


'''simple docstring'''



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]=1   ):
					if n_shave_prefix_segments >= 0:
										return ".".join(path.split('.'   )[n_shave_prefix_segments:]   )
					else:
										return ".".join(path.split('.'   )[:n_shave_prefix_segments]   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   int=0   ):
					__a			:       int					    =  []
					for old_item in old_list:
										__a			:       Optional[int]					    =  old_item.replace('in_layers.0'						,      'norm1'   )
										__a			:       Tuple					    =  new_item.replace('in_layers.2'						,      'conv1'   )

										__a			:       Any					    =  new_item.replace('out_layers.0'						,      'norm2'   )
										__a			:       Optional[int]					    =  new_item.replace('out_layers.3'						,      'conv2'   )

										__a			:       int					    =  new_item.replace('emb_layers.1'						,      'time_emb_proj'   )
										__a			:       Dict					    =  new_item.replace('skip_connection'						,      'conv_shortcut'   )

										__a			:       Tuple					    =  shave_segments(_SCREAMING_SNAKE_CASE						,      n_shave_prefix_segments=_SCREAMING_SNAKE_CASE   )

										mapping.append({'old': old_item, 'new': new_item}   )

					return mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Optional[int]=0   ):
					__a			:       Union[str, Any]					    =  []
					for old_item in old_list:
										__a			:       Any					    =  old_item

										__a			:       int					    =  new_item.replace('norm.weight'						,      'group_norm.weight'   )
										__a			:       Union[str, Any]					    =  new_item.replace('norm.bias'						,      'group_norm.bias'   )

										__a			:       Tuple					    =  new_item.replace('proj_out.weight'						,      'proj_attn.weight'   )
										__a			:       List[str]					    =  new_item.replace('proj_out.bias'						,      'proj_attn.bias'   )

										__a			:       int					    =  shave_segments(_SCREAMING_SNAKE_CASE						,      n_shave_prefix_segments=_SCREAMING_SNAKE_CASE   )

										mapping.append({'old': old_item, 'new': new_item}   )

					return mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]=None						,      _SCREAMING_SNAKE_CASE      :   int=None						,      _SCREAMING_SNAKE_CASE      :   Dict=None   ):
					assert isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ), "Paths should be a list of dicts containing 'old' and 'new' keys."

					# Splits the attention layers into three variables.
					if attention_paths_to_split is not None:
										for path, path_map in attention_paths_to_split.items():
															__a			:       int					    =  old_checkpoint[path]
															__a			:       Any					    =  old_tensor.shape[0] // 3

															__a			:       Any					    =  (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)

															__a			:       Dict					    =  old_tensor.shape[0] // config['num_head_channels'] // 3

															__a			:       Dict					    =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]   )
															__a    ,				__a    ,				__a			:       Dict					    =  old_tensor.split(channels // num_heads						,      dim=1   )

															__a			:       Optional[int]					    =  query.reshape(_SCREAMING_SNAKE_CASE   )
															__a			:       Optional[Any]					    =  key.reshape(_SCREAMING_SNAKE_CASE   )
															__a			:       Union[str, Any]					    =  value.reshape(_SCREAMING_SNAKE_CASE   )

					for path in paths:
										__a			:       Optional[int]					    =  path['new']

										# These have already been assigned
										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

										# Global renaming happens here
										__a			:       List[str]					    =  new_path.replace('middle_block.0'						,      'mid_block.resnets.0'   )
										__a			:       Any					    =  new_path.replace('middle_block.1'						,      'mid_block.attentions.0'   )
										__a			:       Union[str, Any]					    =  new_path.replace('middle_block.2'						,      'mid_block.resnets.1'   )

										if additional_replacements is not None:
															for replacement in additional_replacements:
																				__a			:       Optional[int]					    =  new_path.replace(replacement['old']						,      replacement['new']   )

        # proj_attn.weight has to be converted from conv 1D to linear
										if "proj_attn.weight" in new_path:
															__a			:       str					    =  old_checkpoint[path['old']][:, :, 0]
										else:
															__a			:       Optional[Any]					    =  old_checkpoint[path['old']]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       Optional[Any]					    =  {}

					__a			:       Dict					    =  checkpoint['time_embed.0.weight']
					__a			:       Any					    =  checkpoint['time_embed.0.bias']
					__a			:       Any					    =  checkpoint['time_embed.2.weight']
					__a			:       Optional[Any]					    =  checkpoint['time_embed.2.bias']

					__a			:       int					    =  checkpoint['input_blocks.0.0.weight']
					__a			:       Union[str, Any]					    =  checkpoint['input_blocks.0.0.bias']

					__a			:       List[str]					    =  checkpoint['out.0.weight']
					__a			:       int					    =  checkpoint['out.0.bias']
					__a			:       List[Any]					    =  checkpoint['out.2.weight']
					__a			:       Optional[int]					    =  checkpoint['out.2.bias']

					# Retrieves the keys for the input blocks only
					__a			:       int					    =  len({'.'.join(layer.split('.'   )[:2]   ) for layer in checkpoint if 'input_blocks' in layer}   )
					__a			:       Any					    =  {
					    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
					    for layer_id in range(_SCREAMING_SNAKE_CASE   )
					}

					# Retrieves the keys for the middle blocks only
					__a			:       str					    =  len({'.'.join(layer.split('.'   )[:2]   ) for layer in checkpoint if 'middle_block' in layer}   )
					__a			:       Optional[int]					    =  {
					    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
					    for layer_id in range(_SCREAMING_SNAKE_CASE   )
					}

					# Retrieves the keys for the output blocks only
					__a			:       str					    =  len({'.'.join(layer.split('.'   )[:2]   ) for layer in checkpoint if 'output_blocks' in layer}   )
					__a			:       Tuple					    =  {
					    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
					    for layer_id in range(_SCREAMING_SNAKE_CASE   )
					}

					for i in range(1						,      _SCREAMING_SNAKE_CASE   ):
										__a			:       Tuple					    =  (i - 1) // (config['num_res_blocks'] + 1)
										__a			:       Optional[int]					    =  (i - 1) % (config['num_res_blocks'] + 1)

										__a			:       Dict					    =  [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
										__a			:       Optional[Any]					    =  [key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

										if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
															__a			:       Tuple					    =  checkpoint[
															    F"""input_blocks.{i}.0.op.weight"""
															]
															__a			:       int					    =  checkpoint[
															    F"""input_blocks.{i}.0.op.bias"""
															]
															continue

										__a			:       str					    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE   )
										__a			:       str					    =  {'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
										__a			:       List[Any]					    =  {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
										assign_to_checkpoint(
										    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      additional_replacements=[meta_path, resnet_op]						,      config=_SCREAMING_SNAKE_CASE   )

										if len(_SCREAMING_SNAKE_CASE   ):
															__a			:       Any					    =  renew_attention_paths(_SCREAMING_SNAKE_CASE   )
															__a			:       Optional[int]					    =  {
															    'old': F"""input_blocks.{i}.1""",
															    'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
															}
															__a			:       Tuple					    =  {
															    F"""input_blocks.{i}.1.qkv.bias""": {
															        'key': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
															        'query': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
															        'value': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
															    },
															    F"""input_blocks.{i}.1.qkv.weight""": {
															        'key': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
															        'query': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
															        'value': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
															    },
															}
															assign_to_checkpoint(
															    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      additional_replacements=[meta_path]						,      attention_paths_to_split=_SCREAMING_SNAKE_CASE						,      config=_SCREAMING_SNAKE_CASE						,      )

					__a			:       Dict					    =  middle_blocks[0]
					__a			:       List[Any]					    =  middle_blocks[1]
					__a			:       Optional[Any]					    =  middle_blocks[2]

					__a			:       Union[str, Any]					    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE   )
					assign_to_checkpoint(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      config=_SCREAMING_SNAKE_CASE   )

					__a			:       Any					    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE   )
					assign_to_checkpoint(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      config=_SCREAMING_SNAKE_CASE   )

					__a			:       List[str]					    =  renew_attention_paths(_SCREAMING_SNAKE_CASE   )
					__a			:       List[str]					    =  {
					    'middle_block.1.qkv.bias': {
					        'key': 'mid_block.attentions.0.key.bias',
					        'query': 'mid_block.attentions.0.query.bias',
					        'value': 'mid_block.attentions.0.value.bias',
					    },
					    'middle_block.1.qkv.weight': {
					        'key': 'mid_block.attentions.0.key.weight',
					        'query': 'mid_block.attentions.0.query.weight',
					        'value': 'mid_block.attentions.0.value.weight',
					    },
					}
					assign_to_checkpoint(
					    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      attention_paths_to_split=_SCREAMING_SNAKE_CASE						,      config=_SCREAMING_SNAKE_CASE   )

					for i in range(_SCREAMING_SNAKE_CASE   ):
										__a			:       Dict					    =  i // (config['num_res_blocks'] + 1)
										__a			:       Tuple					    =  i % (config['num_res_blocks'] + 1)
										__a			:       int					    =  [shave_segments(_SCREAMING_SNAKE_CASE						,      2   ) for name in output_blocks[i]]
										__a			:       Dict					    =  {}

										for layer in output_block_layers:
															__a    ,				__a			:       Optional[int]					    =  layer.split('.'   )[0], shave_segments(_SCREAMING_SNAKE_CASE						,      1   )
															if layer_id in output_block_list:
																				output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE   )
															else:
																				__a			:       Union[str, Any]					    =  [layer_name]

										if len(_SCREAMING_SNAKE_CASE   ) > 1:
															__a			:       List[str]					    =  [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
															__a			:       List[str]					    =  [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

															__a			:       Optional[int]					    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE   )
															__a			:       Optional[Any]					    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE   )

															__a			:       Dict					    =  {'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
															assign_to_checkpoint(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      additional_replacements=[meta_path]						,      config=_SCREAMING_SNAKE_CASE   )

															if ["conv.weight", "conv.bias"] in output_block_list.values():
																				__a			:       Optional[Any]					    =  list(output_block_list.values()   ).index(['conv.weight', 'conv.bias']   )
																				__a			:       List[Any]					    =  checkpoint[
																				    F"""output_blocks.{i}.{index}.conv.weight"""
																				]
																				__a			:       str					    =  checkpoint[
																				    F"""output_blocks.{i}.{index}.conv.bias"""
																				]

																				# Clear attentions as they have been attributed above.
																				if len(_SCREAMING_SNAKE_CASE   ) == 2:
																									__a			:       str					    =  []

															if len(_SCREAMING_SNAKE_CASE   ):
																				__a			:       List[str]					    =  renew_attention_paths(_SCREAMING_SNAKE_CASE   )
																				__a			:       List[str]					    =  {
																				    'old': F"""output_blocks.{i}.1""",
																				    'new': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																				}
																				__a			:       Optional[int]					    =  {
																				    F"""output_blocks.{i}.1.qkv.bias""": {
																				        'key': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																				        'query': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																				        'value': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																				    },
																				    F"""output_blocks.{i}.1.qkv.weight""": {
																				        'key': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																				        'query': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																				        'value': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																				    },
																				}
																				assign_to_checkpoint(
																				    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      additional_replacements=[meta_path]						,      attention_paths_to_split=to_split if any('qkv' in key for key in attentions   ) else None						,      config=_SCREAMING_SNAKE_CASE						,      )
										else:
															__a			:       Optional[int]					    =  renew_resnet_paths(_SCREAMING_SNAKE_CASE						,      n_shave_prefix_segments=1   )
															for path in resnet_0_paths:
																				__a			:       List[str]					    =  '.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE   ), path['old']]   )
																				__a			:       Optional[int]					    =  '.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE   ), 'resnets', str(_SCREAMING_SNAKE_CASE   ), path['new']]   )

																				__a			:       List[str]					    =  checkpoint[old_path]

					return new_checkpoint


if __name__ == "__main__":
	__lowercase	:						str       =	argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)

	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The config json file corresponding to the architecture.',
	)

	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

	__lowercase	:						Dict       =	parser.parse_args()

	__lowercase	:						str       =	torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		__lowercase	:						Tuple       =	json.loads(f.read())

	__lowercase	:						List[Any]       =	convert_ldm_checkpoint(checkpoint, config)

	if "ldm" in config:
		del config["ldm"]

	__lowercase	:						List[str]       =	UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__lowercase	:						List[str]       =	DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
		__lowercase	:						List[Any]       =	VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

		__lowercase	:						Optional[int]       =	LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)







'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

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


enable_full_determinism()





class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    StableDiffusionInpaintPipeline
	A_				=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	A_				=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	A_				=    frozenset(
	    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	A_				=    frozenset([]					)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       int					    =  UNetaDConditionModel(
						    block_out_channels=(32, 64)						,				layers_per_block=2						,				sample_size=32						,				in_channels=9						,				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			:       str					    =  PNDMScheduler(skip_prk_steps=__a							)
						torch.manual_seed(0							)
						__a			:       Union[str, Any]					    =  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			:       List[str]					    =  CLIPTextConfig(
						    bos_token_id=0						,				eos_token_id=2						,				hidden_size=32						,				intermediate_size=37						,				layer_norm_eps=1E-0_5						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				hidden_act='gelu'						,				projection_dim=512						,				)
						__a			:       Dict					    =  CLIPTextModel(__a							)
						__a			:       Union[str, Any]					    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						__a			:       Union[str, Any]					    =  {
						    'unet': unet,
						    'scheduler': 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			:       Union[str, Any]					    =  floats_tensor((1, 3, 32, 32)						,				rng=random.Random(__a							)							).to(__a							)
						__a			:       List[Any]					    =  image.cpu().permute(0						,				2						,				3						,				1							)[0]
						__a			:       Tuple					    =  Image.fromarray(np.uinta(__a							)							).convert('RGB'							).resize((64, 64)							)
						__a			:       Tuple					    =  Image.fromarray(np.uinta(image + 4							)							).convert('RGB'							).resize((64, 64)							)
						if str(__a							).startswith('mps'							):
											__a			:       Any					    =  torch.manual_seed(__a							)
						else:
											__a			:       str					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Dict					    =  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': init_image,
						    'mask_image': mask_image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						}
						return inputs




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

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

						assert image.shape == (1, 64, 64, 3)
						__a			:       List[Any]					    =  np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

						assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       List[str]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
						__a			:       str					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench.npy'							)

						__a			:       Optional[int]					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       Optional[int]					    =  StableDiffusionInpaintPipeline.from_pretrained(__a						,				safety_checker=__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Dict					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       int					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       Dict					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 9E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
						__a			:       Any					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'							)

						__a			:       str					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       List[str]					    =  StableDiffusionInpaintPipeline.from_pretrained(
						    __a						,				torch_dtype=torch.floataa						,				safety_checker=__a						,				)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Union[str, Any]					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       int					    =  torch.manual_seed(0							)
						__a			:       Optional[Any]					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       int					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 5E-1





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						__a			:       str					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       List[Any]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)

						__a			:       str					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       Any					    =  PNDMScheduler.from_pretrained(__a						,				subfolder='scheduler'							)
						__a			:       str					    =  StableDiffusionInpaintPipeline.from_pretrained(
						    __a						,				safety_checker=__a						,				scheduler=__a						,				torch_dtype=torch.floataa						,				)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing(1							)
						pipe.enable_sequential_cpu_offload()

						__a			:       str					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       str					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				num_inference_steps=2						,				output_type='np'						,				)

						__a			:       List[str]					    =  torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.65 * 10**9



