

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
A__						=							logging.get_logger()

@dataclass
class 				__UpperCamelCase				:
 _lowercase  :   nn.Module
 _lowercase  :   List[nn.Module]       				=  field(default_factory=__UpperCAmelCase			)
 _lowercase  :   list       				=  field(default_factory=__UpperCAmelCase			)







 def 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: Any  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]      ):

      '''simple docstring'''





      __magic_name__							=    len(list(m.modules()      )      ) == 1 or isinstance(UpperCAmelCase_  , nn.Convad      ) or isinstance(UpperCAmelCase_  , nn.BatchNormad      )
      if has_not_submodules:
           self.traced.append(UpperCAmelCase_      )







 def __call__(   self: str  , __UpperCamelCase: List[str]      ):

      '''simple docstring'''





      for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook      )      )
      self.module(UpperCAmelCase_      )
      [x.remove() for x in self.handles]
      return self







 @property
 def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

      '''simple docstring'''





      return list(filter(lambda __UpperCamelCase      : len(list(x.state_dict().keys()      )      ) > 0  , self.traced      )      )




@dataclass
class 				__UpperCamelCase				:
 _lowercase  :   nn.Module
 _lowercase  :   nn.Module
 _lowercase  :   int       				=  0
 _lowercase  :   List       				=  field(default_factory=__UpperCAmelCase			)
 _lowercase  :   List       				=  field(default_factory=__UpperCAmelCase			)







 def __call__(   self: List[str]  , __UpperCamelCase: Union[str, Any]      ):

      '''simple docstring'''





      __magic_name__							=    Tracker(self.dest      )(UpperCAmelCase_      ).parametrized
      __magic_name__							=    Tracker(self.src      )(UpperCAmelCase_      ).parametrized

      __magic_name__							=    list(filter(lambda __UpperCamelCase      : type(UpperCAmelCase_      ) not in self.src_skip  , UpperCAmelCase_      )      )
      __magic_name__							=    list(filter(lambda __UpperCamelCase      : type(UpperCAmelCase_      ) not in self.dest_skip  , UpperCAmelCase_      )      )

      if len(UpperCAmelCase_      ) != len(UpperCAmelCase_      ):
           raise Exception(
               F'Numbers of operations are different. Source module has {len(UpperCAmelCase_      )} operations while'
               F' destination module has {len(UpperCAmelCase_      )}.'      )

      for dest_m, src_m in zip(UpperCAmelCase_  , UpperCAmelCase_      ):
           dest_m.load_state_dict(src_m.state_dict()      )
           if self.verbose == 1:
                print(F'Transfered from={src_m} to={dest_m}'      )




def 					_lowercase     (				a_				:					List[str]					,a_				:					str					,a_				:					List[str]					,a_				:					str = True				)				->	Union[str, Any]:




     '''simple docstring'''







     print(F'Converting {name}...'				)
     with torch.no_grad():
          __magic_name__							=    timm.create_model(_snake_case					,pretrained=_snake_case				).eval()
          __magic_name__							=    ResNetForImageClassification(_snake_case				).eval()
          __magic_name__							=    ModuleTransfer(src=_snake_case					,dest=_snake_case				)
          __magic_name__							=    torch.randn((1, 3, 2_2_4, 2_2_4)				)
          module_transfer(_snake_case				)

     assert torch.allclose(from_model(_snake_case				)					,our_model(_snake_case				).logits				), "The model logits don't match the original one."

     __magic_name__							=    F'resnet{"-".join(name.split("resnet"				)				)}'
     print(_snake_case				)

     if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name					,commit_message='Add model'					,use_temp_dir=_snake_case					,)

          # we can use the convnext one
          __magic_name__							=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name					,commit_message='Add image processor'					,use_temp_dir=_snake_case					,)

          print(F'Pushed {checkpoint_name}'				)




def 					_lowercase     (				a_				:					List[str]					,a_				:					List[str] = None					,a_				:					int = True				)				->	Optional[int]:




     '''simple docstring'''







     __magic_name__							=    'imagenet-1k-id2label.json'
     __magic_name__							=    1_0_0_0
     __magic_name__							=    (1, num_labels)

     __magic_name__							=    'huggingface/label-files'
     __magic_name__							=    num_labels
     __magic_name__							=    json.load(open(hf_hub_download(_snake_case					,_snake_case					,repo_type='dataset'				)					,'r'				)				)
     __magic_name__							=    {int(_snake_case				): v for k, v in idalabel.items()}

     __magic_name__							=    idalabel
     __magic_name__							=    {v: k for k, v in idalabel.items()}

     __magic_name__							=    partial(_snake_case					,num_labels=_snake_case					,idalabel=_snake_case					,labelaid=_snake_case				)

     __magic_name__							=    {
         'resnet18': ImageNetPreTrainedConfig(
             depths=[2, 2, 2, 2]					,hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]					,layer_type='basic'				),
         'resnet26': ImageNetPreTrainedConfig(
             depths=[2, 2, 2, 2]					,hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]					,layer_type='bottleneck'				),
         'resnet34': ImageNetPreTrainedConfig(
             depths=[3, 4, 6, 3]					,hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]					,layer_type='basic'				),
         'resnet50': ImageNetPreTrainedConfig(
             depths=[3, 4, 6, 3]					,hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]					,layer_type='bottleneck'				),
         'resnet101': ImageNetPreTrainedConfig(
             depths=[3, 4, 2_3, 3]					,hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]					,layer_type='bottleneck'				),
         'resnet152': ImageNetPreTrainedConfig(
             depths=[3, 8, 3_6, 3]					,hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]					,layer_type='bottleneck'				),
     }

     if model_name:
          convert_weight_and_push(_snake_case					,names_to_config[model_name]					,_snake_case					,_snake_case				)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(_snake_case					,_snake_case					,_snake_case					,_snake_case				)
     return config, expected_shape


if __name__ == "__main__":
      A__						=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default=None,
          type=str,
          help=(
              "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
              " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=Path,
          required=True,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument(
          "--push_to_hub",
          default=True,
          type=bool,
          required=False,
          help="If True, push model and image processor to the hub.",
      )

      A__						=							parser.parse_args()
      A__						=							args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







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







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						torch.manual_seed(0      )
						__magic_name__							=    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  , )
						__magic_name__							=    DDIMScheduler(
						    beta_start=0.00085  , beta_end=0.012  , beta_schedule='scaled_linear'  , clip_sample=__UpperCamelCase  , set_alpha_to_one=__UpperCamelCase  , )
						__magic_name__							=    DDIMInverseScheduler(
						    beta_start=0.00085  , beta_end=0.012  , beta_schedule='scaled_linear'  , clip_sample=__UpperCamelCase  , set_alpha_to_zero=__UpperCamelCase  , )
						torch.manual_seed(0      )
						__magic_name__							=    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=1_28  , )
						torch.manual_seed(0      )
						__magic_name__							=    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=10_00  , hidden_act='gelu'  , projection_dim=5_12  , )
						__magic_name__							=    CLIPTextModel(__UpperCamelCase      )
						__magic_name__							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

						__magic_name__							=    {
						    '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 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: List[Any]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 16, 16)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    floats_tensor((1, 2, 4, 16, 16)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: str  , __UpperCamelCase: Optional[int]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 3, 32, 32)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    image.cpu().permute(0  , 2  , 3  , 1      )[0]
						__magic_name__							=    Image.fromarray(np.uinta(__UpperCamelCase      )      ).convert('RGB'      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Optional[int]=0      ):

						'''simple docstring'''





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

						'''simple docstring'''





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

						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    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}      )

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    pipe(**__UpperCamelCase      )[0]

						with tempfile.TemporaryDirectory() as tmpdir:
											pipe.save_pretrained(__UpperCamelCase      )
											__magic_name__							=    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.'  , )

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    pipe_loaded(**__UpperCamelCase      )[0]

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

						self.assertEqual(image.shape  , (2, 32, 32, 3)      )
						__magic_name__							=    np.array(
						    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]  , )
						__magic_name__							=    np.abs(image_slice.flatten() - expected_slice      ).max()
						self.assertLessEqual(__UpperCamelCase  , 1E-3      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()

						__magic_name__							=    {'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
						__magic_name__							=    DPMSolverMultistepScheduler(**__UpperCamelCase      )
						__magic_name__							=    DPMSolverMultistepInverseScheduler(**__UpperCamelCase      )

						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

						self.assertEqual(image.shape  , (2, 32, 32, 3)      )
						__magic_name__							=    np.array(
						    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]  , )
						__magic_name__							=    np.abs(image_slice.flatten() - expected_slice      ).max()
						self.assertLessEqual(__UpperCamelCase  , 1E-3      )




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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





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







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: List[str]      ):

						'''simple docstring'''





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

						__magic_name__							=    raw_image.convert('RGB'      ).resize((7_68, 7_68)      )

						__magic_name__							=    raw_image







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    torch.manual_seed(0      )

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

						__magic_name__							=    'a bowl of fruit'
						__magic_name__							=    'a bowl of pears'

						__magic_name__							=    pipe.generate_mask(
						    image=self.raw_image  , source_prompt=__UpperCamelCase  , target_prompt=__UpperCamelCase  , generator=__UpperCamelCase  , )

						__magic_name__							=    pipe.invert(
						    prompt=__UpperCamelCase  , image=self.raw_image  , inpaint_strength=0.7  , generator=__UpperCamelCase      ).latents

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

						__magic_name__							=    (
						    np.array(
						        load_image(
						            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						            '/diffedit/pears.png'      ).resize((7_68, 7_68)      )      )
						    / 2_55
						)
						assert np.abs((expected_image - image).max()      ) < 5E-1







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    torch.manual_seed(0      )

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

						__magic_name__							=    'a bowl of fruit'
						__magic_name__							=    'a bowl of pears'

						__magic_name__							=    pipe.generate_mask(
						    image=self.raw_image  , source_prompt=__UpperCamelCase  , target_prompt=__UpperCamelCase  , generator=__UpperCamelCase  , )

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

						__magic_name__							=    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]

						__magic_name__							=    (
						    np.array(
						        load_image(
						            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						            '/diffedit/pears.png'      ).resize((7_68, 7_68)      )      )
						    / 2_55
						)
						assert np.abs((expected_image - image).max()      ) < 5E-1




