





"""simple docstring"""





import os
from collections.abc import Iterator


def       lowerCamelCase_       (   UpperCAmelCase_ = "."  )						->Optional[int]:





  """simple docstring"""


  for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase  ):
    __UpperCAmelCase    :						List[Any]       =						[d for d in dir_names if d != "scripts" and d[0] not in "._"]
    for filename in filenames:
      if filename == "__init__.py":
        continue
      if os.path.splitext(__lowerCAmelCase  )[1] in (".py", ".ipynb"):
        yield os.path.join(__lowerCAmelCase  ,			__lowerCAmelCase  ).lstrip('''./'''  )


def       lowerCamelCase_       (   UpperCAmelCase_  )						->int:





  """simple docstring"""


  return f'''{i * '  '}*''' if i else "\n##"


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						old_path.split(os.sep  )
  for i, new_part in enumerate(new_path.split(os.sep  )  ):
    if (i + 1 > len(__lowerCAmelCase  ) or old_parts[i] != new_part) and new_part:
      print(f'''{md_prefix(__lowerCAmelCase  )} {new_part.replace('_'  ,			' '  ).title()}'''  )
  return new_path


def       lowerCamelCase_       (   UpperCAmelCase_ = "."  )						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						""
  for filepath in sorted(good_file_paths(__lowerCAmelCase  )  ):
    __UpperCAmelCase    :						List[Any]       =						os.path.split(__lowerCAmelCase  )
    if filepath != old_path:
      __UpperCAmelCase    :						Union[str, Any]       =						print_path(__lowerCAmelCase  ,			__lowerCAmelCase  )
    __UpperCAmelCase    :						Optional[Any]       =						(filepath.count(os.sep  ) + 1) if filepath else 0
    __UpperCAmelCase    :						Tuple       =						f'''{filepath}/{filename}'''.replace(''' '''  ,			'''%20'''  )
    __UpperCAmelCase    :						Any       =						os.path.splitext(filename.replace('''_'''  ,			''' '''  ).title()  )[0]
    print(f'''{md_prefix(__lowerCAmelCase  )} [{filename}]({url})'''  )


if __name__ == "__main__":
  print_directory_md('.')






"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						OmegaConf.load(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						torch.load(UpperCAmelCase_  ,			map_location='''cpu'''  )['''model''']
  __UpperCAmelCase    :						List[Any]       =						list(state_dict.keys()  )

  # extract state_dict for VQVAE
  __UpperCAmelCase    :						Dict       =						{}
  __UpperCAmelCase    :						Tuple       =						'''first_stage_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						state_dict[key]

    # extract state_dict for UNetLDM
  __UpperCAmelCase    :						Union[str, Any]       =						{}
  __UpperCAmelCase    :						Any       =						'''model.diffusion_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						int       =						state_dict[key]

  __UpperCAmelCase    :						Any       =						config.model.params.first_stage_config.params
  __UpperCAmelCase    :						Dict       =						config.model.params.unet_config.params

  __UpperCAmelCase    :						Tuple       =						VQModel(**UpperCAmelCase_  ).eval()
  vqvae.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						UNetLDMModel(**UpperCAmelCase_  ).eval()
  unet.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, 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=UpperCAmelCase_  ,			)

  __UpperCAmelCase    :						Tuple       =						LDMPipeline(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  pipeline.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:str				   =       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)
  lowercase__				:Dict				   =       parser.parse_args()

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