										lowercase_  							=			{
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """inverse_scheduler""": inverse_scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}

										return components

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   Any=0       ):






										'''simple docstring'''






										lowercase_  							=			floats_tensor((1, 16, 16)							,					rng=random.Random(UpperCamelCase__       )       ).to(UpperCamelCase__       )
										lowercase_  							=			floats_tensor((1, 2, 4, 16, 16)							,					rng=random.Random(UpperCamelCase__       )       ).to(UpperCamelCase__       )
										if str(UpperCamelCase__       ).startswith("""mps"""       ):
															lowercase_  							=			torch.manual_seed(UpperCamelCase__       )
										else:
															lowercase_  							=			torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
										lowercase_  							=			{
										    """prompt""": """a dog and a newt""",
										    """mask_image""": mask,
										    """image_latents""": latents,
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """inpaint_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}

										return inputs

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   str=0       ):






										'''simple docstring'''






										lowercase_  							=			floats_tensor((1, 3, 32, 32)							,					rng=random.Random(UpperCamelCase__       )       ).to(UpperCamelCase__       )
										lowercase_  							=			image.cpu().permute(0							,					2							,					3							,					1       )[0]
										lowercase_  							=			Image.fromarray(np.uinta(UpperCamelCase__       )       ).convert("""RGB"""       )
										if str(UpperCamelCase__       ).startswith("""mps"""       ):
															lowercase_  							=			torch.manual_seed(UpperCamelCase__       )
										else:
															lowercase_  							=			torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
										lowercase_  							=			{
										    """image""": image,
										    """source_prompt""": """a cat and a frog""",
										    """target_prompt""": """a dog and a newt""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """num_maps_per_mask""": 2,
										    """mask_encode_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}

										return inputs

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Tuple=0       ):






										'''simple docstring'''






										lowercase_  							=			floats_tensor((1, 3, 32, 32)							,					rng=random.Random(UpperCamelCase__       )       ).to(UpperCamelCase__       )
										lowercase_  							=			image.cpu().permute(0							,					2							,					3							,					1       )[0]
										lowercase_  							=			Image.fromarray(np.uinta(UpperCamelCase__       )       ).convert("""RGB"""       )
										if str(UpperCamelCase__       ).startswith("""mps"""       ):
															lowercase_  							=			torch.manual_seed(UpperCamelCase__       )
										else:
															lowercase_  							=			torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
										lowercase_  							=			{
										    """image""": image,
										    """prompt""": """a cat and a frog""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """inpaint_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """decode_latents""": True,
										    """output_type""": """numpy""",
										}
										return inputs

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										if not hasattr(self.pipeline_class							,					"""_optional_components"""       ):
															return

										lowercase_  							=			self.get_dummy_components()
										lowercase_  							=			self.pipeline_class(**UpperCamelCase__       )
										pipe.to(UpperCamelCase__       )
										pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										# set all optional components to None and update pipeline config accordingly
										for optional_component in pipe._optional_components:
															setattr(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       )
										pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}       )

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__       )
										lowercase_  							=			pipe(**UpperCamelCase__       )[0]

										with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(UpperCamelCase__       )
															lowercase_  							=			self.pipeline_class.from_pretrained(UpperCamelCase__       )
															pipe_loaded.to(UpperCamelCase__       )
															pipe_loaded.set_progress_bar_config(disable=UpperCamelCase__       )

										for optional_component in pipe._optional_components:
															self.assertTrue(
															    getattr(UpperCamelCase__							,					UpperCamelCase__       ) is None							,					F'''`{optional_component}` did not stay set to None after loading.'''							,					)

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__       )
										lowercase_  							=			pipe_loaded(**UpperCamelCase__       )[0]

										lowercase_  							=			np.abs(output - output_loaded       ).max()
										self.assertLess(UpperCamelCase__							,					1e-4       )

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_  							=			"""cpu"""

										lowercase_  							=			self.get_dummy_components()
										lowercase_  							=			self.pipeline_class(**UpperCamelCase__       )
										pipe.to(UpperCamelCase__       )
										pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			self.get_dummy_mask_inputs(UpperCamelCase__       )
										lowercase_  							=			pipe.generate_mask(**UpperCamelCase__       )
										lowercase_  							=			mask[0, -3:, -3:]

										self.assertEqual(mask.shape							,					(1, 16, 16)       )
										lowercase_  							=			np.array([0] * 9       )
										lowercase_  							=			np.abs(mask_slice.flatten() - expected_slice       ).max()
										self.assertLessEqual(UpperCamelCase__							,					1e-3       )
										self.assertEqual(mask[0, -3, -4]							,					0       )

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			"""cpu"""

										lowercase_  							=			self.get_dummy_components()
										lowercase_  							=			self.pipeline_class(**UpperCamelCase__       )
										pipe.to(UpperCamelCase__       )
										pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			self.get_dummy_inversion_inputs(UpperCamelCase__       )
										lowercase_  							=			pipe.invert(**UpperCamelCase__       ).images
										lowercase_  							=			image[0, -1, -3:, -3:]

										self.assertEqual(image.shape							,					(2, 32, 32, 3)       )
										lowercase_  							=			np.array(
										    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]							,					)
										lowercase_  							=			np.abs(image_slice.flatten() - expected_slice       ).max()
										self.assertLessEqual(UpperCamelCase__							,					1e-3       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										super().test_inference_batch_single_identical(expected_max_diff=5e-3       )







					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			"""cpu"""

										lowercase_  							=			self.get_dummy_components()

										lowercase_  							=			{"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
										lowercase_  							=			DPMSolverMultistepScheduler(**UpperCamelCase__       )
										lowercase_  							=			DPMSolverMultistepInverseScheduler(**UpperCamelCase__       )

										lowercase_  							=			self.pipeline_class(**UpperCamelCase__       )
										pipe.to(UpperCamelCase__       )
										pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			self.get_dummy_inversion_inputs(UpperCamelCase__       )
										lowercase_  							=			pipe.invert(**UpperCamelCase__       ).images
										lowercase_  							=			image[0, -1, -3:, -3:]

										self.assertEqual(image.shape							,					(2, 32, 32, 3)       )
										lowercase_  							=			np.array(
										    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]							,					)
										lowercase_  							=			np.abs(image_slice.flatten() - expected_slice       ).max()
										self.assertLessEqual(UpperCamelCase__							,					1e-3       )







@require_torch_gpu
@slow
class        UpperCamelCase__   (	unittest.TestCase   ):

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






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

					@classmethod
					def   UpperCAmelCase__       (     cls       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""       )

										lowercase_  							=			raw_image.convert("""RGB"""       ).resize((768, 768)       )

										lowercase_  							=			raw_image

					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			torch.manual_seed(0       )

										lowercase_  							=			StableDiffusionDiffEditPipeline.from_pretrained(
										    """stabilityai/stable-diffusion-2-1"""							,					safety_checker=UpperCamelCase__							,					torch_dtype=torch.floataa       )
										lowercase_  							=			DDIMScheduler.from_config(pipe.scheduler.config       )
										lowercase_  							=			DDIMInverseScheduler.from_config(pipe.scheduler.config       )
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			"""a bowl of fruit"""
										lowercase_  							=			"""a bowl of pears"""

										lowercase_  							=			pipe.generate_mask(
										    image=self.raw_image							,					source_prompt=UpperCamelCase__							,					target_prompt=UpperCamelCase__							,					generator=UpperCamelCase__							,					)

										lowercase_  							=			pipe.invert(
										    prompt=UpperCamelCase__							,					image=self.raw_image							,					inpaint_strength=0.7							,					generator=UpperCamelCase__       ).latents

										lowercase_  							=			pipe(
										    prompt=UpperCamelCase__							,					mask_image=UpperCamelCase__							,					image_latents=UpperCamelCase__							,					generator=UpperCamelCase__							,					negative_prompt=UpperCamelCase__							,					inpaint_strength=0.7							,					output_type="""numpy"""							,					).images[0]

										lowercase_  							=			(
										    np.array(
										        load_image(
										            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										            """/diffedit/pears.png"""       ).resize((768, 768)       )       )
										    / 255
										)
										assert np.abs((expected_image - image).max()       ) < 5e-1







					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			torch.manual_seed(0       )

										lowercase_  							=			StableDiffusionDiffEditPipeline.from_pretrained(
										    """stabilityai/stable-diffusion-2-1"""							,					safety_checker=UpperCamelCase__							,					torch_dtype=torch.floataa       )
										lowercase_  							=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
										lowercase_  							=			DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config       )
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			"""a bowl of fruit"""
										lowercase_  							=			"""a bowl of pears"""

										lowercase_  							=			pipe.generate_mask(
										    image=self.raw_image							,					source_prompt=UpperCamelCase__							,					target_prompt=UpperCamelCase__							,					generator=UpperCamelCase__							,					)

										lowercase_  							=			pipe.invert(
										    prompt=UpperCamelCase__							,					image=self.raw_image							,					inpaint_strength=0.7							,					generator=UpperCamelCase__							,					num_inference_steps=25							,					).latents

										lowercase_  							=			pipe(
										    prompt=UpperCamelCase__							,					mask_image=UpperCamelCase__							,					image_latents=UpperCamelCase__							,					generator=UpperCamelCase__							,					negative_prompt=UpperCamelCase__							,					inpaint_strength=0.7							,					num_inference_steps=25							,					output_type="""numpy"""							,					).images[0]

										lowercase_  							=			(
										    np.array(
										        load_image(
										            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										            """/diffedit/pears.png"""       ).resize((768, 768)       )       )
										    / 255
										)
										assert np.abs((expected_image - image).max()       ) < 5e-1


