





import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Dict=False						):





  """simple docstring"""



  a__     : Optional[int]  =OmegaConf.load(SCREAMING_SNAKE_CASE						)
  if display:
    print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE						)						)						)
  return config
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       Tuple=None	,	SCREAMING_SNAKE_CASE					:       Optional[int]=None						):





  """simple docstring"""



  if conf_path is None:
    a__     : Union[str, Any]  ="./model_checkpoints/vqgan_only.yaml"
  a__     : str  =load_config(SCREAMING_SNAKE_CASE	,	display=SCREAMING_SNAKE_CASE						)
  a__     : Any  =VQModel(**config.model.params						)
  if ckpt_path is None:
    a__     : Optional[int]  ="./model_checkpoints/vqgan_only.pt"
  a__     : Dict  =torch.load(SCREAMING_SNAKE_CASE	,	map_location=SCREAMING_SNAKE_CASE						)
  if ".ckpt" in ckpt_path:
    a__     : Optional[int]  =sd["state_dict"]
  model.load_state_dict(SCREAMING_SNAKE_CASE	,	strict=SCREAMING_SNAKE_CASE						)
  model.to(SCREAMING_SNAKE_CASE						)
  del sd
  return model
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  a__ ,							a__ ,							a__     : str  =model.encode(SCREAMING_SNAKE_CASE						)
  print(f'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''						)
  a__     : int  =model.decode(SCREAMING_SNAKE_CASE						)
  return xrec
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       int=False						):





  """simple docstring"""



  a__ ,							a__     : Optional[Any]  =string.rsplit("."	,	1						)
  if reload:
    a__     : List[str]  =importlib.import_module(SCREAMING_SNAKE_CASE						)
    importlib.reload(SCREAMING_SNAKE_CASE						)
  return getattr(importlib.import_module(SCREAMING_SNAKE_CASE	,	package=SCREAMING_SNAKE_CASE						)	,	cls						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Any						):





  """simple docstring"""



  if "target" not in config:
    raise KeyError("Expected key `target` to instantiate."						)
  return get_obj_from_str(config["target"]						)(**config.get("params"	,	{}						)						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]=True	,	SCREAMING_SNAKE_CASE					:       Optional[int]=True						):





  """simple docstring"""



  a__     : List[Any]  =instantiate_from_config(SCREAMING_SNAKE_CASE						)
  if sd is not None:
    model.load_state_dict(SCREAMING_SNAKE_CASE						)
  if gpu:
    model.cuda()
  if eval_mode:
    model.eval()
  return {"model": model}





def 					_A	(		SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  if ckpt:
    a__     : Dict  =torch.load(SCREAMING_SNAKE_CASE	,	map_location="cpu"						)
    a__     : Any  =pl_sd["global_step"]
    print(f'''loaded model from global step {global_step}.'''						)
  else:
    a__     : Optional[Any]  ={"state_dict": None}
    a__     : Any  =None
  a__     : int  =load_model_from_config(config.model	,	pl_sd["state_dict"]	,	gpu=SCREAMING_SNAKE_CASE	,	eval_mode=SCREAMING_SNAKE_CASE						)["model"]
  return model, global_step













def 					_A	(		SCREAMING_SNAKE_CASE					:       float	,	SCREAMING_SNAKE_CASE					:       float						):





  """simple docstring"""



  return price * (1 + tax_rate)


if __name__ == "__main__":
     print(F"""{price_plus_tax(100, 0.2_5) = }""")
     print(F"""{price_plus_tax(1_2_5.5_0, 0.0_5) = }""")






