code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  from collections import Counter
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  int:
  						_a     =			[True] * n
  						_a     =			False
  						_a     =			False
  						_a     =			True
  
  						for i in range(3							,     int(n**0.5 + 1				)							,     2				):
  												_a     =			i * 2
  												while index < n:
  																		_a     =			False
  																		_a     =			index + i
  
  						_a     =			[2]
  
  						for i in range(3							,     _UpperCAmelCase							,     2				):
  												if is_prime[i]:
  																		primes.append(_UpperCAmelCase				)
  
  						return primes
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  int:
  						_a     =			str(_UpperCAmelCase				)
  						_a     =			[]
  						_a     =			["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
  
  						for duplicate in Counter(_UpperCAmelCase				) - Counter(set(_UpperCAmelCase				)				):
  												_a     =			[int(number_str.replace(_UpperCAmelCase							,     _UpperCAmelCase				)				) for digit in digits]
  												replacements.append(_UpperCAmelCase				)
  
  						return replacements
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase = 8				)    ->  Union[str, Any]:
  						_a     =			set()
  
  						# Filter primes with less than 3 replaceable digits
  						_a     =			{
  						    x for x in set(prime_sieve(1000000				)				) if len(str(_UpperCAmelCase				)				) - len(set(str(_UpperCAmelCase				)				)				) >= 3
  						}
  
  						for prime in primes:
  												if prime in numbers_checked:
  																		continue
  
  												_a     =			digit_replacements(_UpperCAmelCase				)
  
  												for family in replacements:
  																		numbers_checked.update(_UpperCAmelCase				)
  																		_a     =			primes.intersection(_UpperCAmelCase				)
  
  																		if len(_UpperCAmelCase				) != family_length:
  																								continue
  
  																		return min(_UpperCAmelCase				)
  
  						return -1
  
  
  if __name__ == "__main__":
  		print(solution())
  
  
  
style_context: 
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
  						_a     =			min(img.size				)
  
  						if s < target_image_size:
  												raise ValueError(f"""min dim for image {s} < {target_image_size}"""				)
  
  						_a     =			target_image_size / s
  						_a     =			(round(r * img.size[1]				), round(r * img.size[0]				))
  						_a     =			TF.resize(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
  						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
  						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
  						return img
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
  						_a     =			2.0 * x - 1.0
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
  						_a     =			x.detach().cpu()
  						if process:
  												_a     =			post_process_tensor(_UpperCAmelCase				)
  						_a     =			x.numpy()
  						if process:
  												_a     =			(255 * x).astype(np.uinta				)
  						_a     =			Image.fromarray(_UpperCAmelCase				)
  						if not x.mode == mode:
  												_a     =			x.convert(_UpperCAmelCase				)
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
  						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
  						_a     =			(x + 1.0) / 2.0
  						_a     =			x.permute(1							,     2							,     0				)
  						return x
  
  
  
  
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
  						_a     =			post_process_tensor(x.squeeze()				)
  						return x.permute(2							,     0							,     1				).unsqueeze(0				)
  
  
  