






import random
def 						_a    (      a :int )							->			bool:
 a		  =      num - 1
 a		  =      0

 while s % 2 == 0:
  a		  =      s // 2
  t += 1

 for _ in range(5 ):
  a		  =      random.randrange(2	,							num - 1 )
  a		  =      pow(a	,							a	,							a )
  if v != 1:
   a		  =      0
   while v != (num - 1):
    if i == t - 1:
     return False
    else:
     a		  =      i + 1
     a		  =      (v**2) % num
 return True
def 						_a    (      a :int )							->			bool:
 if num < 2:
  return False

 a		  =      [
     2,
     3,
     5,
     7,
     11,
     13,
     17,
     19,
     23,
     29,
     31,
     37,
     41,
     43,
     47,
     53,
     59,
     61,
     67,
     71,
     73,
     79,
     83,
     89,
     97,
     101,
     103,
     107,
     109,
     113,
     127,
     131,
     137,
     139,
     149,
     151,
     157,
     163,
     167,
     173,
     179,
     181,
     191,
     193,
     197,
     199,
     211,
     223,
     227,
     229,
     233,
     239,
     241,
     251,
     257,
     263,
     269,
     271,
     277,
     281,
     283,
     293,
     307,
     311,
     313,
     317,
     331,
     337,
     347,
     349,
     353,
     359,
     367,
     373,
     379,
     383,
     389,
     397,
     401,
     409,
     419,
     421,
     431,
     433,
     439,
     443,
     449,
     457,
     461,
     463,
     467,
     479,
     487,
     491,
     499,
     503,
     509,
     521,
     523,
     541,
     547,
     557,
     563,
     569,
     571,
     577,
     587,
     593,
     599,
     601,
     607,
     613,
     617,
     619,
     631,
     641,
     643,
     647,
     653,
     659,
     661,
     673,
     677,
     683,
     691,
     701,
     709,
     719,
     727,
     733,
     739,
     743,
     751,
     757,
     761,
     769,
     773,
     787,
     797,
     809,
     811,
     821,
     823,
     827,
     829,
     839,
     853,
     857,
     859,
     863,
     877,
     881,
     883,
     887,
     907,
     911,
     919,
     929,
     937,
     941,
     947,
     953,
     967,
     971,
     977,
     983,
     991,
     997,
 ]

 if num in low_primes:
  return True

 for prime in low_primes:
  if (num % prime) == 0:
   return False

 return rabin_miller(a )
def 						_a    (      a :int = 1_024 )							->			int:
 while True:
  a		  =      random.randrange(2 ** (keysize - 1)	,							2 ** (keysize) )
  if is_prime_low_num(a ):
   return num


if __name__ == "__main__":
     UpperCAmelCase__				   =		generate_large_prime()
     print(("Prime number:", num))
     print(("is_prime_low_num:", is_prime_low_num(num)))





'''simple docstring'''
import importlib

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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     if display:
          print(yaml.dump(OmegaConf.to_container(_UpperCamelCase				)				)				)
     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]=None							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			Optional[Any]:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model.encode(_UpperCamelCase				)
     print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
     _SCREAMING_SNAKE_CASE							    =model.decode(_UpperCamelCase				)
     return xrec


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =string.rsplit('.'							, 1				)
     if reload:
          _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)
          importlib.reload(_UpperCamelCase				)
     return getattr(importlib.import_module(_UpperCamelCase							, package=_UpperCamelCase				)							, cls				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			List[str]:
     """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        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[str]=True							, _UpperCamelCase						:     int=True				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =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        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]				)      ->			List[Any]:
     """simple docstring"""




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