
"""simple docstring"""

UpperCamelCase     :    Tuple       =			[0, 2, 4, 6, 8]
UpperCamelCase     :    str       =			[1, 3, 5, 7, 9]






def 							A						(    snake_case				:int    , snake_case				:int    , snake_case				:list[int]    , snake_case				:int		)				->     int:
 if remaining_length == 0:
  if digits[0] == 0 or digits[-1] == 0:
   return 0

  for i in range(length // 2 - 1    , -1    , -1		):
   remainder += digits[i] + digits[length - i - 1]

   if remainder % 2 == 0:
    return 0

   remainder //= 1_0

  return 1

 if remaining_length == 1:
  if remainder % 2 == 0:
   return 0

  __UpperCamelCase      =	0
  for digit in range(1_0		):
   __UpperCamelCase      =	digit
   result += reversible_numbers(
       0    , (remainder + 2 * digit) // 1_0    , snake_case    , snake_case		)
  return result

 __UpperCamelCase      =	0
 for digita in range(1_0		):
  __UpperCamelCase      =	digita

  if (remainder + digita) % 2 == 0:
   __UpperCamelCase      =	ODD_DIGITS
  else:
   __UpperCamelCase      =	EVEN_DIGITS

  for digita in other_parity_digits:
   __UpperCamelCase      =	digita
   result += reversible_numbers(
       remaining_length - 2    , (remainder + digita + digita) // 1_0    , snake_case    , snake_case    , )
 return result






def 							A						(    snake_case				:int = 9		)				->     int:
 __UpperCamelCase      =	0
 for length in range(1    , max_power + 1		):
  result += reversible_numbers(snake_case    , 0    , [0] * length    , snake_case		)
 return result


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





"""simple docstring"""

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 							A						(    snake_case				:Dict    , snake_case				:Optional[Any]    , snake_case				:List[Any]		)				->     Optional[Any]:
 __UpperCamelCase      =	OmegaConf.load(snake_case		)
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)['model']
 __UpperCamelCase      =	list(state_dict.keys()		)

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

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

 __UpperCamelCase      =	config.model.params.first_stage_config.params
 __UpperCamelCase      =	config.model.params.unet_config.params

 __UpperCamelCase      =	VQModel(**snake_case		).eval()
 vqvae.load_state_dict(snake_case		)

 __UpperCamelCase      =	UNetLDMModel(**snake_case		).eval()
 unet.load_state_dict(snake_case		)

 __UpperCamelCase      =	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    , )

 __UpperCamelCase      =	LDMPipeline(snake_case    , snake_case    , snake_case		)
 pipeline.save_pretrained(snake_case		)


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

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



