






'''simple docstring'''







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  OmegaConf.load(__lowerCAmelCase						)
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)["""model"""]
						lowerCamelCase__         =  list(state_dict.keys()						)

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

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

						lowerCamelCase__         =  config.model.params.first_stage_config.params
						lowerCamelCase__         =  config.model.params.unet_config.params

						lowerCamelCase__         =  VQModel(**__lowerCAmelCase						).eval()
						vqvae.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  UNetLDMModel(**__lowerCAmelCase						).eval()
						unet.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  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=__lowerCAmelCase   ,    )

						lowerCamelCase__         =  LDMPipeline(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						pipeline.save_pretrained(__lowerCAmelCase						)


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

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









'''simple docstring'''







def 						A__			(				):
						return [
						    a * b * (1000 - a - b)
						    for a in range(1   ,    999						)
						    for b in range(__lowerCAmelCase   ,    999						)
						    if (a * a + b * b == (1000 - a - b) ** 2)
						][0]


if __name__ == "__main__":
							print(F'{solution() = }')

