



from typing import List

from .keymap import KEYMAP, get_character






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							def decorator(_lowerCamelCase						):
														_lowerCAmelCase  :							Dict				     =					getattr(__UpperCamelCase       ,				"handle_key"       ,				[]						)
														handle += [key]
														setattr(__UpperCamelCase       ,				"handle_key"       ,				__UpperCamelCase						)
														return func

							return decorator






def        A       (      *_lowerCamelCase						):





							'''simple docstring'''




							def decorator(_lowerCamelCase						):
														_lowerCAmelCase  :							Optional[int]				     =					getattr(__UpperCamelCase       ,				"handle_key"       ,				[]						)
														handle += keys
														setattr(__UpperCamelCase       ,				"handle_key"       ,				__UpperCamelCase						)
														return func

							return decorator



class 						UpperCAmelCase_					( SCREAMING_SNAKE_CASE__):







							def __new__(     cls,    __a,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					super().__new__(cls,    _lowercase,    _lowercase,    _lowercase)
														if not hasattr(_lowercase,    "key_handler"):
																					setattr(_lowercase,    "key_handler",    {})
														setattr(_lowercase,    "handle_input",    KeyHandler.handle_input)

														for value in attrs.values():
																					_lowerCAmelCase  :							List[str]				     =					getattr(_lowercase,    "handle_key",    [])
																					for key in handled_keys:
																												_lowerCAmelCase  :							List[str]				     =					value
														return new_cls







							@staticmethod
							def      snake_case__   (     cls):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					get_character()
														if char != KEYMAP["undefined"]:
																					_lowerCAmelCase  :							int				     =					ord(_lowercase)
														_lowerCAmelCase  :							Tuple				     =					cls.key_handler.get(_lowercase)
														if handler:
																					_lowerCAmelCase  :							Union[str, Any]				     =					char
																					return handler(cls)
														else:
																					return None






def        A       (      cls						):





							'''simple docstring'''




							return KeyHandler(cls.__name__       ,				cls.__bases__       ,				cls.__dict__.copy()						)





import importlib

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					OmegaConf.load(_lowerCamelCase						)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase						)						)						)
							return config






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=None       ,				_lowerCamelCase=None						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Tuple				     =					model.encode(_lowerCamelCase						)
							print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"						)
							_lowerCAmelCase  :							int				     =					model.decode(_lowerCamelCase						)
							return xrec






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase						):





							'''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       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=True       ,				_lowerCamelCase=True						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




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