




import importlib

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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     Union[str, Any]=False							) ->			int:
						SCREAMING_SNAKE_CASE_     						=      OmegaConf.load(__UpperCAmelCase							)
						if display:
												print(yaml.dump(OmegaConf.to_container(__UpperCAmelCase							)							)							)
						return config

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     Dict=None      ,      __UpperCAmelCase						:     Optional[int]=None							) ->			Tuple:
						if conf_path is None:
												SCREAMING_SNAKE_CASE_     						=      './model_checkpoints/vqgan_only.yaml'
						SCREAMING_SNAKE_CASE_     						=      load_config(__UpperCAmelCase      ,      display=__UpperCAmelCase							)
						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(__UpperCAmelCase      ,      map_location=__UpperCAmelCase							)
						if ".ckpt" in ckpt_path:
												SCREAMING_SNAKE_CASE_     						=      sd['state_dict']
						model.load_state_dict(__UpperCAmelCase      ,      strict=__UpperCAmelCase							)
						model.to(__UpperCAmelCase							)
						del sd
						return model

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     str							) ->			str:
						SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      model.encode(__UpperCAmelCase							)
						print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"							)
						SCREAMING_SNAKE_CASE_     						=      model.decode(__UpperCAmelCase							)
						return xrec

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     List[str]=False							) ->			Any:
						SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      string.rsplit('.'      ,      1							)
						if reload:
												SCREAMING_SNAKE_CASE_     						=      importlib.import_module(__UpperCAmelCase							)
												importlib.reload(__UpperCAmelCase							)
						return getattr(importlib.import_module(__UpperCAmelCase      ,      package=__UpperCAmelCase							)      ,      cls							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int							) ->			Union[str, Any]:
						if "target" not in config:
												raise KeyError('Expected key `target` to instantiate.'							)
						return get_obj_from_str(config['target']							)(**config.get('params'      ,      {}							)							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     Any=True      ,      __UpperCAmelCase						:     Union[str, Any]=True							) ->			Dict:
						SCREAMING_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  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     Optional[int]							) ->			Optional[Any]:
						# load the specified checkpoint
						if ckpt:
												SCREAMING_SNAKE_CASE_     						=      torch.load(__UpperCAmelCase      ,      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=__UpperCAmelCase      ,      eval_mode=__UpperCAmelCase							)['model']
						return model, global_step





from abc import ABC, abstractmethod
from argparse import ArgumentParser





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 @staticmethod
 @abstractmethod
 def 				lowerCAmelCase_  (							_lowerCAmelCase  :		ArgumentParser						):
       raise NotImplementedError()




 @abstractmethod
 def 				lowerCAmelCase_  (							self  :		Dict						):
       raise NotImplementedError()