






import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel






def 							lowerCamelCase__ (       __lowerCamelCase     :	int    ,						__lowerCamelCase     :	List[str]=False						):
  __UpperCAmelCase				:      Tuple	 =    OmegaConf.load(__lowerCamelCase						)
  if display:
    print(yaml.dump(OmegaConf.to_container(__lowerCamelCase						)						)						)
  return config






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






def 							lowerCamelCase__ (       __lowerCamelCase     :	Union[str, Any]    ,						__lowerCamelCase     :	List[Any]						):
  __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      str	 =    model.encode(__lowerCamelCase						)
  print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""						)
  __UpperCAmelCase				:      List[Any]	 =    model.decode(__lowerCamelCase						)
  return xrec






def 							lowerCamelCase__ (       __lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	List[str]=False						):
  __UpperCAmelCase ,					__UpperCAmelCase				:      Any	 =    string.rsplit("""."""    ,						1						)
  if reload:
    __UpperCAmelCase				:      List[Any]	 =    importlib.import_module(__lowerCamelCase						)
    importlib.reload(__lowerCamelCase						)
  return getattr(importlib.import_module(__lowerCamelCase    ,						package=__lowerCamelCase						)    ,						cls						)






def 							lowerCamelCase__ (       __lowerCamelCase     :	Dict						):
  if "target" not in config:
    raise KeyError("""Expected key `target` to instantiate."""						)
  return get_obj_from_str(config["""target"""]						)(**config.get("""params"""    ,						{}						)						)






def 							lowerCamelCase__ (       __lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	Any=True    ,						__lowerCamelCase     :	List[str]=True						):
  __UpperCAmelCase				:      Optional[int]	 =    instantiate_from_config(__lowerCamelCase						)
  if sd is not None:
    model.load_state_dict(__lowerCamelCase						)
  if gpu:
    model.cuda()
  if eval_mode:
    model.eval()
  return {"model": model}







def 							lowerCamelCase__ (       __lowerCamelCase     :	Union[str, Any]    ,						__lowerCamelCase     :	Union[str, Any]    ,						__lowerCamelCase     :	Any    ,						__lowerCamelCase     :	Tuple						):
  # load the specified checkpoint
  if ckpt:
    __UpperCAmelCase				:      Optional[Any]	 =    torch.load(__lowerCamelCase    ,						map_location="""cpu"""						)
    __UpperCAmelCase				:      Optional[int]	 =    pl_sd["""global_step"""]
    print(f"""loaded model from global step {global_step}."""						)
  else:
    __UpperCAmelCase				:      Tuple	 =    {"""state_dict""": None}
    __UpperCAmelCase				:      int	 =    None
  __UpperCAmelCase				:      int	 =    load_model_from_config(config.model    ,						pl_sd["""state_dict"""]    ,						gpu=__lowerCamelCase    ,						eval_mode=__lowerCamelCase						)["""model"""]
  return model, global_step







'''simple docstring'''





# flake8: noqa
# Lint as: python3
__a  			=						[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
