
import importlib

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




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Tuple=False  ):
 __a :  List[Any]						=  OmegaConf.load(lowerCAmelCase_  )
 if display:
  print(yaml.dump(OmegaConf.to_container(lowerCAmelCase_  )  )  )
 return config




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				str=None  ,   lowerCAmelCase__							:				Tuple=None  ):
 if conf_path is None:
  __a :  Union[str, Any]						=  '''./model_checkpoints/vqgan_only.yaml'''
 __a :  List[str]						=  load_config(lowerCAmelCase_  ,   display=lowerCAmelCase_  )
 __a :  List[Any]						=  VQModel(**config.model.params  )
 if ckpt_path is None:
  __a :  Optional[Any]						=  '''./model_checkpoints/vqgan_only.pt'''
 __a :  Tuple						=  torch.load(lowerCAmelCase_  ,   map_location=lowerCAmelCase_  )
 if ".ckpt" in ckpt_path:
  __a :  Optional[Any]						=  sd['''state_dict''']
 model.load_state_dict(lowerCAmelCase_  ,   strict=lowerCAmelCase_  )
 model.to(lowerCAmelCase_  )
 del sd
 return model




def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				Any  ):
 __a				,       __a				,       __a :  List[str]						=  model.encode(lowerCAmelCase_  )
 print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"  )
 __a :  List[str]						=  model.decode(lowerCAmelCase_  )
 return xrec




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				List[Any]=False  ):
 __a				,       __a :  str						=  string.rsplit('''.'''  ,   1  )
 if reload:
  __a :  Optional[Any]						=  importlib.import_module(lowerCAmelCase_  )
  importlib.reload(lowerCAmelCase_  )
 return getattr(importlib.import_module(lowerCAmelCase_  ,   package=lowerCAmelCase_  )  ,   cls  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 if "target" not in config:
  raise KeyError('''Expected key `target` to instantiate.'''  )
 return get_obj_from_str(config['''target''']  )(**config.get('''params'''  ,   {}  )  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				List[Any]=True  ,   lowerCAmelCase__							:				List[Any]=True  ):
 __a :  Any						=  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    __UpperCamelCase     (				lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				Any  ):
 # load the specified checkpoint
 if ckpt:
  __a :  Tuple						=  torch.load(lowerCAmelCase_  ,   map_location='''cpu'''  )
  __a :  List[str]						=  pl_sd['''global_step''']
  print(f"loaded model from global step {global_step}."  )
 else:
  __a :  str						=  {'''state_dict''': None}
  __a :  Union[str, Any]						=  None
 __a :  List[Any]						=  load_model_from_config(config.model  ,   pl_sd['''state_dict''']  ,   gpu=lowerCAmelCase_  ,   eval_mode=lowerCAmelCase_  )['''model''']
 return model, global_step







import re

from filelock import FileLock


try:
     import nltk

     _snake_case  :    Any						    =  True
except (ImportError, ModuleNotFoundError):
     _snake_case  :    Union[str, Any]						    =  False

if NLTK_AVAILABLE:
     with FileLock('.lock') as lock:
          nltk.download('punkt', quiet=True)

def   a_    ( lowerCAmelCase_      :			str	):
   re.sub('<n>',	'',	lowerCAmelCase_	)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(lowerCAmelCase_	)	)

