




'''simple docstring'''






import importlib

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






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=False		)    ->   Dict:
      snake_case__       :   Tuple	      =    OmegaConf.load(__SCREAMING_SNAKE_CASE		)
      if display:
            print(yaml.dump(OmegaConf.to_container(__SCREAMING_SNAKE_CASE		)		)		)
      return config






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






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=False		)    ->   Any:
      snake_case__      ,							snake_case__       :   int	      =    string.rsplit('.'				,      1		)
      if reload:
            snake_case__       :   Optional[int]	      =    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		)    ->   Union[str, Any]:
      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				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=True				,      __SCREAMING_SNAKE_CASE=True		)    ->   int:
      snake_case__       :   Tuple	      =    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				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Dict:
      # load the specified checkpoint
      if ckpt:
            snake_case__       :   List[Any]	      =    torch.load(__SCREAMING_SNAKE_CASE				,      map_location='cpu'		)
            snake_case__       :   List[str]	      =    pl_sd['global_step']
            print(f"loaded model from global step {global_step}."		)
      else:
            snake_case__       :   Optional[Any]	      =    {'state_dict': None}
            snake_case__       :   List[str]	      =    None
      snake_case__       :   Union[str, 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






'''simple docstring'''






from math import pi






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   float:
      return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
      print(arc_length(90, 10))
