


"""simple docstring"""



import importlib

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







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  int=False )	->						str:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  Dict=None						,							lowercase  :  Optional[int]=None )	->						Tuple:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Any )	->						Tuple:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Optional[int]=False )	->						Optional[Any]:


    """simple docstring"""





    snake_case					,snake_case    :      Union[str, Any]      					=    string.rsplit("."						,							1 )
    if reload:
        snake_case    :      Any      					=    importlib.import_module(lowercase )
        importlib.reload(lowercase )
    return getattr(importlib.import_module(lowercase						,							package=lowercase )						,							cls )







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any] )	->						List[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   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  str						,							lowercase  :  Dict=True						,							lowercase  :  Optional[int]=True )	->						Union[str, Any]:


    """simple docstring"""





    snake_case    :      List[Any]      					=    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   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Tuple						,							lowercase  :  Tuple )	->						Tuple:


    """simple docstring"""





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









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Union[str, Any]						,							lowercase  :  List[str]						,							lowercase  :  Tuple						,							lowercase  :  List[Any]						,							lowercase  :  int )	->						List[Any]:


    """simple docstring"""





    if index == r:
        for j in range(lowercase ):
            print(data[j]						,							end=" " )
        print(" " )
        return
    #  When no more elements are there to put in data[]
    if i >= n:
        return
    # current is included, put next at next location
    snake_case    :      Union[str, Any]      					=    arr[i]
    combination_util(lowercase						,							lowercase						,							lowercase						,							index + 1						,							lowercase						,							i + 1 )
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							i + 1 )
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int] )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Any      					=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(lowercase						,							lowercase						,							lowercase						,							0						,							lowercase						,							0 )


if __name__ == "__main__":
  # Driver code to check the function above
  __snake_case   			=    [10, 20, 30, 40, 50]
  print_combination(arr, len(arr), 3)
  # This code is contributed by Ambuj sahu





