






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[str]					,  snake_case_ :							Any					,  snake_case_ :							Dict       ):
	snake_case__			:				int       =  OmegaConf.load(snake_case_       )
	snake_case__			:				List[Any]       =  torch.load(snake_case_					,  map_location="cpu"       )["model"]
	snake_case__			:				str       =  list(state_dict.keys()       )

	# extract state_dict for VQVAE
	snake_case__			:				Union[str, Any]       =  {}
	snake_case__			:				Dict       =  "first_stage_model."
	for key in keys:
		if key.startswith(snake_case_       ):
			snake_case__			:				List[str]       =  state_dict[key]

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

	snake_case__			:				List[Any]       =  config.model.params.first_stage_config.params
	snake_case__			:				List[Any]       =  config.model.params.unet_config.params

	snake_case__			:				Any       =  VQModel(**snake_case_       ).eval()
	vqvae.load_state_dict(snake_case_       )

	snake_case__			:				Optional[Any]       =  UNetLDMModel(**snake_case_       ).eval()
	unet.load_state_dict(snake_case_       )

	snake_case__			:				List[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=snake_case_					,  )

	snake_case__			:				str       =  LDMPipeline(snake_case_					,  snake_case_					,  snake_case_       )
	pipeline.save_pretrained(snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      Union[str, 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)
	__lowerCamelCase :      List[Any]										=				parser.parse_args()

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











__lowerCamelCase :      Optional[int]										=				"""Tobias Carryer"""

from time import time



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  List[Any]							,		__A					:  List[Any]							,		__A					:  Optional[int]							,		__A					:  List[str]							,		__A					:  Dict=int(time()	)	):  # noqa: B008
						snake_case__			:				List[Any]       =  multiplier
						snake_case__			:				Optional[int]       =  increment
						snake_case__			:				Optional[int]       =  modulo
						snake_case__			:				Union[str, Any]       =  seed






					def 	_lowercase					( self					:  str	):
						snake_case__			:				Union[str, Any]       =  (self.multiplier * self.seed + self.increment) % self.modulo
						return self.seed


if __name__ == "__main__":
	# Show the LCG in action.
	__lowerCamelCase :      int										=				LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
	while True:
		print(lcg.next_number())



