




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					OmegaConf.load(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					torch.load(SCREAMING_SNAKE_CASE					,	map_location="""cpu"""   )["""model"""]
			SCREAMING_SNAKE_CASE           =					list(state_dict.keys()   )

			# extract state_dict for VQVAE
			SCREAMING_SNAKE_CASE           =					{}
			SCREAMING_SNAKE_CASE           =					"""first_stage_model."""
			for key in keys:
						if key.startswith(SCREAMING_SNAKE_CASE   ):
									SCREAMING_SNAKE_CASE           =					state_dict[key]

    # extract state_dict for UNetLDM
			SCREAMING_SNAKE_CASE           =					{}
			SCREAMING_SNAKE_CASE           =					"""model.diffusion_model."""
			for key in keys:
						if key.startswith(SCREAMING_SNAKE_CASE   ):
									SCREAMING_SNAKE_CASE           =					state_dict[key]

			SCREAMING_SNAKE_CASE           =					config.model.params.first_stage_config.params
			SCREAMING_SNAKE_CASE           =					config.model.params.unet_config.params

			SCREAMING_SNAKE_CASE           =					VQModel(**SCREAMING_SNAKE_CASE   ).eval()
			vqvae.load_state_dict(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					UNetLDMModel(**SCREAMING_SNAKE_CASE   ).eval()
			unet.load_state_dict(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					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=SCREAMING_SNAKE_CASE					,	)

			SCREAMING_SNAKE_CASE           =					LDMPipeline(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			pipeline.save_pretrained(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
		__A						:							List[Any]							=  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)
		__A						:							Optional[Any]							=  parser.parse_args()

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












def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if not all(char in """01""" for char in bin_string   ):
						raise ValueError("""Non-binary value was passed to the function"""   )
			if not bin_string:
						raise ValueError("""Empty string was passed to the function"""   )
			SCREAMING_SNAKE_CASE           =					""""""
			while len(SCREAMING_SNAKE_CASE   ) % 3 != 0:
						SCREAMING_SNAKE_CASE           =					"""0""" + bin_string
			SCREAMING_SNAKE_CASE           =					[
			    bin_string[index : index + 3]
			    for index in range(len(SCREAMING_SNAKE_CASE   )   )
			    if index % 3 == 0
			]
			for bin_group in bin_string_in_3_list:
						SCREAMING_SNAKE_CASE           =					0
						for index, val in enumerate(SCREAMING_SNAKE_CASE   ):
									oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE   )   )
						oct_string += str(SCREAMING_SNAKE_CASE   )
			return oct_string


if __name__ == "__main__":
		from doctest import testmod

		testmod()






