


def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     int:




						'''simple docstring'''







						return abs(lowercase_      ) if a == 0 else greatest_common_divisor(b % a      ,       lowercase_      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     int:




						'''simple docstring'''







						while y:  # --> when y=0 then loop will terminate and return x as final GCD.
												__UpperCAmelCase    ,      __UpperCAmelCase			:							Any	    =	y, x % y
						return abs(lowercase_      )




def     __SCREAMING_SNAKE_CASE							(       )		->     Union[str, Any]:




						'''simple docstring'''







						try:
												__UpperCAmelCase			:							Any	    =	input('''Enter two integers separated by comma (,): '''      ).split(''','''      )
												__UpperCAmelCase			:							int	    =	int(nums[0]      )
												__UpperCAmelCase			:							Optional[Any]	    =	int(nums[1]      )
												print(
												    f"greatest_common_divisor({num_a}, {num_a}) = "
												    f"{greatest_common_divisor(lowercase_      ,       lowercase_      )}"      )
												print(f"By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowercase_      ,       lowercase_      )}"      )
						except (IndexError, UnboundLocalError, ValueError):
												print('''Wrong input'''      )


if __name__ == "__main__":
				main()





import importlib

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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     Optional[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	OmegaConf.load(lowercase_      )
						if display:
												print(yaml.dump(OmegaConf.to_container(lowercase_      )      )      )
						return config




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=None      ,       lowercase_=None      )		->     Union[str, Any]:




						'''simple docstring'''







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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     List[Any]:




						'''simple docstring'''







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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	string.rsplit('''.'''      ,       1      )
						if reload:
												__UpperCAmelCase			:							Optional[int]	    =	importlib.import_module(lowercase_      )
												importlib.reload(lowercase_      )
						return getattr(importlib.import_module(lowercase_      ,       package=lowercase_      )      ,       cls      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Union[str, 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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_=True      ,       lowercase_=True      )		->     int:




						'''simple docstring'''







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





def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Optional[Any]:




						'''simple docstring'''







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

