


import importlib

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=False       ):
  A_      :				List[str]		     =					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 ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=None       ):
  if conf_path is None:
    A_      :				Dict		     =					'''./model_checkpoints/vqgan_only.yaml'''
  A_      :				Any		     =					load_config(SCREAMING_SNAKE_CASE ,			display=SCREAMING_SNAKE_CASE       )
  A_      :				List[Any]		     =					VQModel(**config.model.params       )
  if ckpt_path is None:
    A_      :				Any		     =					'''./model_checkpoints/vqgan_only.pt'''
  A_      :				Any		     =					torch.load(SCREAMING_SNAKE_CASE ,			map_location=SCREAMING_SNAKE_CASE       )
  if ".ckpt" in ckpt_path:
    A_      :				Optional[int]		     =					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 ,			SCREAMING_SNAKE_CASE       ):
  A_     ,       A_     ,       A_      :				str		     =					model.encode(SCREAMING_SNAKE_CASE       )
  print(f'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''       )
  A_      :				Optional[int]		     =					model.decode(SCREAMING_SNAKE_CASE       )
  return xrec


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=False       ):
  A_     ,       A_      :				Union[str, Any]		     =					string.rsplit('''.''' ,			1       )
  if reload:
    A_      :				Tuple		     =					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       ):
  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 ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=True ,			SCREAMING_SNAKE_CASE=True       ):
  A_      :				Union[str, Any]		     =					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 ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  # load the specified checkpoint
  if ckpt:
    A_      :				Dict		     =					torch.load(SCREAMING_SNAKE_CASE ,			map_location='''cpu'''       )
    A_      :				Tuple		     =					pl_sd['''global_step''']
    print(f'''loaded model from global step {global_step}.'''       )
  else:
    A_      :				Dict		     =					{'''state_dict''': None}
    A_      :				Dict		     =					None
  A_      :				List[Any]		     =					load_model_from_config(config.model ,			pl_sd['''state_dict'''] ,			gpu=SCREAMING_SNAKE_CASE ,			eval_mode=SCREAMING_SNAKE_CASE       )['''model''']
  return model, global_step








def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if not nums:  # Makes sure that the list is not empty
    raise ValueError('''List is empty'''       )

  A_      :				Optional[int]		     =					sum(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )  # Calculate the average
  return sum(abs(x - average       ) for x in nums       ) / len(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




