





import importlib

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





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    List[Any]=False  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			OmegaConf.load(UpperCamelCase__  )
					if display:
										print(yaml.dump(OmegaConf.to_container(UpperCamelCase__  )  )  )
					return config





def   lowerCamelCase__       (       UpperCamelCase__     :    Any    ,      UpperCamelCase__     :    List[Any]=None    ,      UpperCamelCase__     :    Optional[int]=None  )     ->	Dict:



					'''simple docstring'''




					if conf_path is None:
										_snake_case													=			'./model_checkpoints/vqgan_only.yaml'
					_snake_case													=			load_config(UpperCamelCase__    ,      display=UpperCamelCase__  )
					_snake_case													=			VQModel(**config.model.params  )
					if ckpt_path is None:
										_snake_case													=			'./model_checkpoints/vqgan_only.pt'
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location=UpperCamelCase__  )
					if ".ckpt" in ckpt_path:
										_snake_case													=			sd['state_dict']
					model.load_state_dict(UpperCamelCase__    ,      strict=UpperCamelCase__  )
					model.to(UpperCamelCase__  )
					del sd
					return model





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    List[str]  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case							,       _snake_case							,       _snake_case													=			model.encode(UpperCamelCase__  )
					print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''  )
					_snake_case													=			model.decode(UpperCamelCase__  )
					return xrec





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Optional[int]=False  )     ->	Any:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			string.rsplit('.'    ,      1  )
					if reload:
										_snake_case													=			importlib.import_module(UpperCamelCase__  )
										importlib.reload(UpperCamelCase__  )
					return getattr(importlib.import_module(UpperCamelCase__    ,      package=UpperCamelCase__  )    ,      cls  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]  )     ->	Tuple:



					'''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__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    int=True    ,      UpperCamelCase__     :    Tuple=True  )     ->	List[str]:



					'''simple docstring'''




					_snake_case													=			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__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Any    ,      UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    Tuple  )     ->	List[Any]:



					'''simple docstring'''




					if ckpt:
										_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )
										_snake_case													=			pl_sd['global_step']
										print(F'''loaded model from global step {global_step}.'''  )
					else:
										_snake_case													=			{'state_dict': None}
										_snake_case													=			None
					_snake_case													=			load_model_from_config(config.model    ,      pl_sd['state_dict']    ,      gpu=UpperCamelCase__    ,      eval_mode=UpperCamelCase__  )['model']
					return model, global_step










from cva import destroyAllWindows, imread, imshow, waitKey





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict  )     ->	Optional[Any]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			img.shape[0], img.shape[1]

					# converting each pixel's color to its negative
					for i in range(UpperCamelCase__  ):
										for j in range(UpperCamelCase__  ):
															_snake_case													=			[255, 255, 255] - img[i][j]

					return img


if __name__ == "__main__":
					# read original image
					UpperCAmelCase_ 							=       imread("""image_data/lena.jpg""", 1)

					# convert to its negative
					UpperCAmelCase_ 							=       convert_to_negative(img)

					# show result image
					imshow("""negative of original image""", img)
					waitKey(0)
					destroyAllWindows()



