



import importlib

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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     List[str]=False     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	OmegaConf.load(__UpperCAmelCase     )
 if display:
  print(yaml.dump(OmegaConf.to_container(__UpperCAmelCase     )     )     )
 return config






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     Optional[int]=None		,	__lowerCamelCase   :     Any=None     ):



 """simple docstring"""





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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     Union[str, Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	model.encode(__UpperCAmelCase     )
 print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""     )
 __SCREAMING_SNAKE_CASE       :    Dict       =	model.decode(__UpperCAmelCase     )
 return xrec






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     Optional[Any]=False     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	string.rsplit("."		,	1     )
 if reload:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	importlib.import_module(__UpperCAmelCase     )
  importlib.reload(__UpperCAmelCase     )
 return getattr(importlib.import_module(__UpperCAmelCase		,	package=__UpperCAmelCase     )		,	cls     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[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 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     Union[str, Any]=True		,	__lowerCamelCase   :     Dict=True     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	instantiate_from_config(__UpperCAmelCase     )
 if sd is not None:
  model.load_state_dict(__UpperCAmelCase     )
 if gpu:
  model.cuda()
 if eval_mode:
  model.eval()
 return {"model": model}






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     List[Any]     ):



 """simple docstring"""





 if ckpt:
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.load(__UpperCAmelCase		,	map_location="cpu"     )
  __SCREAMING_SNAKE_CASE       :    Tuple       =	pl_sd["global_step"]
  print(F"""loaded model from global step {global_step}."""     )
 else:
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{"state_dict": None}
  __SCREAMING_SNAKE_CASE       :    List[str]       =	None
 __SCREAMING_SNAKE_CASE       :    List[str]       =	load_model_from_config(config.model		,	pl_sd["state_dict"]		,	gpu=__UpperCAmelCase		,	eval_mode=__UpperCAmelCase     )["model"]
 return model, global_step











from diffusers.utils.testing_utils import require_onnxruntime

@require_onnxruntime
class       _SCREAMING_SNAKE_CASE	:
     pass






