



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	Optional[Any]=False				)->int:
      _lowerCAmelCase 			=  OmegaConf.load(_SCREAMING_SNAKE_CASE				)
      if display:
            print(yaml.dump(OmegaConf.to_container(_SCREAMING_SNAKE_CASE				)				)				)
      return config
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Any     ,				_SCREAMING_SNAKE_CASE   :	List[Any]=None     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]=None				)->Union[str, Any]:
      if conf_path is None:
            _lowerCAmelCase 			=  '''./model_checkpoints/vqgan_only.yaml'''
      _lowerCAmelCase 			=  load_config(_SCREAMING_SNAKE_CASE     ,				display=_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  VQModel(**config.model.params				)
      if ckpt_path is None:
            _lowerCAmelCase 			=  '''./model_checkpoints/vqgan_only.pt'''
      _lowerCAmelCase 			=  torch.load(_SCREAMING_SNAKE_CASE     ,				map_location=_SCREAMING_SNAKE_CASE				)
      if ".ckpt" in ckpt_path:
            _lowerCAmelCase 			=  sd['''state_dict''']
      model.load_state_dict(_SCREAMING_SNAKE_CASE     ,				strict=_SCREAMING_SNAKE_CASE				)
      model.to(_SCREAMING_SNAKE_CASE				)
      del sd
      return model
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	str				)->int:
      _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  model.encode(_SCREAMING_SNAKE_CASE				)
      print(f'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''				)
      _lowerCAmelCase 			=  model.decode(_SCREAMING_SNAKE_CASE				)
      return xrec
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Any=False				)->str:
      _lowerCAmelCase			,   _lowerCAmelCase 			=  string.rsplit('''.'''     ,				1				)
      if reload:
            _lowerCAmelCase 			=  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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Dict				)->Dict:
      if "target" not in config:
            raise KeyError('''Expected key `target` to instantiate.'''				)
      return get_obj_from_str(config['''target''']				)(**config.get('''params'''     ,				{}				)				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	int=True     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]=True				)->List[str]:
      _lowerCAmelCase 			=  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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]				)->Optional[Any]:
      # load the specified checkpoint
      if ckpt:
            _lowerCAmelCase 			=  torch.load(_SCREAMING_SNAKE_CASE     ,				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=_SCREAMING_SNAKE_CASE     ,				eval_mode=_SCREAMING_SNAKE_CASE				)['''model''']
      return model, global_step




from __future__ import annotations
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	list				)->list:
      if len(_SCREAMING_SNAKE_CASE				) == 0:
            return []
      _lowerCAmelCase			,   _lowerCAmelCase 			=  min(_SCREAMING_SNAKE_CASE				), max(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  int(max_value - min_value				) + 1
      _lowerCAmelCase 			=  [[] for _ in range(_SCREAMING_SNAKE_CASE				)]

      for i in my_list:
            buckets[int(i - min_value				)].append(_SCREAMING_SNAKE_CASE				)

      return [v for bucket in buckets for v in sorted(_SCREAMING_SNAKE_CASE				)]


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

    testmod()
    assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
    assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]