





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			Any											=	OmegaConf.load(__a   )
	snake_case_		:			Dict											=	torch.load(__a   , map_location='cpu'   )['model']
	snake_case_		:			Union[str, Any]											=	list(state_dict.keys()   )

	# extract state_dict for VQVAE
	snake_case_		:			int											=	{}
	snake_case_		:			List[Any]											=	'first_stage_model.'
	for key in keys:
		if key.startswith(__a   ):
			snake_case_		:			List[Any]											=	state_dict[key]

    # extract state_dict for UNetLDM
	snake_case_		:			Optional[Any]											=	{}
	snake_case_		:			List[str]											=	'model.diffusion_model.'
	for key in keys:
		if key.startswith(__a   ):
			snake_case_		:			Optional[int]											=	state_dict[key]

	snake_case_		:			Optional[Any]											=	config.model.params.first_stage_config.params
	snake_case_		:			int											=	config.model.params.unet_config.params

	snake_case_		:			int											=	VQModel(**__a   ).eval()
	vqvae.load_state_dict(__a   )

	snake_case_		:			Optional[int]											=	UNetLDMModel(**__a   ).eval()
	unet.load_state_dict(__a   )

	snake_case_		:			Any											=	DDIMScheduler(
	    timesteps=config.model.params.timesteps   , beta_schedule='scaled_linear'   , beta_start=config.model.params.linear_start   , beta_end=config.model.params.linear_end   , clip_sample=__a   , )

	snake_case_		:			List[str]											=	LDMPipeline(__a   , __a   , __a   )
	pipeline.save_pretrained(__a   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						parser.add_argument("""--checkpoint_path""", type=str, required=True)
						parser.add_argument("""--config_path""", type=str, required=True)
						parser.add_argument("""--output_path""", type=str, required=True)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()

						convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)








import numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	return (2 / (1 + np.exp(-2 * vector   ))) - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

