



import importlib

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






def      __lowercase       (			a__							,       a__=False       )    ->			Any:
							__SCREAMING_SNAKE_CASE									= OmegaConf.load(a__       )
							if display:
														print(yaml.dump(OmegaConf.to_container(a__       )       )       )
							return config






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






def      __lowercase       (			a__							,       a__       )    ->			Tuple:
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= model.encode(a__       )
							print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""       )
							__SCREAMING_SNAKE_CASE									= model.decode(a__       )
							return xrec






def      __lowercase       (			a__							,       a__=False       )    ->			Tuple:
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= string.rsplit('.'							,       1       )
							if reload:
														__SCREAMING_SNAKE_CASE									= importlib.import_module(a__       )
														importlib.reload(a__       )
							return getattr(importlib.import_module(a__							,       package=a__       )							,       cls       )






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






def      __lowercase       (			a__							,       a__							,       a__=True							,       a__=True       )    ->			int:
							__SCREAMING_SNAKE_CASE									= instantiate_from_config(a__       )
							if sd is not None:
														model.load_state_dict(a__       )
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}






def      __lowercase       (			a__							,       a__							,       a__							,       a__       )    ->			str:
							# load the specified checkpoint
							if ckpt:
														__SCREAMING_SNAKE_CASE									= torch.load(a__							,       map_location='cpu'       )
														__SCREAMING_SNAKE_CASE									= pl_sd['global_step']
														print(f"""loaded model from global step {global_step}."""       )
							else:
														__SCREAMING_SNAKE_CASE									= {'state_dict': None}
														__SCREAMING_SNAKE_CASE									= None
							__SCREAMING_SNAKE_CASE									= load_model_from_config(config.model							,       pl_sd['state_dict']							,       gpu=a__							,       eval_mode=a__       )['model']
							return model, global_step











lowerCAmelCase__		:					Optional[int]							=9.80_665






def      __lowercase       (			a__							,       a__							,       a__ = g       )    ->			float:

							if fluid_density <= 0:
														raise ValueError('Impossible fluid density'       )
							if volume < 0:
														raise ValueError('Impossible Object volume'       )
							if gravity <= 0:
														raise ValueError('Impossible Gravity'       )

							return fluid_density * gravity * volume


if __name__ == "__main__":
			import doctest

			# run doctest
			doctest.testmod()






