



'''simple docstring'''



import importlib

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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=False					):



							"""simple docstring"""



							__lowercase       	=OmegaConf.load(_lowerCAmelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCAmelCase					)					)					)
							return config



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=None  ,   _lowerCAmelCase=None					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=False					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase					):



							"""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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase=True  ,   _lowerCAmelCase=True					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							if ckpt:
														__lowercase       	=torch.load(_lowerCAmelCase  ,   map_location='cpu'					)
														__lowercase       	=pl_sd['global_step']
														print(f"""loaded model from global step {global_step}."""					)
							else:
														__lowercase       	={'state_dict': None}
														__lowercase       	=None
							__lowercase       	=load_model_from_config(config.model  ,   pl_sd['state_dict']  ,   gpu=_lowerCAmelCase  ,   eval_mode=_lowerCAmelCase					)['model']
							return model, global_step








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase	 =       {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
		from .tokenization_bertweet import BertweetTokenizer

else:
		import sys

		lowerCamelCase	 =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



