

"""simple docstring"""




import importlib

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					Optional[Any]:
	__lowerCamelCase    :    List[str] 	=			OmegaConf.load(lowerCamelCase__      )
	if display:
		print(yaml.dump(OmegaConf.to_container(lowerCamelCase__      )      )      )
	return config






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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    Tuple 	=			model.encode(lowerCamelCase__      )
	print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"      )
	__lowerCamelCase    :    Dict 	=			model.decode(lowerCamelCase__      )
	return xrec






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					str:
	__lowerCamelCase    :    str 	=			string.rsplit('.'     ,			1      )
	if reload:
		__lowerCamelCase    :    int 	=			importlib.import_module(lowerCamelCase__      )
		importlib.reload(lowerCamelCase__      )
	return getattr(importlib.import_module(lowerCamelCase__     ,			package=lowerCamelCase__      )     ,			cls      )






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






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







# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


a		=open  # noqa: we just need to have a builtin inside this module to test it properly




