



"""simple docstring"""




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						List[Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   OmegaConf.load(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     List[str]        =   torch.load(SCREAMING_SNAKE_CASE__						,  map_location="cpu"    )["model"]
     _UpperCAmelCase      :     List[Any]        =   list(state_dict.keys()    )

     # extract state_dict for VQVAE
     _UpperCAmelCase      :     List[Any]        =   {}
     _UpperCAmelCase      :     str        =   "first_stage_model."
     for key in keys:
          if key.startswith(SCREAMING_SNAKE_CASE__    ):
               _UpperCAmelCase      :     List[str]        =   state_dict[key]

    # extract state_dict for UNetLDM
     _UpperCAmelCase      :     Dict        =   {}
     _UpperCAmelCase      :     Union[str, Any]        =   "model.diffusion_model."
     for key in keys:
          if key.startswith(SCREAMING_SNAKE_CASE__    ):
               _UpperCAmelCase      :     List[Any]        =   state_dict[key]

     _UpperCAmelCase      :     Any        =   config.model.params.first_stage_config.params
     _UpperCAmelCase      :     List[Any]        =   config.model.params.unet_config.params

     _UpperCAmelCase      :     Optional[Any]        =   VQModel(**SCREAMING_SNAKE_CASE__    ).eval()
     vqvae.load_state_dict(SCREAMING_SNAKE_CASE__    )

     _UpperCAmelCase      :     Dict        =   UNetLDMModel(**SCREAMING_SNAKE_CASE__    ).eval()
     unet.load_state_dict(SCREAMING_SNAKE_CASE__    )

     _UpperCAmelCase      :     Dict        =   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=SCREAMING_SNAKE_CASE__						,  )

     _UpperCAmelCase      :     List[Any]        =   LDMPipeline(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     pipeline.save_pretrained(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 _lowerCAmelCase    : Tuple							=							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)
 _lowerCAmelCase    : Optional[Any]							=							parser.parse_args()

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





"""simple docstring"""




def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list    )  ->       list:





     '''simple docstring'''

     if len(SCREAMING_SNAKE_CASE__    ) < 2:
          return collection

     def circle_sort_util(SCREAMING_SNAKE_CASE__     :						list						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						int    ) -> bool:
          _UpperCAmelCase      :     Dict        =   False

          if low == high:
               return swapped

          _UpperCAmelCase      :     str        =   low
          _UpperCAmelCase      :     Dict        =   high

          while left < right:
               if collection[left] > collection[right]:
                    _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   (
                        collection[right],
                        collection[left],
                    )
                    _UpperCAmelCase      :     Optional[int]        =   True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   (
                   collection[right + 1],
                   collection[left],
               )

               _UpperCAmelCase      :     Union[str, Any]        =   True

          _UpperCAmelCase      :     Dict        =   low + int((high - low) / 2    )
          _UpperCAmelCase      :     Any        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Optional[Any]        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  mid + 1						,  SCREAMING_SNAKE_CASE__    )

          return swapped or left_swap or right_swap

     _UpperCAmelCase      :     Tuple        =   True

     while is_not_sorted is True:
          _UpperCAmelCase      :     Dict        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  0						,  len(SCREAMING_SNAKE_CASE__    ) - 1    )

     return collection


if __name__ == "__main__":
 _lowerCAmelCase    : Dict							=							input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase    : Optional[Any]							=							[int(item) for item in user_input.split(",")]
 print(circle_sort(unsorted))
