'''simple docstring'''




import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Any						=						BarthezTokenizer
      __UpperCamelCase:  Union[str, Any]						=						BarthezTokenizerFast
      __UpperCamelCase:  Dict						=						True
      __UpperCamelCase:  Any						=						True




      def      _A	(       self						:				Optional[Any]       ):
             super().setUp()

             _UpperCAmelCase     :  Union[str, Any]							      =    BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"       )
             tokenizer.save_pretrained(self.tmpdirname       )
             tokenizer.save_pretrained(self.tmpdirname						,					legacy_format=A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    tokenizer




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  str							      =    "<pad>"
             _UpperCAmelCase     :  int							      =    1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(A       )						,					A       )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(A       )						,					A       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    list(self.get_tokenizer().get_vocab().keys()       )

             self.assertEqual(vocab_keys[0]						,					"<s>"       )
             self.assertEqual(vocab_keys[1]						,					"<pad>"       )
             self.assertEqual(vocab_keys[-1]						,					"<mask>"       )
             self.assertEqual(len(A       )						,					101122       )




      def      _A	(       self						:				int       ):
             self.assertEqual(self.get_tokenizer().vocab_size						,					101122       )




      @require_torch
      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
             _UpperCAmelCase     :  Any							      =    [0, 57, 3018, 70307, 91, 2]

             _UpperCAmelCase     :  List[str]							      =    self.tokenizer(
                 A						,					max_length=len(A       )						,					padding=A						,					truncation=A						,					return_tensors="pt"       )
             self.assertIsInstance(A						,					A       )

             self.assertEqual((2, 6)						,					batch.input_ids.shape       )
             self.assertEqual((2, 6)						,					batch.attention_mask.shape       )
             _UpperCAmelCase     :  Dict							      =    batch.input_ids.tolist()[0]
             self.assertListEqual(A						,					A       )




      def      _A	(       self						:				List[str]       ):
             if not self.test_rust_tokenizer:
                    return

             _UpperCAmelCase     :  Optional[int]							      =    self.get_tokenizer()
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_rust_tokenizer()

             _UpperCAmelCase     :  List[Any]							      =    "I was born in 92000, and this is falsé."

             _UpperCAmelCase     :  str							      =    tokenizer.tokenize(A       )
             _UpperCAmelCase     :  Dict							      =    rust_tokenizer.tokenize(A       )
             self.assertListEqual(A						,					A       )

             _UpperCAmelCase     :  List[Any]							      =    tokenizer.encode(A						,					add_special_tokens=A       )
             _UpperCAmelCase     :  List[str]							      =    rust_tokenizer.encode(A						,					add_special_tokens=A       )
             self.assertListEqual(A						,					A       )

             _UpperCAmelCase     :  Dict							      =    self.get_rust_tokenizer()
             _UpperCAmelCase     :  Any							      =    tokenizer.encode(A       )
             _UpperCAmelCase     :  List[str]							      =    rust_tokenizer.encode(A       )
             self.assertListEqual(A						,					A       )





      @slow
      def      _A	(       self						:				List[Any]       ):
             # fmt: off
             _UpperCAmelCase     :  List[Any]							      =    {"input_ids": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
             # fmt: on

             # moussaKam/mbarthez is a french model. So we also use french texts.
             _UpperCAmelCase     :  int							      =    [
                 "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
                 "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
                 "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
                 "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
                 "telles que la traduction et la synthèse de texte.",
             ]

             self.tokenizer_integration_test_util(
                 expected_encoding=A						,					model_name="moussaKam/mbarthez"						,					revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"						,					sequences=A						,					)


'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      list    )    ->    list:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    len(_UpperCAmelCase    )
       for _ in range(_UpperCAmelCase    ):
              for i in range(_ % 2			,	arr_size - 1			,	2    ):
                     if arr[i + 1] < arr[i]:
                            _UpperCAmelCase      ,  _UpperCAmelCase     :  int							      =    arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  list(range(10, 0, -1))
  print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')

