


import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=False						)     ->			Optional[Any]:







 """simple docstring"""


 _lowercase							      =OmegaConf.load(_UpperCamelCase						)
 if display:
  print(yaml.dump(OmegaConf.to_container(_UpperCamelCase						)						)						)
 return config
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=None     ,						__snake_case=None						)     ->			Optional[Any]:







 """simple docstring"""


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







 """simple docstring"""


 _lowercase      ,    _lowercase      ,    _lowercase							      =model.encode(_UpperCamelCase						)
 print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"						)
 _lowercase							      =model.decode(_UpperCamelCase						)
 return xrec
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=False						)     ->			int:







 """simple docstring"""


 _lowercase      ,    _lowercase							      =string.rsplit('''.'''     ,						1						)
 if reload:
  _lowercase							      =importlib.import_module(_UpperCamelCase						)
  importlib.reload(_UpperCamelCase						)
 return getattr(importlib.import_module(_UpperCamelCase     ,						package=_UpperCamelCase						)     ,						cls						)
def 				UpperCAmelCase_					(							__snake_case						)     ->			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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=True     ,						__snake_case=True						)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 if ckpt:
  _lowercase							      =torch.load(_UpperCamelCase     ,						map_location='''cpu'''						)
  _lowercase							      =pl_sd['''global_step''']
  print(F"loaded model from global step {global_step}."						)
 else:
  _lowercase							      ={'''state_dict''': None}
  _lowercase							      =None
 _lowercase							      =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        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			bool:
     """simple docstring"""




     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5							, int(math.sqrt(_UpperCamelCase				) + 1				)							, 6				):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =str(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =[n]
     for i in range(1							, len(_UpperCamelCase				)				):
          list_nums.append(int(str_num[i:]				)				)
          list_nums.append(int(str_num[:-i]				)				)
     return list_nums


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			bool:
     """simple docstring"""




     if len(str(_UpperCamelCase				)				) > 3:
          if not is_prime(int(str(_UpperCamelCase				)[-3:]				)				) or not is_prime(int(str(_UpperCamelCase				)[:3]				)				):
               return False
     return True


def        _lowerCAmelCase					(						_UpperCamelCase						:     int = 11				)      ->			list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =13
     while len(_UpperCamelCase				) != count:
          if validate(_UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =list_truncated_nums(_UpperCamelCase				)
               if all(is_prime(_UpperCamelCase				) for i in list_nums				):
                    list_truncated_primes.append(_UpperCamelCase				)
          num += 2
     return list_truncated_primes


def        _lowerCAmelCase					(						)      ->			int:
     """simple docstring"""




     return sum(compute_truncated_primes(11				)				)


if __name__ == "__main__":
     print(f'''{sum(compute_truncated_primes(1_1)) = }''')
