code: 
  
  
  '''simple docstring'''
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Dict = 30   ):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						exponent + 2
     UpperCamelCase__ 						=						(1 + 5**0.5) / 2
     UpperCamelCase__ 						=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5
  
     return int(_UpperCamelCase   )
  
  
  if __name__ == "__main__":
         print(F"""{solution() = }""")
style_context: 
  
  
  '''simple docstring'''
  
  from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
  from transformers.testing_utils import require_tokenizers, slow
  
  from ..bert.test_tokenization_bert import BertTokenizationTest
  
  
  
  
  
  
  
  @require_tokenizers
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Optional[int]    					=					SqueezeBertTokenizer
   _lowerCamelCase :					List[Any]    					=					SqueezeBertTokenizerFast
   _lowerCamelCase :					int    					=					True
  
   def 		lowercase_						( self :	List[str],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname,					**a_					)
  
   @slow
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"					)
  
      UpperCamelCase__ 						=						tokenizer.encode("sequence builders",					add_special_tokens=a_					)
      UpperCamelCase__ 						=						tokenizer.encode("multi-sequence build",					add_special_tokens=a_					)
  
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_					)
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_,					a_					)
  
      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]