



"""simple docstring"""

import importlib

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







def 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	List[str]					,     SCREAMING_SNAKE_CASE__			:	List[Any]=False      ):



					"""simple docstring"""





					snake_case_       :		Optional[int]			       =       OmegaConf.load(SCREAMING_SNAKE_CASE__      )
					if display:
										print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE__      )      )      )
					return config







def 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	Dict					,     SCREAMING_SNAKE_CASE__			:	Optional[Any]=None					,     SCREAMING_SNAKE_CASE__			:	Dict=None      ):



					"""simple docstring"""





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







def 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	Optional[Any]					,     SCREAMING_SNAKE_CASE__			:	List[str]      ):



					"""simple docstring"""





					snake_case_       :		Optional[Any]			       =       model.encode(SCREAMING_SNAKE_CASE__      )
					print(f'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'      )
					snake_case_       :		List[Any]			       =       model.decode(SCREAMING_SNAKE_CASE__      )
					return xrec







def 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	Union[str, Any]					,     SCREAMING_SNAKE_CASE__			:	str=False      ):



					"""simple docstring"""





					snake_case_       :		Optional[int]			       =       string.rsplit("""."""					,     1      )
					if reload:
										snake_case_       :		Optional[int]			       =       importlib.import_module(SCREAMING_SNAKE_CASE__      )
										importlib.reload(SCREAMING_SNAKE_CASE__      )
					return getattr(importlib.import_module(SCREAMING_SNAKE_CASE__					,     package=SCREAMING_SNAKE_CASE__      )					,     cls      )







def 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	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__   (		SCREAMING_SNAKE_CASE__			:	Optional[Any]					,     SCREAMING_SNAKE_CASE__			:	Optional[int]					,     SCREAMING_SNAKE_CASE__			:	Dict=True					,     SCREAMING_SNAKE_CASE__			:	List[Any]=True      ):



					"""simple docstring"""





					snake_case_       :		str			       =       instantiate_from_config(SCREAMING_SNAKE_CASE__      )
					if sd is not None:
										model.load_state_dict(SCREAMING_SNAKE_CASE__      )
					if gpu:
										model.cuda()
					if eval_mode:
										model.eval()
					return {"model": model}







def 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	Optional[Any]					,     SCREAMING_SNAKE_CASE__			:	Tuple					,     SCREAMING_SNAKE_CASE__			:	List[str]					,     SCREAMING_SNAKE_CASE__			:	Tuple      ):



					"""simple docstring"""





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










import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										debug_launcher(test_script.main			)


						def      A__    (		self :Dict			):
										'''simple docstring'''

										debug_launcher(test_ops.main			)




