


import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






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




		lowercase__					      =		OmegaConf.load(__magic_name__					)
		lowercase__					      =		torch.load(__magic_name__   ,   map_location="""cpu"""					)["""model"""]
		lowercase__					      =		list(state_dict.keys()					)

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

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

		lowercase__					      =		config.model.params.first_stage_config.params
		lowercase__					      =		config.model.params.unet_config.params

		lowercase__					      =		VQModel(**__magic_name__					).eval()
		vqvae.load_state_dict(__magic_name__					)

		lowercase__					      =		UNetLDMModel(**__magic_name__					).eval()
		unet.load_state_dict(__magic_name__					)

		lowercase__					      =		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=__magic_name__   ,   )

		lowercase__					      =		LDMPipeline(__magic_name__   ,   __magic_name__   ,   __magic_name__					)
		pipeline.save_pretrained(__magic_name__					)


if __name__ == "__main__":
	A	:		str    =       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	:		List[str]    =       parser.parse_args()

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





def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  list:
		"""simple docstring"""




		lowercase__					      =		len(__magic_name__					)
		lowercase__					      =		[]
		for i in range(len(__magic_name__					) - pat_len + 1					):
				lowercase__					      =		True
				for j in range(__magic_name__					):
						if s[i + j] != pattern[j]:
								lowercase__					      =		False
								break
				if match_found:
						position.append(__magic_name__					)
		return position


if __name__ == "__main__":
	assert naive_pattern_search('ABCDEFG', 'DE') == [3]
	print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))

