






"""simple docstring"""






import argparse
import os
import re


SCREAMING_SNAKE_CASE						:						Dict					   =       '''src/diffusers'''

# Pattern that looks at the indentation in a line.
SCREAMING_SNAKE_CASE						:						int					   =       re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
SCREAMING_SNAKE_CASE						:						List[Any]					   =       re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
SCREAMING_SNAKE_CASE						:						List[Any]					   =       re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
SCREAMING_SNAKE_CASE						:						Tuple					   =       re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
SCREAMING_SNAKE_CASE						:						Optional[int]					   =       re.compile(R'''\[([^\]]+)\]''')



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       _re_indent.search(lowerCAmelCase__				)
				return "" if search is None else search.groups()[0]



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__=""       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None				):
				A__							 =       0
				A__							 =       code.split('\n'				)
				if start_prompt is not None:
								while not lines[index].startswith(lowerCAmelCase__				):
												index += 1
								A__							 =       ['\n'.join(lines[:index]				)]
				else:
								A__							 =       []

				# We split into blocks until we get to the `end_prompt` (or the end of the block).
				A__							 =       [lines[index]]
				index += 1
				while index < len(lowerCAmelCase__				) and (end_prompt is None or not lines[index].startswith(lowerCAmelCase__				)):
								if len(lines[index]				) > 0 and get_indent(lines[index]				) == indent_level:
												if len(lowerCAmelCase__				) > 0 and get_indent(current_block[-1]				).startswith(indent_level + ' '				):
																current_block.append(lines[index]				)
																blocks.append('\n'.join(lowerCAmelCase__				)				)
																if index < len(lowerCAmelCase__				) - 1:
																				A__							 =       [lines[index + 1]]
																				index += 1
																else:
																				A__							 =       []
												else:
																blocks.append('\n'.join(lowerCAmelCase__				)				)
																A__							 =       [lines[index]]
								else:
												current_block.append(lines[index]				)
								index += 1

				# Adds current block if it's nonempty.
				if len(lowerCAmelCase__				) > 0:
								blocks.append('\n'.join(lowerCAmelCase__				)				)

				# Add final block after end_prompt if provided.
				if end_prompt is not None and index < len(lowerCAmelCase__				):
								blocks.append('\n'.join(lines[index:]				)				)

				return blocks



def  __lowerCamelCase					(					lowerCAmelCase__				):

				def _inner(lowerCAmelCase__				):
								return key(lowerCAmelCase__				).lower().replace('_'       ,''				)

				return _inner



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__=None				):

				# If no key is provided, we use a noop.
				def noop(lowerCAmelCase__				):
								return x

				if key is None:
								A__							 =       noop
				# Constants are all uppercase, they go first.
				A__							 =       [obj for obj in objects if key(lowerCAmelCase__				).isupper()]
				# Classes are not all uppercase but start with a capital, they go second.
				A__							 =       [obj for obj in objects if key(lowerCAmelCase__				)[0].isupper() and not key(lowerCAmelCase__				).isupper()]
				# Functions begin with a lowercase, they go last.
				A__							 =       [obj for obj in objects if not key(lowerCAmelCase__				)[0].isupper()]

				A__							 =       ignore_underscore(lowerCAmelCase__				)
				return sorted(lowerCAmelCase__       ,key=lowerCAmelCase__				) + sorted(lowerCAmelCase__       ,key=lowerCAmelCase__				) + sorted(lowerCAmelCase__       ,key=lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__				):

				# This inner function sort imports between [ ].
				def _replace(lowerCAmelCase__				):
								A__							 =       match.groups()[0]
								if "," not in imports:
												return f'''[{imports}]'''
								A__							 =       [part.strip().replace('"'       ,''				) for part in imports.split(','				)]
								# We will have a final empty element if the line finished with a comma.
								if len(keys[-1]				) == 0:
												A__							 =       keys[:-1]
								return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(lowerCAmelCase__				)]				) + "]"

				A__							 =       import_statement.split('\n'				)
				if len(lowerCAmelCase__				) > 3:
								# Here we have to sort internal imports that are on several lines (one per name):
								# key: [
								#     "object1",
								#     "object2",
								#     ...
								# ]

								# We may have to ignore one or two lines on each side.
								A__							 =       2 if lines[1].strip() == '[' else 1
								A__							 =       [(i, _re_strip_line.search(lowerCAmelCase__				).groups()[0]) for i, line in enumerate(lines[idx:-idx]				)]
								A__							 =       sort_objects(lowerCAmelCase__       ,key=lambda lowerCAmelCase__				: x[1]				)
								A__							 =       [lines[x[0] + idx] for x in sorted_indices]
								return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]				)
				elif len(lowerCAmelCase__				) == 3:
								# Here we have to sort internal imports that are on one separate line:
								# key: [
								#     "object1", "object2", ...
								# ]
								if _re_bracket_content.search(lines[1]				) is not None:
												A__							 =       _re_bracket_content.sub(_replace       ,lines[1]				)
								else:
												A__							 =       [part.strip().replace('"'       ,''				) for part in lines[1].split(','				)]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1]				) == 0:
																A__							 =       keys[:-1]
												A__							 =       get_indent(lines[1]				) + ', '.join([f'''"{k}"''' for k in sort_objects(lowerCAmelCase__				)]				)
								return "\n".join(lowerCAmelCase__				)
				else:
								# Finally we have to deal with imports fitting on one line
								A__							 =       _re_bracket_content.sub(_replace       ,lowerCAmelCase__				)
								return import_statement



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__=True				):
				with open(lowerCAmelCase__       ,'r'				) as f:
								A__							 =       f.read()

				if "_import_structure" not in code:
								return

				# Blocks of indent level 0
				A__							 =       split_code_in_indented_blocks(
				    lowerCAmelCase__       ,start_prompt='_import_structure = {'       ,end_prompt='if TYPE_CHECKING:'				)

				# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
				for block_idx in range(1       ,len(lowerCAmelCase__				) - 1				):
								# Check if the block contains some `_import_structure`s thingy to sort.
								A__							 =       main_blocks[block_idx]
								A__							 =       block.split('\n'				)

								# Get to the start of the imports.
								A__							 =       0
								while line_idx < len(lowerCAmelCase__				) and "_import_structure" not in block_lines[line_idx]:
												# Skip dummy import blocks
												if "import dummy" in block_lines[line_idx]:
																A__							 =       len(lowerCAmelCase__				)
												else:
																line_idx += 1
								if line_idx >= len(lowerCAmelCase__				):
												continue

								# Ignore beginning and last line: they don't contain anything.
								A__							 =       '\n'.join(block_lines[line_idx:-1]				)
								A__							 =       get_indent(block_lines[1]				)
								# Slit the internal block into blocks of indent level 1.
								A__							 =       split_code_in_indented_blocks(lowerCAmelCase__       ,indent_level=lowerCAmelCase__				)
								# We have two categories of import key: list or _import_structure[key].append/extend
								A__							 =       _re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
								# Grab the keys, but there is a trap: some lines are empty or just comments.
								A__							 =       [(pattern.search(lowerCAmelCase__				).groups()[0] if pattern.search(lowerCAmelCase__				) is not None else None) for b in internal_blocks]
								# We only sort the lines with a key.
								A__							 =       [(i, key) for i, key in enumerate(lowerCAmelCase__				) if key is not None]
								A__							 =       [x[0] for x in sorted(lowerCAmelCase__       ,key=lambda lowerCAmelCase__				: x[1]				)]

								# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
								A__							 =       0
								A__							 =       []
								for i in range(len(lowerCAmelCase__				)				):
												if keys[i] is None:
																reordered_blocks.append(internal_blocks[i]				)
												else:
																A__							 =       sort_objects_in_import(internal_blocks[sorted_indices[count]]				)
																reordered_blocks.append(lowerCAmelCase__				)
																count += 1

        # And we put our main block back together with its first and last line.
								A__							 =       '\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]				)

				if code != "\n".join(lowerCAmelCase__				):
								if check_only:
												return True
								else:
												print(f'''Overwriting {file}.'''				)
												with open(lowerCAmelCase__       ,'w'				) as f:
																f.write('\n'.join(lowerCAmelCase__				)				)



def  __lowerCamelCase					(					lowerCAmelCase__=True				):
				A__							 =       []
				for root, _, files in os.walk(lowerCAmelCase__				):
								if "__init__.py" in files:
												A__							 =       sort_imports(os.path.join(lowerCAmelCase__       ,'__init__.py'				)       ,check_only=lowerCAmelCase__				)
												if result:
																A__							 =       [os.path.join(lowerCAmelCase__       ,'__init__.py'				)]
				if len(lowerCAmelCase__				) > 0:
								raise ValueError(f'''Would overwrite {len(lowerCAmelCase__				)} files, run `make style`.'''				)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE						:						Optional[int]					   =       argparse.ArgumentParser()
					parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
					SCREAMING_SNAKE_CASE						:						Optional[Any]					   =       parser.parse_args()

					sort_imports_in_all_inits(check_only=args.check_only)










"""simple docstring"""






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       snake_case_       (    _lowerCamelCase    ,							_lowerCamelCase    ,							unittest.TestCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					StableDiffusionDiffEditPipeline
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
	SCREAMING_SNAKE_CASE_:							int   			=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
	SCREAMING_SNAKE_CASE_:							str   			=					frozenset(
	    []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	SCREAMING_SNAKE_CASE_:							int   			=					frozenset([]       )


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					torch.manual_seed(0						)
					A__							 =       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=__a  ,					)
					A__							 =       DDIMScheduler(
					    beta_start=0.0_0085  ,					beta_end=0.012  ,					beta_schedule='scaled_linear'  ,					clip_sample=__a  ,					set_alpha_to_one=__a  ,					)
					A__							 =       DDIMInverseScheduler(
					    beta_start=0.0_0085  ,					beta_end=0.012  ,					beta_schedule='scaled_linear'  ,					clip_sample=__a  ,					set_alpha_to_zero=__a  ,					)
					torch.manual_seed(0						)
					A__							 =       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						)
					A__							 =       CLIPTextConfig(
					    bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=32  ,					intermediate_size=37  ,					layer_norm_eps=1E-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=1000  ,					hidden_act='gelu'  ,					projection_dim=512  ,					)
					A__							 =       CLIPTextModel(__a						)
					A__							 =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

					A__							 =       {
					    '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  ,					__a  ,					__a=0						):


					"""simple docstring"""

					A__							 =       floats_tensor((1, 16, 16)  ,					rng=random.Random(__a						)						).to(__a						)
					A__							 =       floats_tensor((1, 2, 4, 16, 16)  ,					rng=random.Random(__a						)						).to(__a						)
					if str(__a						).startswith('mps'						):
									A__							 =       torch.manual_seed(__a						)
					else:
									A__							 =       torch.Generator(device=__a						).manual_seed(__a						)
					A__							 =       {
					    '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  ,					__a  ,					__a=0						):


					"""simple docstring"""

					A__							 =       floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(__a						)						).to(__a						)
					A__							 =       image.cpu().permute(0  ,					2  ,					3  ,					1						)[0]
					A__							 =       Image.fromarray(np.uinta(__a						)						).convert('RGB'						)
					if str(__a						).startswith('mps'						):
									A__							 =       torch.manual_seed(__a						)
					else:
									A__							 =       torch.Generator(device=__a						).manual_seed(__a						)
					A__							 =       {
					    '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  ,					__a  ,					__a=0						):


					"""simple docstring"""

					A__							 =       floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(__a						)						).to(__a						)
					A__							 =       image.cpu().permute(0  ,					2  ,					3  ,					1						)[0]
					A__							 =       Image.fromarray(np.uinta(__a						)						).convert('RGB'						)
					if str(__a						).startswith('mps'						):
									A__							 =       torch.manual_seed(__a						)
					else:
									A__							 =       torch.Generator(device=__a						).manual_seed(__a						)
					A__							 =       {
					    '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						):


					"""simple docstring"""

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

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

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

					A__							 =       self.get_dummy_inputs(__a						)
					A__							 =       pipe(**__a						)[0]

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

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

					A__							 =       self.get_dummy_inputs(__a						)
					A__							 =       pipe_loaded(**__a						)[0]

					A__							 =       np.abs(output - output_loaded						).max()
					self.assertLess(__a  ,					1E-4						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'cpu'

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

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

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'cpu'

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

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

					self.assertEqual(image.shape  ,					(2, 32, 32, 3)						)
					A__							 =       np.array(
					    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]  ,					)
					A__							 =       np.abs(image_slice.flatten() - expected_slice						).max()
					self.assertLessEqual(__a  ,					1E-3						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'cpu'

					A__							 =       self.get_dummy_components()

					A__							 =       {'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
					A__							 =       DPMSolverMultistepScheduler(**__a						)
					A__							 =       DPMSolverMultistepInverseScheduler(**__a						)

					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

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

					self.assertEqual(image.shape  ,					(2, 32, 32, 3)						)
					A__							 =       np.array(
					    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]  ,					)
					A__							 =       np.abs(image_slice.flatten() - expected_slice						).max()
					self.assertLessEqual(__a  ,					1E-3						)





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



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	@classmethod
	def       _UpperCAmelCase						(      cls						):


					"""simple docstring"""

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

					A__							 =       raw_image.convert('RGB'						).resize((768, 768)						)

					A__							 =       raw_image


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       torch.manual_seed(0						)

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

					A__							 =       'a bowl of fruit'
					A__							 =       'a bowl of pears'

					A__							 =       pipe.generate_mask(
					    image=self.raw_image  ,					source_prompt=__a  ,					target_prompt=__a  ,					generator=__a  ,					)

					A__							 =       pipe.invert(
					    prompt=__a  ,					image=self.raw_image  ,					inpaint_strength=0.7  ,					generator=__a						).latents

					A__							 =       pipe(
					    prompt=__a  ,					mask_image=__a  ,					image_latents=__a  ,					generator=__a  ,					negative_prompt=__a  ,					inpaint_strength=0.7  ,					output_type='numpy'  ,					).images[0]

					A__							 =       (
					    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						):


					"""simple docstring"""

					A__							 =       torch.manual_seed(0						)

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

					A__							 =       'a bowl of fruit'
					A__							 =       'a bowl of pears'

					A__							 =       pipe.generate_mask(
					    image=self.raw_image  ,					source_prompt=__a  ,					target_prompt=__a  ,					generator=__a  ,					)

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

					A__							 =       pipe(
					    prompt=__a  ,					mask_image=__a  ,					image_latents=__a  ,					generator=__a  ,					negative_prompt=__a  ,					inpaint_strength=0.7  ,					num_inference_steps=25  ,					output_type='numpy'  ,					).images[0]

					A__							 =       (
					    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


