
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       logging.get_logger(__name__)
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       DetaConfig(
					    num_queries=9_00							,					encoder_ffn_dim=20_48							,					decoder_ffn_dim=20_48							,					num_feature_levels=5							,					assign_first_stage=__magic_name__							,					with_box_refine=__magic_name__							,					two_stage=__magic_name__							,					)

					# set labels
					lowerCAmelCase						:   Tuple							=       91
					lowerCAmelCase						:   Tuple							=       '''huggingface/label-files'''
					lowerCAmelCase						:   int							=       '''coco-detection-id2label.json'''
					lowerCAmelCase						:   List[Any]							=       json.load(open(cached_download(hf_hub_url(__magic_name__							,					__magic_name__							,					repo_type='''dataset'''					)					)							,					'''r'''					)					)
					lowerCAmelCase						:   List[Any]							=       {int(__magic_name__					): v for k, v in idalabel.items()}
					lowerCAmelCase						:   int							=       idalabel
					lowerCAmelCase						:   Optional[Any]							=       {v: k for k, v in idalabel.items()}

					return config
def       UpperCAmelCase__							(    __magic_name__  :       str					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       []

					# stem
					# fmt: off
					rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''')					)
					rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''')					)
					rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''')					)
					rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''')					)
					rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.model.embedder.embedder.normalization.running_var''')					)
					# stages
					for stage_idx in range(len(config.backbone_config.depths					)					):
										for layer_idx in range(config.backbone_config.depths[stage_idx]					):
															# shortcut
															if layer_idx == 0:
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																				    )					)
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																				    )					)
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																				    )					)
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																				    )					)
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																				    )					)
															# 3 convs
															for i in range(3					):
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																				    )					)
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																				    )					)
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																				    )					)
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																				    )					)
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																				    )					)
    # transformer encoder
					for i in range(config.encoder_layers					):
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')					)
										rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')					)

					# transformer decoder
					for i in range(config.decoder_layers					):
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')					)
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')					)

					# fmt: on

					return rename_keys
def       UpperCAmelCase__							(    __magic_name__  :       List[str]							,					__magic_name__  :       List[str]							,					__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					lowerCAmelCase						:   Dict							=       dct.pop(__magic_name__					)
					lowerCAmelCase						:   Union[str, Any]							=       val
def       UpperCAmelCase__							(    __magic_name__  :       List[Any]							,					__magic_name__  :       int					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       config.d_model
					for i in range(config.decoder_layers					):
										# read in weights + bias of input projection layer of self-attention
										lowerCAmelCase						:   Any							=       state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
										lowerCAmelCase						:   Dict							=       state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''					)
										# next, add query, keys and values (in that order) to the state dict
										lowerCAmelCase						:   Tuple							=       in_proj_weight[:hidden_size, :]
										lowerCAmelCase						:   Any							=       in_proj_bias[:hidden_size]
										lowerCAmelCase						:   Dict							=       in_proj_weight[
										    hidden_size : hidden_size * 2, :
										]
										lowerCAmelCase						:   Union[str, Any]							=       in_proj_bias[hidden_size : hidden_size * 2]
										lowerCAmelCase						:   Dict							=       in_proj_weight[-hidden_size:, :]
										lowerCAmelCase						:   Dict							=       in_proj_bias[-hidden_size:]
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowerCAmelCase						:   Optional[int]							=       Image.open(requests.get(__magic_name__							,					stream=__magic_name__					).raw					)

					return im
@torch.no_grad()
def       UpperCAmelCase__							(    __magic_name__  :       int							,					__magic_name__  :       int							,					__magic_name__  :       int					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       get_deta_config()

					# load original state dict
					if model_name == "deta-resnet-50":
										lowerCAmelCase						:   Any							=       '''adet_checkpoint0011.pth'''
					elif model_name == "deta-resnet-50-24-epochs":
										lowerCAmelCase						:   List[Any]							=       '''adet_2x_checkpoint0023.pth'''
					else:
										raise ValueError(f'''Model name {model_name} not supported'''					)
					lowerCAmelCase						:   List[Any]							=       hf_hub_download(repo_id='''nielsr/deta-checkpoints'''							,					filename=__magic_name__					)
					lowerCAmelCase						:   str							=       torch.load(__magic_name__							,					map_location='''cpu'''					)['''model''']

					# rename keys
					lowerCAmelCase						:   str							=       create_rename_keys(__magic_name__					)
					for src, dest in rename_keys:
										rename_key(__magic_name__							,					__magic_name__							,					__magic_name__					)
					read_in_decoder_q_k_v(__magic_name__							,					__magic_name__					)

					# fix some prefixes
					for key in state_dict.copy().keys():
										if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
															lowerCAmelCase						:   str							=       state_dict.pop(__magic_name__					)
															lowerCAmelCase						:   Any							=       val
										if "input_proj" in key:
															lowerCAmelCase						:   Optional[Any]							=       state_dict.pop(__magic_name__					)
															lowerCAmelCase						:   Tuple							=       val
										if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
															lowerCAmelCase						:   int							=       state_dict.pop(__magic_name__					)
															lowerCAmelCase						:   List[Any]							=       val

    # finally, create HuggingFace model and load state dict
					lowerCAmelCase						:   Optional[int]							=       DetaForObjectDetection(__magic_name__					)
					model.load_state_dict(__magic_name__					)
					model.eval()

					lowerCAmelCase						:   List[str]							=       '''cuda''' if torch.cuda.is_available() else '''cpu'''
					model.to(__magic_name__					)

					# load image processor
					lowerCAmelCase						:   Union[str, Any]							=       DetaImageProcessor(format='''coco_detection'''					)

					# verify our conversion on image
					lowerCAmelCase						:   List[Any]							=       prepare_img()
					lowerCAmelCase						:   str							=       processor(images=__magic_name__							,					return_tensors='''pt'''					)
					lowerCAmelCase						:   Optional[Any]							=       encoding['''pixel_values''']
					lowerCAmelCase						:   Dict							=       model(pixel_values.to(__magic_name__					)					)

					# verify logits
					if model_name == "deta-resnet-50":
										lowerCAmelCase						:   List[str]							=       torch.tensor(
										    [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]					)
										lowerCAmelCase						:   Optional[int]							=       torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]					)
					elif model_name == "deta-resnet-50-24-epochs":
										lowerCAmelCase						:   List[Any]							=       torch.tensor(
										    [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]					)
										lowerCAmelCase						:   Any							=       torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]					)

					assert torch.allclose(outputs.logits[0, :3, :3]							,					expected_logits.to(__magic_name__					)							,					atol=1e-4					)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,					expected_boxes.to(__magic_name__					)							,					atol=1e-4					)
					print('''Everything ok!'''					)

					if pytorch_dump_folder_path:
										# Save model and processor
										logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''					)
										Path(__magic_name__					).mkdir(exist_ok=__magic_name__					)
										model.save_pretrained(__magic_name__					)
										processor.save_pretrained(__magic_name__					)

					# Push to hub
					if push_to_hub:
										print('''Pushing model and processor to hub...'''					)
										model.push_to_hub(f'''jozhang97/{model_name}'''					)
										processor.push_to_hub(f'''jozhang97/{model_name}'''					)


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       argparse.ArgumentParser()

		parser.add_argument(
		    '--model_name',
		    type=str,
		    default='deta-resnet-50',
		    choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
		    help='Name of the model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    help='Path to the folder to output PyTorch model.',
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)
		__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 						__magic_name__				(  snake_case,			unittest.TestCase      ):
		_lowerCAmelCase						=		RePaintPipeline
		_lowerCAmelCase						=		IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
		_lowerCAmelCase						=		PipelineTesterMixin.required_optional_params - {
		    "latents",
		    "num_images_per_prompt",
		    "callback",
		    "callback_steps",
		}
		_lowerCAmelCase						=		IMAGE_INPAINTING_BATCH_PARAMS
		_lowerCAmelCase						=		False





		def  _A							(							self      :		Optional[int]			):
							torch.manual_seed(0			)
							torch.manual_seed(0			)
							lowerCAmelCase						:   Dict							=       UNetaDModel(
							    block_out_channels=(3_2, 6_4)  ,					layers_per_block=2  ,					sample_size=3_2  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')  ,					up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')  ,					)
							lowerCAmelCase						:   List[Any]							=       RePaintScheduler()
							lowerCAmelCase						:   Tuple							=       {'''unet''': unet, '''scheduler''': scheduler}
							return components





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		int=0			):
							if str(lowerCamelCase__			).startswith('''mps'''			):
												lowerCAmelCase						:   Union[str, Any]							=       torch.manual_seed(lowerCamelCase__			)
							else:
												lowerCAmelCase						:   Optional[Any]							=       torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       np.random.RandomState(lowerCamelCase__			).standard_normal((1, 3, 3_2, 3_2)			)
							lowerCAmelCase						:   Tuple							=       torch.from_numpy(lowerCamelCase__			).to(device=lowerCamelCase__  ,					dtype=torch.floataa			)
							lowerCAmelCase						:   Tuple							=       (image > 0).to(device=lowerCamelCase__  ,					dtype=torch.floataa			)
							lowerCAmelCase						:   List[Any]							=       {
							    '''image''': image,
							    '''mask_image''': mask,
							    '''generator''': generator,
							    '''num_inference_steps''': 5,
							    '''eta''': 0.0,
							    '''jump_length''': 2,
							    '''jump_n_sample''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   List[str]							=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase						:   Dict							=       self.get_dummy_components()
							lowerCAmelCase						:   str							=       RePaintPipeline(**lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       sd_pipe.to(lowerCamelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							lowerCAmelCase						:   List[Any]							=       self.get_dummy_inputs(lowerCamelCase__			)
							lowerCAmelCase						:   int							=       sd_pipe(**lowerCamelCase__			).images
							lowerCAmelCase						:   Dict							=       image[0, -3:, -3:, -1]

							assert image.shape == (1, 3_2, 3_2, 3)
							lowerCAmelCase						:   List[str]							=       np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3





		@skip_mps
		def  _A							(							self      :		Optional[Any]			):
							return super().test_save_load_local()





		@unittest.skip('''non-deterministic pipeline'''			)
		def  _A							(							self      :		Any			):
							return super().test_inference_batch_single_identical()





		@skip_mps
		def  _A							(							self      :		List[Any]			):
							return super().test_dict_tuple_outputs_equivalent()





		@skip_mps
		def  _A							(							self      :		Dict			):
							return super().test_save_load_optional_components()





		@skip_mps
		def  _A							(							self      :		Tuple			):
							return super().test_attention_slicing_forward_pass()



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





		def  _A							(							self      :		Optional[Any]			):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   str							=       load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
							    '''repaint/celeba_hq_256.png'''			)
							lowerCAmelCase						:   Tuple							=       load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''			)
							lowerCAmelCase						:   int							=       load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
							    '''repaint/celeba_hq_256_result.npy'''			)

							lowerCAmelCase						:   List[str]							=       '''google/ddpm-ema-celebahq-256'''
							lowerCAmelCase						:   List[str]							=       UNetaDModel.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   str							=       RePaintScheduler.from_pretrained(lowerCamelCase__			)

							lowerCAmelCase						:   Optional[int]							=       RePaintPipeline(unet=lowerCamelCase__  ,					scheduler=lowerCamelCase__			).to(lowerCamelCase__			)
							repaint.set_progress_bar_config(disable=lowerCamelCase__			)
							repaint.enable_attention_slicing()

							lowerCAmelCase						:   str							=       torch.manual_seed(0			)
							lowerCAmelCase						:   Dict							=       repaint(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					num_inference_steps=2_5_0  ,					eta=0.0  ,					jump_length=1_0  ,					jump_n_sample=1_0  ,					generator=lowerCamelCase__  ,					output_type='''np'''  ,					)
							lowerCAmelCase						:   str							=       output.images[0]

							assert image.shape == (2_5_6, 2_5_6, 3)
							assert np.abs(expected_image - image			).mean() < 1E-2
