




"""simple docstring"""


import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def  UpperCAmelCase__	(snake_case__							: List[Any]		,       snake_case__							: Dict		,       snake_case__							: Dict   ):
  """simple docstring"""

  _snake_case							:		Dict								=  OmegaConf.load(snake_case__   )
  _snake_case							:		Optional[Any]								=  torch.load(snake_case__		,       map_location="""cpu"""   )["""model"""]
  _snake_case							:		Optional[Any]								=  list(state_dict.keys()   )

  # extract state_dict for VQVAE
  _snake_case							:		Tuple								=  {}
  _snake_case							:		Optional[Any]								=  """first_stage_model."""
  for key in keys:
    if key.startswith(snake_case__   ):
      _snake_case							:		Optional[int]								=  state_dict[key]

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

  _snake_case							:		str								=  config.model.params.first_stage_config.params
  _snake_case							:		Any								=  config.model.params.unet_config.params

  _snake_case							:		Optional[Any]								=  VQModel(**snake_case__   ).eval()
  vqvae.load_state_dict(snake_case__   )

  _snake_case							:		Tuple								=  UNetLDMModel(**snake_case__   ).eval()
  unet.load_state_dict(snake_case__   )

  _snake_case							:		List[str]								=  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__		,       )

  _snake_case							:		Any								=  LDMPipeline(snake_case__		,       snake_case__		,       snake_case__   )
  pipeline.save_pretrained(snake_case__   )


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)








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  []
  _snake_case							:		Dict								=  set({"""(""", """[""", """{"""}   )
  _snake_case							:		Union[str, Any]								=  set({""")""", """]""", """}"""}   )
  _snake_case							:		Tuple								=  {"""{""": """}""", """[""": """]""", """(""": """)"""}

  for i in range(len(snake_case__   )   ):
    if s[i] in open_brackets:
      stack.append(s[i]   )

    elif s[i] in closed_brackets and (
        len(snake_case__   ) == 0 or (len(snake_case__   ) > 0 and open_to_closed[stack.pop()] != s[i])
    ):
      return False

  return len(snake_case__   ) == 0


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Any								=  input("""Enter sequence of brackets: """   )
  if is_balanced(snake_case__   ):
    print(snake_case__		,       """is balanced"""   )
  else:
    print(snake_case__		,       """is not balanced"""   )


if __name__ == "__main__":
     main()


