





import importlib

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

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

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

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Any	):
   __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase								=	model.encode(lowerCAmelCase_	)
   print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""	)
   __lowerCAmelCase								=	model.decode(lowerCAmelCase_	)
   return xrec

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

def   a_    ( 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   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			List[Any]=True,	lowerCAmelCase_      :			List[Any]=True	):
   __lowerCAmelCase								=	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_      :			str,	lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			List[Any],	lowerCAmelCase_      :			Any	):
   # load the specified checkpoint
   if ckpt:
      __lowerCAmelCase								=	torch.load(lowerCAmelCase_,	map_location='cpu'	)
      __lowerCAmelCase								=	pl_sd['global_step']
      print(F"""loaded model from global step {global_step}."""	)
   else:
      __lowerCAmelCase								=	{'state_dict': None}
      __lowerCAmelCase								=	None
   __lowerCAmelCase								=	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_	)	)

