





def   lowerCamelCase__       (       )     ->	int:



					'''simple docstring'''




					return 1





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else two_pence(x - 2  ) + one_pence()





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else five_pence(x - 5  ) + two_pence(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else ten_pence(x - 10  ) + five_pence(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else twenty_pence(x - 20  ) + ten_pence(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else fifty_pence(x - 50  ) + twenty_pence(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else one_pound(x - 100  ) + fifty_pence(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else two_pound(x - 200  ) + one_pound(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int = 200  )     ->	int:



					'''simple docstring'''




					return two_pound(UpperCamelCase__  )


if __name__ == "__main__":
					print(solution(int(input().strip())))










import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Dict  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case													=			OmegaConf.load(UpperCamelCase__  )
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )['model']
					_snake_case													=			list(state_dict.keys()  )

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

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

					_snake_case													=			config.model.params.first_stage_config.params
					_snake_case													=			config.model.params.unet_config.params

					_snake_case													=			VQModel(**UpperCamelCase__  ).eval()
					vqvae.load_state_dict(UpperCamelCase__  )

					_snake_case													=			UNetLDMModel(**UpperCamelCase__  ).eval()
					unet.load_state_dict(UpperCamelCase__  )

					_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=UpperCamelCase__    ,      )

					_snake_case													=			LDMPipeline(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )
					pipeline.save_pretrained(UpperCamelCase__  )


if __name__ == "__main__":
					UpperCAmelCase_ 							=       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_ 							=       parser.parse_args()

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



