






"""simple docstring"""


import importlib

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




def       snake_case  (A_				:str     ,      A_				:Optional[int]=False				):







  '''simple docstring'''

  a					:      Tuple    =       OmegaConf.load(A_				)
  if display:
    print(yaml.dump(OmegaConf.to_container(A_				)				)				)
  return config




def       snake_case  (A_				:List[str]     ,      A_				:int=None     ,      A_				:Optional[Any]=None				):







  '''simple docstring'''

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




def       snake_case  (A_				:Optional[int]     ,      A_				:int				):







  '''simple docstring'''

  a,					a,					a					:      int    =       model.encode(A_				)
  print(f'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''				)
  a					:      int    =       model.decode(A_				)
  return xrec




def       snake_case  (A_				:Union[str, Any]     ,      A_				:Any=False				):







  '''simple docstring'''

  a,					a					:      Optional[int]    =       string.rsplit('.'     ,      1				)
  if reload:
    a					:      Optional[int]    =       importlib.import_module(A_				)
    importlib.reload(A_				)
  return getattr(importlib.import_module(A_     ,      package=A_				)     ,      cls				)




def       snake_case  (A_				:Optional[Any]				):







  '''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       snake_case  (A_				:Union[str, Any]     ,      A_				:Optional[int]     ,      A_				:Dict=True     ,      A_				:List[Any]=True				):







  '''simple docstring'''

  a					:      Optional[Any]    =       instantiate_from_config(A_				)
  if sd is not None:
    model.load_state_dict(A_				)
  if gpu:
    model.cuda()
  if eval_mode:
    model.eval()
  return {"model": model}






def       snake_case  (A_				:List[Any]     ,      A_				:Any     ,      A_				:Any     ,      A_				:Optional[Any]				):







  '''simple docstring'''

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












"""simple docstring"""


def       snake_case  (A_				:str     ,      A_				:bool = False				):







  '''simple docstring'''

  if not isinstance(A_     ,      A_				):
    a					:      Union[str, Any]    =       f'''Expected string as input, found {type(A_				)}'''
    raise ValueError(A_				)
  if not isinstance(A_     ,      A_				):
    a					:      Optional[int]    =       f'''Expected boolean as use_pascal parameter, found {type(A_				)}'''
    raise ValueError(A_				)

  a					:      Tuple    =       input_str.split('_'				)

  a					:      Dict    =       0 if use_pascal else 1

  a					:      int    =       words[start_index:]

  a					:      int    =       [word[0].upper() + word[1:] for word in words_to_capitalize]

  a					:      List[str]    =       '' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
   from doctest import testmod

   testmod()




