


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def 				UpperCamelCase      (			__magic_name__  :							int					,					__magic_name__  :							str  )     ->						Optional[Any]:
		"""simple docstring"""




		lowercase__      =	UNetaDModel(**__magic_name__  )
		lowercase__      =	checkpoint["""all_modules.0.W"""].data
		lowercase__      =	checkpoint["""all_modules.0.W"""].data
		lowercase__      =	checkpoint["""all_modules.1.weight"""].data
		lowercase__      =	checkpoint["""all_modules.1.bias"""].data

		lowercase__      =	checkpoint["""all_modules.2.weight"""].data
		lowercase__      =	checkpoint["""all_modules.2.bias"""].data

		lowercase__      =	checkpoint["""all_modules.3.weight"""].data
		lowercase__      =	checkpoint["""all_modules.3.bias"""].data

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

		lowercase__      =	4

		def set_attention_weights(__magic_name__  :							Dict					,					__magic_name__  :							Any					,					__magic_name__  :							Dict  ):
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

				lowercase__      =	old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

				lowercase__      =	old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

				lowercase__      =	old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

		def set_resnet_weights(__magic_name__  :							Optional[Any]					,					__magic_name__  :							Optional[int]					,					__magic_name__  :							str  ):
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

				lowercase__      =	old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
				lowercase__      =	old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

				lowercase__      =	old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
				lowercase__      =	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:
						lowercase__      =	old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
						lowercase__      =	old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

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

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

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

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

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

		lowercase__      =	checkpoint[f'''all_modules.{module_index}.weight'''].data
		lowercase__      =	checkpoint[f'''all_modules.{module_index}.bias'''].data
		module_index += 1
		lowercase__      =	checkpoint[f'''all_modules.{module_index}.weight'''].data
		lowercase__      =	checkpoint[f'''all_modules.{module_index}.bias'''].data

		return new_model_architecture.state_dict()


if __name__ == "__main__":
	A :	int												=     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.',
	)

	A :	int												=     parser.parse_args()

	A :	Optional[int]												=     torch.load(args.checkpoint_path, map_location='cpu')

	with open(args.config_file) as f:
		A :	int												=     json.loads(f.read())

	A :	int												=     convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

	A :	Tuple												=     UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		A :	Dict												=     ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

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





from __future__ import annotations






def 				UpperCamelCase      (			__magic_name__  :							list[list[int]]					,					__magic_name__  :							list[list[int]]  )     ->						list[int]:
		"""simple docstring"""




		assert len(__magic_name__  ) == len(__magic_name__  )

		lowercase__      =	len(__magic_name__  )
		lowercase__      =	list(range(__magic_name__  )  )
		lowercase__      =	[-1] * n  # who the donor has donated to
		lowercase__      =	[-1] * n  # who the recipient has received from
		lowercase__      =	[0] * n

		while unmatched_donors:
				lowercase__      =	unmatched_donors[0]
				lowercase__      =	donor_pref[donor]
				lowercase__      =	donor_preference[num_donations[donor]]
				num_donations[donor] += 1
				lowercase__      =	recipient_pref[recipient]
				lowercase__      =	rec_record[recipient]

				if prev_donor != -1:
						if rec_preference.index(__magic_name__  ) > rec_preference.index(__magic_name__  ):
								lowercase__      =	donor
								lowercase__      =	recipient
								unmatched_donors.append(__magic_name__  )
								unmatched_donors.remove(__magic_name__  )
				else:
						lowercase__      =	donor
						lowercase__      =	recipient
						unmatched_donors.remove(__magic_name__  )
		return donor_record

