





import importlib

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       Tuple=False						)			-> Dict:



							'''simple docstring'''




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





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



							'''simple docstring'''




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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       Tuple						)			-> Optional[int]:



							'''simple docstring'''




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





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



							'''simple docstring'''




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





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



							'''simple docstring'''




							_snake_case									=				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 						lowerCamelCase__     (     UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       Union[str, Any]						)			-> int:



							'''simple docstring'''




							if ckpt:
														_snake_case									=				torch.load(lowercase_       ,    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=lowercase_       ,    eval_mode=lowercase_						)["""model"""]
							return model, global_step










def 						lowerCamelCase__     (     UpperCamelCase__			:       list[int]       ,    UpperCamelCase__			:       int						)			-> bool:



					'''simple docstring'''




					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				[[False] * (required_sum + 1) for _ in range(arr_len + 1						)]

					# for each arr value, a sum of zero(0) can be formed by not taking any element
					# hence True/1
					for i in range(arr_len + 1						):
										_snake_case									=				True

					# sum is not zero and set is empty then false
					for i in range(1       ,    required_sum + 1						):
										_snake_case									=				False

					for i in range(1       ,    arr_len + 1						):
										for j in range(1       ,    required_sum + 1						):
															if arr[i - 1] > j:
																				_snake_case									=				subset[i - 1][j]
															if arr[i - 1] <= j:
																				_snake_case									=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

					return subset[arr_len][required_sum]


if __name__ == "__main__":
					import doctest

					doctest.testmod()



