




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase = False ):
				if n == 2:
								return True
				if not n % 2 or n < 2:
								return False
				if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
								return False
				if n > 3317044064679887385961981 and not allow_probable:
								raise ValueError(
								    """Warning: upper bound of deterministic test is exceeded. """
								    """Pass allow_probable=True to allow probabilistic test. """
								    """A return value of True indicates a probable prime.""" )
				# array bounds provided by analysis
				__magic_name__						:						Optional[Any]										=[
				    2047,
				    1373653,
				    25326001,
				    3215031751,
				    2152302898747,
				    3474749660383,
				    341550071728321,
				    1,
				    3825123056546413051,
				    1,
				    1,
				    318665857834031151167461,
				    3317044064679887385961981,
				]

				__magic_name__						:						Dict										=[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
				for idx, _p in enumerate(lowerCamelCase						,  1 ):
								if n < _p:
												# then we have our last prime to check
												__magic_name__						:						Union[str, Any]										=primes[:idx]
												break
				__magic_name__					,			__magic_name__						:						Tuple										=n - 1, 0
				# break up n -1 into a power of 2 (s) and
				# remaining odd component
				# essentially, solve for d * 2 ** s == n - 1
				while d % 2 == 0:
								d //= 2
								s += 1
				for prime in plist:
								__magic_name__						:						Tuple										=False
								for r in range(lowerCamelCase ):
												__magic_name__						:						int										=pow(lowerCamelCase						,  d * 2**r						,  lowerCamelCase )
												# see article for analysis explanation for m
												if (r == 0 and m == 1) or ((m + 1) % n == 0):
																__magic_name__						:						Optional[int]										=True
																# this loop will not determine compositeness
																break
								if pr:
												continue
								# if pr is False, then the above loop never evaluated to true,
								# and the n MUST be composite
								return False
				return True




def 		lowerCAmelCase_ (						):
				assert not miller_rabin(561 )
				assert miller_rabin(563 )
				# 2047

				assert not miller_rabin(838201 )
				assert miller_rabin(838207 )
				# 1_373_653

				assert not miller_rabin(17316001 )
				assert miller_rabin(17316017 )
				# 25_326_001

				assert not miller_rabin(3078386641 )
				assert miller_rabin(3078386653 )
				# 3_215_031_751

				assert not miller_rabin(1713045574801 )
				assert miller_rabin(1713045574819 )
				# 2_152_302_898_747

				assert not miller_rabin(2779799728307 )
				assert miller_rabin(2779799728327 )
				# 3_474_749_660_383

				assert not miller_rabin(113850023909441 )
				assert miller_rabin(113850023909527 )
				# 341_550_071_728_321

				assert not miller_rabin(1275041018848804351 )
				assert miller_rabin(1275041018848804391 )
				# 3_825_123_056_546_413_051

				assert not miller_rabin(79666464458507787791867 )
				assert miller_rabin(79666464458507787791951 )
				# 318_665_857_834_031_151_167_461

				assert not miller_rabin(552840677446647897660333 )
				assert miller_rabin(552840677446647897660359 )
				# 3_317_044_064_679_887_385_961_981
				# upper limit for probabilistic test


if __name__ == "__main__":
							test_miller_rabin()










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




