






"""simple docstring"""


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if number < 0 or shift_amount < 0:
												raise ValueError('both inputs must be positive integers' )

						lowerCAmelCase   :Tuple						=			str(bin(a__ ) )
						binary_number += "0" * shift_amount
						return binary_number


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if number < 0 or shift_amount < 0:
												raise ValueError('both inputs must be positive integers' )

						lowerCAmelCase   :List[Any]						=			str(bin(a__ ) )[2:]
						if shift_amount >= len(a__ ):
												return "0b0"
						lowerCAmelCase   :Tuple						=			binary_number[: len(a__ ) - shift_amount]
						return "0b" + shifted_binary_number


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if number >= 0:  # Get binary representation of positive number
												lowerCAmelCase   :List[str]						=			"""0""" + str(bin(a__ ) ).strip('-' )[2:]
						else:  # Get binary (2's complement) representation of negative number
												lowerCAmelCase   :str						=			len(bin(a__ )[3:] )  # Find 2's complement of number
												lowerCAmelCase   :List[Any]						=			bin(abs(a__ ) - (1 << binary_number_length) )[3:]
												lowerCAmelCase   :Union[str, Any]						=			(
												    """1""" + """0""" * (binary_number_length - len(a__ )) + binary_number
												)

						if shift_amount >= len(a__ ):
												return "0b" + binary_number[0] * len(a__ )
						return (
						    "0b"
						    + binary_number[0] * shift_amount
						    + binary_number[: len(a__ ) - shift_amount]
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





import importlib

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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__						:						Optional[int]										=OmegaConf.load(lowerCamelCase )
				if display:
								print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) )
				return config




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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Optional[Any]										=model.encode(lowerCamelCase )
				print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
				__magic_name__						:						List[Any]										=model.decode(lowerCamelCase )
				return xrec




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__					,			__magic_name__						:						Optional[int]										=string.rsplit("""."""						,  1 )
				if reload:
								__magic_name__						:						Optional[int]										=importlib.import_module(lowerCamelCase )
								importlib.reload(lowerCamelCase )
				return getattr(importlib.import_module(lowerCamelCase						,  package=lowerCamelCase )						,  cls )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if "target" not in config:
								raise KeyError("""Expected key `target` to instantiate.""" )
				return get_obj_from_str(config["""target"""] )(**config.get("""params"""						,  {} ) )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=True						,  lowerCamelCase=True ):
				__magic_name__						:						str										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				# load the specified checkpoint
				if ckpt:
								__magic_name__						:						str										=torch.load(lowerCamelCase						,  map_location="""cpu""" )
								__magic_name__						:						Any										=pl_sd["""global_step"""]
								print(F"loaded model from global step {global_step}." )
				else:
								__magic_name__						:						List[Any]										={"""state_dict""": None}
								__magic_name__						:						Optional[Any]										=None
				__magic_name__						:						Tuple										=load_model_from_config(config.model						,  pl_sd["""state_dict"""]						,  gpu=lowerCamelCase						,  eval_mode=lowerCamelCase )["""model"""]
				return model, global_step




