



'''simple docstring'''





import os

__a  			=						{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}





def 		__snake_case(							_lowerCAmelCase						)						->					int:
      snake_case__    :       Union[str, Any]       		= 0

      snake_case__    :       List[str]       		= 0
      while index < len(_lowerCAmelCase						) - 1:
            snake_case__    :       List[Any]       		= SYMBOLS[numerals[index]]
            snake_case__    :       List[Any]       		= SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value





def 		__snake_case(							_lowerCAmelCase						)						->					str:
      snake_case__    :       Dict       		= """"""

      snake_case__    :       str       		= num // 1_000
      numerals += m_count * "M"
      num %= 1_000

      snake_case__    :       Optional[Any]       		= num // 100
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 100

      snake_case__    :       str       		= num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals





def 		__snake_case(							_lowerCAmelCase = "/p089_roman.txt"						)						->					int:
      snake_case__    :       str       		= 0

      with open(os.path.dirname(_lowerCAmelCase						) + roman_numerals_filename						) as filea:
            snake_case__    :       List[Any]       		= filea.readlines()

      for line in lines:
            snake_case__    :       Optional[int]       		= line.strip()
            snake_case__    :       List[str]       		= parse_roman_numerals(_lowerCAmelCase						)
            snake_case__    :       Any       		= generate_roman_numerals(_lowerCAmelCase						)
            savings += len(_lowerCAmelCase						) - len(_lowerCAmelCase						)

      return savings


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





'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Tuple       		= OmegaConf.load(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= torch.load(_lowerCAmelCase	,  map_location="""cpu"""						)["""model"""]
      snake_case__    :       int       		= list(state_dict.keys()						)

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

    # extract state_dict for UNetLDM
      snake_case__    :       Optional[Any]       		= {}
      snake_case__    :       Optional[int]       		= """model.diffusion_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

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

      snake_case__    :       Tuple       		= VQModel(**_lowerCAmelCase						).eval()
      vqvae.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= UNetLDMModel(**_lowerCAmelCase						).eval()
      unet.load_state_dict(_lowerCAmelCase						)

      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=_lowerCAmelCase	,  )

      snake_case__    :       List[str]       		= LDMPipeline(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
      pipeline.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
       __a  			=						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  			=						parser.parse_args()

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