





import importlib

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







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a=False    ) -> Dict:
					__A      :		Union[str, Any]         =							OmegaConf.load(lowerCamelCase__    )
					if display:
										print(yaml.dump(OmegaConf.to_container(lowerCamelCase__    )    )    )
					return config







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a=None   ,		a=None    ) -> Optional[int]:
					if conf_path is None:
										__A      :		Dict         =							'./model_checkpoints/vqgan_only.yaml'
					__A      :		Optional[int]         =							load_config(lowerCamelCase__   ,		display=lowerCamelCase__    )
					__A      :		Union[str, Any]         =							VQModel(**config.model.params    )
					if ckpt_path is None:
										__A      :		Any         =							'./model_checkpoints/vqgan_only.pt'
					__A      :		Optional[Any]         =							torch.load(lowerCamelCase__   ,		map_location=lowerCamelCase__    )
					if ".ckpt" in ckpt_path:
										__A      :		Union[str, Any]         =							sd['state_dict']
					model.load_state_dict(lowerCamelCase__   ,		strict=lowerCamelCase__    )
					model.to(lowerCamelCase__    )
					del sd
					return model







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a    ) -> List[str]:
					__A       ,							__A       ,							__A      :		List[str]         =							model.encode(lowerCamelCase__    )
					print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""    )
					__A      :		Any         =							model.decode(lowerCamelCase__    )
					return xrec







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a=False    ) -> Tuple:
					__A       ,							__A      :		Tuple         =							string.rsplit('.'   ,		1    )
					if reload:
										__A      :		Dict         =							importlib.import_module(lowerCamelCase__    )
										importlib.reload(lowerCamelCase__    )
					return getattr(importlib.import_module(lowerCamelCase__   ,		package=lowerCamelCase__    )   ,		cls    )







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







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a=True   ,		a=True    ) -> Optional[int]:
					__A      :		Optional[int]         =							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 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a   ,		a    ) -> List[str]:
					# load the specified checkpoint
					if ckpt:
										__A      :		Any         =							torch.load(lowerCamelCase__   ,		map_location='cpu'    )
										__A      :		str         =							pl_sd['global_step']
										print(F"""loaded model from global step {global_step}."""    )
					else:
										__A      :		List[str]         =							{'state_dict': None}
										__A      :		Optional[Any]         =							None
					__A      :		Union[str, Any]         =							load_model_from_config(config.model   ,		pl_sd['state_dict']   ,		gpu=lowerCamelCase__   ,		eval_mode=lowerCamelCase__    )['model']
					return model, global_step






from __future__ import annotations

import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

				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(lowerCamelCase__       ) + 1       )      ,					6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True


__A							=[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if not isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
								raise ValueError("n must be an integer"       )
				if n <= 0:
								raise ValueError("n must be >= 0"       )

				lowerCamelCase_         =     []
				for num in range(len(lowerCamelCase__       )       ):
								lowerCamelCase_         =     0
								while 2 * i * i <= odd_composites[num]:
												lowerCamelCase_         =     odd_composites[num] - 2 * i * i
												if is_prime(lowerCamelCase__       ):
																break
												i += 1
								else:
												list_nums.append(odd_composites[num]       )
												if len(lowerCamelCase__       ) == n:
																return list_nums

				return []





def      lowerCamelCase_					(							):
				return compute_nums(1       )[0]


if __name__ == "__main__":
			print(F"""{solution() = }""")


