


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class   A__							(						UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  PaintByExamplePipeline
						UpperCAmelCase									=  IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
						UpperCAmelCase									=  IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						UpperCAmelCase									=  frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




						def 							__UpperCamelCase (				self    :					Tuple     )							->   Any:


										"""simple docstring"""





										torch.manual_seed(0     )
										_SCREAMING_SNAKE_CASE	     =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 ,			)
										_SCREAMING_SNAKE_CASE	     =PNDMScheduler(skip_prk_steps=_a     )
										torch.manual_seed(0     )
										_SCREAMING_SNAKE_CASE	     =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 ,			)
										torch.manual_seed(0     )
										_SCREAMING_SNAKE_CASE	     =CLIPVisionConfig(
										    hidden_size=32 ,			projection_dim=32 ,			intermediate_size=37 ,			layer_norm_eps=1E-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			image_size=32 ,			patch_size=4 ,			)
										_SCREAMING_SNAKE_CASE	     =PaintByExampleImageEncoder(_a ,			proj_size=32     )
										_SCREAMING_SNAKE_CASE	     =CLIPImageProcessor(crop_size=32 ,			size=32     )

										_SCREAMING_SNAKE_CASE	     ={
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''image_encoder''': image_encoder,
										    '''safety_checker''': None,
										    '''feature_extractor''': feature_extractor,
										}
										return components




						def 							__UpperCamelCase (				self    :					Optional[Any] ,			_a    :					Dict     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =np.array(image.convert('''RGB'''     )     )
										_SCREAMING_SNAKE_CASE	     =image[None].transpose(0 ,			3 ,			1 ,			2     )
										_SCREAMING_SNAKE_CASE	     =torch.from_numpy(_a     ).to(dtype=torch.floataa     ) / 1_27.5 - 1.0
										return image




						def 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					Tuple="cpu" ,			_a    :					Optional[int]=0     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =floats_tensor((1, 3, 32, 32) ,			rng=random.Random(_a     )     ).to(_a     )
										_SCREAMING_SNAKE_CASE	     =image.cpu().permute(0 ,			2 ,			3 ,			1     )[0]
										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(_a     )     ).convert('''RGB'''     ).resize((64, 64)     )
										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(image + 4     )     ).convert('''RGB'''     ).resize((64, 64)     )
										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(_a     )     ).convert('''RGB'''     ).resize((32, 32)     )

										if str(_a     ).startswith('''mps'''     ):
														_SCREAMING_SNAKE_CASE	     =torch.manual_seed(_a     )
										else:
														_SCREAMING_SNAKE_CASE	     =torch.Generator(device=_a     ).manual_seed(_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''example_image''': example_image,
										    '''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    :					Optional[int]     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.get_dummy_components()

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE	     =PaintByExamplePipeline(**_a     )
										_SCREAMING_SNAKE_CASE	     =pipe.to('''cpu'''     )
										pipe.set_progress_bar_config(disable=_a     )

										_SCREAMING_SNAKE_CASE	     =self.get_dummy_inputs()
										_SCREAMING_SNAKE_CASE	     =pipe(**_a     )
										_SCREAMING_SNAKE_CASE	     =output.images

										_SCREAMING_SNAKE_CASE	     =image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										_SCREAMING_SNAKE_CASE	     =np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]     )

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




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ='''cpu'''
										_SCREAMING_SNAKE_CASE	     =self.get_dummy_inputs()
										inputs.pop('''mask_image'''     )
										_SCREAMING_SNAKE_CASE	     =self.convert_to_pt(inputs.pop('''image'''     )     )
										_SCREAMING_SNAKE_CASE	     =image.clamp(0 ,			1     ) / 2

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE	     =PaintByExamplePipeline(**self.get_dummy_components()     )
										_SCREAMING_SNAKE_CASE	     =pipe.to(_a     )
										pipe.set_progress_bar_config(disable=_a     )

										_SCREAMING_SNAKE_CASE	     =pipe(image=_a ,			mask_image=mask_image[:, 0] ,			**_a     )
										_SCREAMING_SNAKE_CASE	     =output.images

										_SCREAMING_SNAKE_CASE	     =image.cpu().permute(0 ,			2 ,			3 ,			1     )[0]
										_SCREAMING_SNAKE_CASE	     =mask_image.cpu().permute(0 ,			2 ,			3 ,			1     )[0]

										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(_a     )     ).convert('''RGB'''     )
										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(_a     )     ).convert('''RGB'''     )

										_SCREAMING_SNAKE_CASE	     =pipe(**self.get_dummy_inputs()     )
										_SCREAMING_SNAKE_CASE	     =output.images

										assert out_a.shape == (1, 64, 64, 3)
										assert np.abs(out_a.flatten() - out_a.flatten()     ).max() < 5E-2




						def 							__UpperCamelCase (				self    :					List[str]     )							->   Any:


										"""simple docstring"""





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

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




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Optional[Any]:


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					Dict     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/paint_by_example/dog_in_bucket.png'''     )
										_SCREAMING_SNAKE_CASE	     =load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/paint_by_example/mask.png'''     )
										_SCREAMING_SNAKE_CASE	     =load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/paint_by_example/panda.jpg'''     )

										_SCREAMING_SNAKE_CASE	     =PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''     )
										_SCREAMING_SNAKE_CASE	     =pipe.to(_a     )
										pipe.set_progress_bar_config(disable=_a     )

										_SCREAMING_SNAKE_CASE	     =torch.manual_seed(321     )
										_SCREAMING_SNAKE_CASE	     =pipe(
										    image=_a ,			mask_image=_a ,			example_image=_a ,			generator=_a ,			guidance_scale=5.0 ,			num_inference_steps=50 ,			output_type='''np''' ,			)

										_SCREAMING_SNAKE_CASE	     =output.images

										_SCREAMING_SNAKE_CASE	     =image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										_SCREAMING_SNAKE_CASE	     =np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]     )

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



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =UNetaDModel(**a__)
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-4]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-3]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-2]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-1]].data

				_SCREAMING_SNAKE_CASE	     =4

				def set_attention_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

				def set_resnet_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data

								if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

				for i, block in enumerate(new_model_architecture.downsample_blocks):
								_SCREAMING_SNAKE_CASE	     =hasattr(a__		,'''attentions''')
								for j in range(len(block.resnets)):
												set_resnet_weights(block.resnets[j]		,a__		,a__)
												module_index += 1
												if has_attentions:
																set_attention_weights(block.attentions[j]		,a__		,a__)
																module_index += 1

								if hasattr(a__		,'''downsamplers''') and block.downsamplers is not None:
												set_resnet_weights(block.resnet_down		,a__		,a__)
												module_index += 1
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
												module_index += 1

				set_resnet_weights(new_model_architecture.mid_block.resnets[0]		,a__		,a__)
				module_index += 1
				set_attention_weights(new_model_architecture.mid_block.attentions[0]		,a__		,a__)
				module_index += 1
				set_resnet_weights(new_model_architecture.mid_block.resnets[1]		,a__		,a__)
				module_index += 1

				for i, block in enumerate(new_model_architecture.up_blocks):
								_SCREAMING_SNAKE_CASE	     =hasattr(a__		,'''attentions''')
								for j in range(len(block.resnets)):
												set_resnet_weights(block.resnets[j]		,a__		,a__)
												module_index += 1
								if has_attentions:
												set_attention_weights(
												    block.attentions[0]		,a__		,a__)  # why can there only be a single attention layer for up?
												module_index += 1

								if hasattr(a__		,'''resnet_up''') and block.resnet_up is not None:
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												set_resnet_weights(block.resnet_up		,a__		,a__)
												module_index += 1

				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
				module_index += 1
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
							snake_case_  :	Tuple							  =							argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
							    type=str,
							    required=False,
							    help='''Path to the checkpoint to convert.''',
							)

							parser.add_argument(
							    '''--config_file''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
							    type=str,
							    required=False,
							    help='''The config json file corresponding to the architecture.''',
							)

							parser.add_argument(
							    '''--dump_path''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
							    type=str,
							    required=False,
							    help='''Path to the output model.''',
							)

							snake_case_  :	List[str]							  =							parser.parse_args()

							snake_case_  :	List[Any]							  =							torch.load(args.checkpoint_path, map_location='''cpu''')

							with open(args.config_file) as f:
														snake_case_  :	List[str]							  =							json.loads(f.read())

							snake_case_  :	Optional[int]							  =							convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

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

							snake_case_  :	Union[str, Any]							  =							UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														snake_case_  :	List[Any]							  =							ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														snake_case_  :	int							  =							ScoreSdeVePipeline(unet=model, scheduler=scheduler)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)