
"""simple docstring"""

class __lowerCAmelCase		:





     def __init__(     self				):

      '''simple docstring'''




      __UpperCamelCase      =	''
      __UpperCamelCase      =	''
      __UpperCamelCase      =	[]





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      if m == -1:
       return n + 1
      elif n == -1:
       return m + 1
      elif self.dp[m][n] > -1:
       return self.dp[m][n]
      else:
       if self.worda[m] == self.worda[n]:
        __UpperCamelCase      =	self.__min_dist_top_down_dp(m - 1     ,					n - 1				)
       else:
        __UpperCamelCase      =	self.__min_dist_top_down_dp(__UpperCAmelCase     ,					n - 1				)
        __UpperCamelCase      =	self.__min_dist_top_down_dp(m - 1     ,					__UpperCAmelCase				)
        __UpperCamelCase      =	self.__min_dist_top_down_dp(m - 1     ,					n - 1				)
        __UpperCamelCase      =	1 + min(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

       return self.dp[m][n]





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	worda
      __UpperCamelCase      =	worda
      __UpperCamelCase      =	[[-1 for _ in range(len(__UpperCAmelCase				)				)] for _ in range(len(__UpperCAmelCase				)				)]

      return self.__min_dist_top_down_dp(len(__UpperCAmelCase				) - 1     ,					len(__UpperCAmelCase				) - 1				)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	worda
      __UpperCamelCase      =	worda
      __UpperCamelCase      =	len(__UpperCAmelCase				)
      __UpperCamelCase      =	len(__UpperCAmelCase				)
      __UpperCamelCase      =	[[0 for _ in range(n + 1				)] for _ in range(m + 1				)]

      for i in range(m + 1				):
       for j in range(n + 1				):
        if i == 0:  # first string is empty
         __UpperCamelCase      =	j
        elif j == 0:  # second string is empty
         __UpperCamelCase      =	i
        elif worda[i - 1] == worda[j - 1]:  # last characters are equal
         __UpperCamelCase      =	self.dp[i - 1][j - 1]
        else:
         __UpperCamelCase      =	self.dp[i][j - 1]
         __UpperCamelCase      =	self.dp[i - 1][j]
         __UpperCamelCase      =	self.dp[i - 1][j - 1]
         __UpperCamelCase      =	1 + min(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
      return self.dp[m][n]


if __name__ == "__main__":
      UpperCamelCase     :    Union[str, Any]       =			EditDistance()

      print("****************** Testing Edit Distance DP Algorithm ******************")
      print()

      UpperCamelCase     :    Dict       =			input("Enter the first string: ").strip()
      UpperCamelCase     :    List[Any]       =			input("Enter the second string: ").strip()

      print()
      print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
      print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
      print()
      print("*************** End of Testing Edit Distance DP Algorithm ***************")





"""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)



