



import importlib

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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase=False		)       ->  Tuple:



    """simple docstring"""






    A :       Union[str, Any]          =   OmegaConf.load(_lowerCAmelCase		)
    if display:
        print(yaml.dump(OmegaConf.to_container(_lowerCAmelCase		)		)		)
    return config




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase=None      ,     _lowerCAmelCase=None		)       ->  Optional[int]:



    """simple docstring"""






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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Tuple:



    """simple docstring"""






    A     ,							A     ,							A :       Tuple          =   model.encode(_lowerCAmelCase		)
    print(f'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''		)
    A :       List[Any]          =   model.decode(_lowerCAmelCase		)
    return xrec




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase=False		)       ->  int:



    """simple docstring"""






    A     ,							A :       Optional[int]          =   string.rsplit("""."""      ,     1		)
    if reload:
        A :       str          =   importlib.import_module(_lowerCAmelCase		)
        importlib.reload(_lowerCAmelCase		)
    return getattr(importlib.import_module(_lowerCAmelCase      ,     package=_lowerCAmelCase		)      ,     cls		)




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=True      ,     _lowerCAmelCase=True		)       ->  int:



    """simple docstring"""






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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  List[Any]:



    """simple docstring"""






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







def 	__UpperCamelCase	(  _lowerCAmelCase = 100_0000		)       ->  int:



    """simple docstring"""






    A :       str          =   limit + 1
    A :       Tuple          =   [0] * limit
    for first_term in range(1      ,     _lowerCAmelCase		):
        for n in range(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		):
            A :       Any          =   first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    A :       Optional[int]          =   sum(1 for x in frequency[1:limit] if x == 10		)

    return count


if __name__ == "__main__":
     print(F"""{solution() = }""")


