




import importlib

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




def 					lowerCamelCase__       (						A__       :					Dict  ,			A__       :					Optional[int]=False	):





       '''simple docstring'''



       __lowerCamelCase   =						OmegaConf.load(A__	)
       if display:
              print(yaml.dump(OmegaConf.to_container(A__	)	)	)
       return config




def 					lowerCamelCase__       (						A__       :					Optional[int]  ,			A__       :					Union[str, Any]=None  ,			A__       :					Any=None	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Optional[Any]  ,			A__       :					List[Any]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					List[Any]=False	):





       '''simple docstring'''



       __lowerCamelCase,   __lowerCamelCase   =						string.rsplit("""."""  ,			1	)
       if reload:
              __lowerCamelCase   =						importlib.import_module(A__	)
              importlib.reload(A__	)
       return getattr(importlib.import_module(A__  ,			package=A__	)  ,			cls	)




def 					lowerCamelCase__       (						A__       :					Optional[int]	):





       '''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 					lowerCamelCase__       (						A__       :					Optional[Any]  ,			A__       :					Optional[Any]  ,			A__       :					Dict=True  ,			A__       :					int=True	):





       '''simple docstring'''



       __lowerCamelCase   =						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 					lowerCamelCase__       (						A__       :					List[Any]  ,			A__       :					str  ,			A__       :					Dict  ,			A__       :					List[Any]	):





       '''simple docstring'''



       if ckpt:
              __lowerCamelCase   =						torch.load(A__  ,			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=A__  ,			eval_mode=A__	)["""model"""]
       return model, global_step







def 					lowerCamelCase__       (						A__       :					list	):





       '''simple docstring'''



       __lowerCamelCase   =						len(A__	)
       for _ in range(A__	):
              for i in range(_ % 2  ,			arr_size - 1  ,			2	):
                     if arr[i + 1] < arr[i]:
                            __lowerCamelCase,   __lowerCamelCase   =						arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
       UpperCAmelCase_									=	list(range(10, 0, -1))
       print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")

