
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						)			,  )






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))




