


'''simple docstring'''



import importlib

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




def      _a     (						_lowercase :     str      , _lowercase :     Any=False  ):





     '''simple docstring'''






     __UpperCAmelCase     :			List[Any]  							=      OmegaConf.load(_lowercase  )
     if display:
          print(yaml.dump(OmegaConf.to_container(_lowercase  )  )  )
     return config




def      _a     (						_lowercase :     int      , _lowercase :     Any=None      , _lowercase :     Any=None  ):





     '''simple docstring'''






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




def      _a     (						_lowercase :     Dict      , _lowercase :     Tuple  ):





     '''simple docstring'''






     __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Optional[Any]  							=      model.encode(_lowercase  )
     print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'  )
     __UpperCAmelCase     :			List[str]  							=      model.decode(_lowercase  )
     return xrec




def      _a     (						_lowercase :     List[Any]      , _lowercase :     str=False  ):





     '''simple docstring'''






     __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      string.rsplit('''.'''      , 1  )
     if reload:
          __UpperCAmelCase     :			Optional[Any]  							=      importlib.import_module(_lowercase  )
          importlib.reload(_lowercase  )
     return getattr(importlib.import_module(_lowercase      , package=_lowercase  )      , cls  )




def      _a     (						_lowercase :     Dict  ):





     '''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      _a     (						_lowercase :     Optional[Any]      , _lowercase :     int      , _lowercase :     Union[str, Any]=True      , _lowercase :     Optional[int]=True  ):





     '''simple docstring'''






     __UpperCAmelCase     :			List[str]  							=      instantiate_from_config(_lowercase  )
     if sd is not None:
          model.load_state_dict(_lowercase  )
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}




def      _a     (						_lowercase :     Union[str, Any]      , _lowercase :     str      , _lowercase :     Tuple      , _lowercase :     List[Any]  ):





     '''simple docstring'''






     if ckpt:
          __UpperCAmelCase     :			Optional[int]  							=      torch.load(_lowercase      , map_location='''cpu'''  )
          __UpperCAmelCase     :			List[Any]  							=      pl_sd['''global_step''']
          print(F'loaded model from global step {global_step}.'  )
     else:
          __UpperCAmelCase     :			Optional[Any]  							=      {'''state_dict''': None}
          __UpperCAmelCase     :			List[Any]  							=      None
     __UpperCAmelCase     :			List[Any]  							=      load_model_from_config(config.model      , pl_sd['''state_dict''']      , gpu=_lowercase      , eval_mode=_lowercase  )['''model''']
     return model, global_step



'''simple docstring'''



from argparse import ArgumentParser

from .env import EnvironmentCommand




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      ArgumentParser('''Diffusers CLI tool'''      , usage='''diffusers-cli <command> [<args>]'''  )
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.add_subparsers(help='''diffusers-cli command helpers'''  )

     # Register commands
     EnvironmentCommand.register_subcommand(_lowercase  )

     # Let's go
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.parse_args()

     if not hasattr(_lowercase      , '''func'''  ):
          parser.print_help()
          exit(1  )

     # Run
     __UpperCAmelCase     :			Any  							=      args.func(_lowercase  )
     service.run()


if __name__ == "__main__":
      main()