
import importlib

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

def      A (       A_							:					Dict    ,							A_							:					Optional[Any]=False       ):
						snake_case       :     List[Any] =    OmegaConf.load(__UpperCAmelCase       )
						if display:
												print(yaml.dump(OmegaConf.to_container(__UpperCAmelCase       )       )       )
						return config

def      A (       A_							:					Dict    ,							A_							:					Optional[int]=None    ,							A_							:					Optional[Any]=None       ):







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

def      A (       A_							:					List[str]    ,							A_							:					int       ):
						snake_case,					snake_case,					snake_case       :     Dict =    model.encode(__UpperCAmelCase       )
						print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""       )
						snake_case       :     List[Any] =    model.decode(__UpperCAmelCase       )
						return xrec

def      A (       A_							:					Tuple    ,							A_							:					int=False       ):
						snake_case,					snake_case       :     Optional[Any] =    string.rsplit('''.'''    ,							1       )
						if reload:
												snake_case       :     Any =    importlib.import_module(__UpperCAmelCase       )
												importlib.reload(__UpperCAmelCase       )
						return getattr(importlib.import_module(__UpperCAmelCase    ,							package=__UpperCAmelCase       )    ,							cls       )

def      A (       A_							:					List[str]       ):







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

def      A (       A_							:					str    ,							A_							:					Optional[Any]    ,							A_							:					int=True    ,							A_							:					Union[str, Any]=True       ):
						snake_case       :     Any =    instantiate_from_config(__UpperCAmelCase       )
						if sd is not None:
												model.load_state_dict(__UpperCAmelCase       )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}





def      A (       A_							:					int    ,							A_							:					Dict    ,							A_							:					Dict    ,							A_							:					str       ):







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



'''simple docstring'''





from __future__ import annotations

import math

def      A (       A_							:					int       ):

						if num <= 0:
												snake_case       :     List[Any] =    F"""{num}: Invalid input, please enter a positive integer."""
												raise ValueError(A_       )

						snake_case       :     Optional[Any] =    [True] * (num + 1)
						snake_case       :     List[str] =    []
						snake_case       :     List[Any] =    2
						snake_case       :     str =    int(math.sqrt(A_       )       )

						while start <= end:
												# If start is a prime
												if sieve[start] is True:
																		prime.append(A_       )

																		# Set multiples of start be False
																		for i in range(start * start    ,							num + 1    ,							A_       ):
																								if sieve[i] is True:
																														snake_case       :     int =    False

												start += 1

						for j in range(end + 1    ,							num + 1       ):
												if sieve[j] is True:
																		prime.append(A_       )

						return prime


if __name__ == "__main__":
						print(prime_sieve(int(input("Enter a positive integer: ").strip())))

