code: 
  import unittest
  import warnings
  from dataclasses import dataclass
  
  from transformers.convert_slow_tokenizer import SpmConverter
  from transformers.testing_utils import get_tests_dir
  
  
  
  
  @dataclass
  class      __lowercase	:
  	_A   			=      42
  
  
  
  
  class      __lowercase	(	unittest.TestCase					):
  
  
  
  
  
  
  	def 			_a(self							:   Dict						)   -> Tuple:
  							_lowercase					:  List[str]    			=      get_tests_dir("fixtures/test_sentencepiece.model"						)
  							_lowercase					:  Dict    			=      get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"						)
  
  							_lowercase					:  Tuple    			=      FakeOriginalTokenizer(vocab_file=snake_case						)
  
  							with warnings.catch_warnings(record=snake_case						) as w:
  													_lowercase					:  Optional[Any]    			=      SpmConverter(snake_case						)
  							self.assertEqual(len(snake_case						)			,  0						)
  
  							_lowercase					:  int    			=      FakeOriginalTokenizer(vocab_file=snake_case						)
  
  							with warnings.catch_warnings(record=snake_case						) as w:
  													_lowercase					:  List[Any]    			=      SpmConverter(snake_case						)
  							self.assertEqual(len(snake_case						)			,  1						)
  
  							self.assertIn(
  							    "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
  							    " which is not implemented in the fast tokenizers."			,  str(w[0].message						)			,  )
  
  
  
  
  
style_context: 
  from __future__ import annotations
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      list[int]   , _UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase			,						_lowercase					:  Any    			=      0, len(_UpperCAmelCase     ) - 1
  						while start_ind <= end_ind:
  												if array[start_ind] == search_item:
  																		return start_ind
  												elif array[end_ind] == search_item:
  																		return end_ind
  												else:
  																		start_ind += 1
  																		end_ind -= 1
      # returns -1 if search_item is not found in array
  						return -1
  
  
  if __name__ == "__main__":
  							print(double_linear_search(list(range(100)), 40))
  
  
  
  
  