
"""simple docstring"""



import importlib

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


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

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


def 			_UpperCamelCase						(    _A       ,   _A=None       ,   _A=None   )     ->	Any:





    """simple docstring"""

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


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = model.encode(A__   )
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
    _UpperCAmelCase        = model.decode(A__   )
    return xrec


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	List[str]:





    """simple docstring"""

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


def 			_UpperCamelCase						(    _A   )     ->	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						(    _A       ,   _A       ,   _A=True       ,   _A=True   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = 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 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	str:





    """simple docstring"""

    if ckpt:
        _UpperCAmelCase        = torch.load(A__       ,   map_location="""cpu"""   )
        _UpperCAmelCase        = pl_sd["global_step"]
        print(F"""loaded model from global step {global_step}."""   )
    else:
        _UpperCAmelCase        = {"state_dict": None}
        _UpperCAmelCase        = None
    _UpperCAmelCase        = load_model_from_config(config.model       ,   pl_sd["""state_dict"""]       ,   gpu=A__       ,   eval_mode=A__   )["model"]
    return model, global_step







"""simple docstring"""
def 							snake_case (  A__		):
			UpperCAmelCase_	:					int     	=   [0 for i in range(len(A__		)		)]

			# initialize interval's left pointer and right pointer
			UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   0, 0

			for i in range(1      ,len(A__		)		):
						# case when current index is inside the interval
						if i <= right_pointer:
									UpperCAmelCase_	:					Any     	=   min(right_pointer - i + 1      ,z_result[i - left_pointer]		)
									UpperCAmelCase_	:					Union[str, Any]     	=   min_edge

						while go_next(A__      ,A__      ,A__		):
									z_result[i] += 1

						# if new index's result gives us more right interval,
						# we've to update left_pointer and right_pointer
						if i + z_result[i] - 1 > right_pointer:
									UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   i, i + z_result[i] - 1

			return z_result

def 							snake_case (  A__      ,A__      ,A__		):
			return i + z_result[i] < len(A__		) and s[z_result[i]] == s[i + z_result[i]]

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			UpperCAmelCase_	:					Tuple     	=   z_function(pattern + input_str		)

			for val in z_result:
						# if value is greater then length of the pattern string
						# that means this index is starting position of substring
						# which is equal to pattern string
						if val >= len(A__		):
									answer += 1

			return answer


if __name__ == "__main__":
			import doctest

			doctest.testmod()



