code: 
  
  from transformers import (
      DPRContextEncoderTokenizer,
      DPRContextEncoderTokenizerFast,
      DPRQuestionEncoderTokenizer,
      DPRQuestionEncoderTokenizerFast,
      DPRReaderOutput,
      DPRReaderTokenizer,
      DPRReaderTokenizerFast,
  )
  from transformers.testing_utils import require_tokenizers, slow
  from transformers.tokenization_utils_base import BatchEncoding
  
  from ..bert.test_tokenization_bert import BertTokenizationTest
  
  @require_tokenizers
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      DPRContextEncoderTokenizer
  					__magic_name__     			=      DPRContextEncoderTokenizerFast
  					__magic_name__     			=      True
  
  @require_tokenizers
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      DPRQuestionEncoderTokenizer
  					__magic_name__     			=      DPRQuestionEncoderTokenizerFast
  					__magic_name__     			=      True
  
  @require_tokenizers
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      DPRReaderTokenizer
  					__magic_name__     			=      DPRReaderTokenizerFast
  					__magic_name__     			=      True
  
  
  
  
  
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Any		       =   self.tokenizer_class.from_pretrained("bert-base-uncased"	)
  
  						UpperCAmelCase__:  Dict		       =   tokenizer.encode("question sequence"							,			add_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  str		       =   tokenizer.encode("title sequence"							,			add_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  str		       =   tokenizer.encode("text sequence " * 4							,			add_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  List[str]		       =   [[1_0_1] + text_a + [1_0_2] + text_a + [1_0_2] + text_a]
  						UpperCAmelCase__:  str		       =   BatchEncoding({"input_ids": input_ids}	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   [[0] * len(input_ids[0]	)]
  						UpperCAmelCase__:  Tuple		       =   [[0] * len(input_ids[0]	)]
  						UpperCAmelCase__:  Optional[int]		       =   [0]
  						UpperCAmelCase__:  Any		       =   DPRReaderOutput(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)
  
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[Any]		       =   8, 9
  						UpperCAmelCase__:  str		       =   1_0
  						UpperCAmelCase__:  Dict		       =   1_0
  						UpperCAmelCase__:  List[Any]		       =   tokenizer.decode_best_spans(lowerCamelCase__							,			lowerCamelCase__	)
  						self.assertEqual(predicted_spans[0].start_index							,			lowerCamelCase__	)
  						self.assertEqual(predicted_spans[0].end_index							,			lowerCamelCase__	)
  						self.assertEqual(predicted_spans[0].doc_id							,			0	)
  
  
  
  
  
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[Any]		       =   self.tokenizer_class.from_pretrained("bert-base-uncased"	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   tokenizer.encode("question sequence"							,			add_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  List[Any]		       =   tokenizer.encode("title sequence"							,			add_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  List[Any]		       =   tokenizer.encode("text sequence"							,			add_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  Dict		       =   [1_0_1] + text_a + [1_0_2] + text_a + [1_0_2] + text_a
  						UpperCAmelCase__:  str		       =   tokenizer(questions=["question sequence"]							,			titles=["title sequence"]							,			texts=["text sequence"]	)
  						self.assertIn("input_ids"							,			lowerCamelCase__	)
  						self.assertIn("attention_mask"							,			lowerCamelCase__	)
  						self.assertListEqual(encoded_input["input_ids"][0]							,			lowerCamelCase__	)
style_context: 
  
  from __future__ import annotations
  
  from collections import Counter
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Optional[Any]		       =   [True] * n
  	UpperCAmelCase__:  Any		       =   False
  	UpperCAmelCase__:  Tuple		       =   False
  	UpperCAmelCase__:  Optional[Any]		       =   True
  
  	for i in range(3  ,int(n**0.5 + 1			)  ,2			):
  		UpperCAmelCase__:  Any		       =   i * 2
  		while index < n:
  			UpperCAmelCase__:  Optional[int]		       =   False
  			UpperCAmelCase__:  Tuple		       =   index + i
  
  	UpperCAmelCase__:  int		       =   [2]
  
  	for i in range(3  ,SCREAMING_SNAKE_CASE  ,2			):
  		if is_prime[i]:
  			primes.append(SCREAMING_SNAKE_CASE			)
  
  	return primes
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  List[Any]		       =   str(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Tuple		       =   []
  	UpperCAmelCase__:  Optional[int]		       =   ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
  
  	for duplicate in Counter(SCREAMING_SNAKE_CASE			) - Counter(set(SCREAMING_SNAKE_CASE			)			):
  		UpperCAmelCase__:  Optional[Any]		       =   [int(number_str.replace(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)			) for digit in digits]
  		replacements.append(SCREAMING_SNAKE_CASE			)
  
  	return replacements
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE = 8			):
  	UpperCAmelCase__:  Union[str, Any]		       =   set()
  
  	# Filter primes with less than 3 replaceable digits
  	UpperCAmelCase__:  Optional[Any]		       =   {
  	    x for x in set(prime_sieve(1_0_0_0_0_0_0			)			) if len(str(SCREAMING_SNAKE_CASE			)			) - len(set(str(SCREAMING_SNAKE_CASE			)			)			) >= 3
  	}
  
  	for prime in primes:
  		if prime in numbers_checked:
  			continue
  
  		UpperCAmelCase__:  Optional[int]		       =   digit_replacements(SCREAMING_SNAKE_CASE			)
  
  		for family in replacements:
  			numbers_checked.update(SCREAMING_SNAKE_CASE			)
  			UpperCAmelCase__:  Dict		       =   primes.intersection(SCREAMING_SNAKE_CASE			)
  
  			if len(SCREAMING_SNAKE_CASE			) != family_length:
  				continue
  
  			return min(SCREAMING_SNAKE_CASE			)
  
  	return -1
  
  
  if __name__ == "__main__":
  					print(solution())