





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 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     int		     =      BarthezTokenizer
     __snake_case   :     Union[str, Any]		     =      BarthezTokenizerFast
     __snake_case   :     List[Any]		     =      True
     __snake_case   :     str		     =      True






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Any:




           '''simple docstring'''


           super().setUp()

           SCREAMING_SNAKE_CASE		=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""	)
           tokenizer.save_pretrained(self.tmpdirname	)
           tokenizer.save_pretrained(self.tmpdirname						,legacy_format=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						tokenizer






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""<pad>"""
           SCREAMING_SNAKE_CASE		=						1

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						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(lowerCamelCase__	)						,101122	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[str]:




           '''simple docstring'''


           self.assertEqual(self.get_tokenizer().vocab_size						,101122	)






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           SCREAMING_SNAKE_CASE		=						[0, 57, 3018, 70307, 91, 2]

           SCREAMING_SNAKE_CASE		=						self.tokenizer(
               lowerCamelCase__						,max_length=len(lowerCamelCase__	)						,padding=lowerCamelCase__						,truncation=lowerCamelCase__						,return_tensors="""pt"""	)
           self.assertIsInstance(lowerCamelCase__						,lowerCamelCase__	)

           self.assertEqual((2, 6)						,batch.input_ids.shape	)
           self.assertEqual((2, 6)						,batch.attention_mask.shape	)
           SCREAMING_SNAKE_CASE		=						batch.input_ids.tolist()[0]
           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           if not self.test_rust_tokenizer:
                 return

           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()
           SCREAMING_SNAKE_CASE		=						self.get_rust_tokenizer()

           SCREAMING_SNAKE_CASE		=						"""I was born in 92000, and this is falsé."""

           SCREAMING_SNAKE_CASE		=						tokenizer.tokenize(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rust_tokenizer.tokenize(lowerCamelCase__	)
           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						tokenizer.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rust_tokenizer.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__	)
           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE		=						tokenizer.encode(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rust_tokenizer.encode(lowerCamelCase__	)
           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{"""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.
           SCREAMING_SNAKE_CASE		=						[
               """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=lowerCamelCase__						,model_name="""moussaKam/mbarthez"""						,revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""						,sequences=lowerCamelCase__						,)









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 1			,						_SCREAMING_SNAKE_CASE = 10_00     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						1
      SCREAMING_SNAKE_CASE		=						0

      for divide_by_number in range(_SCREAMING_SNAKE_CASE			,						digit + 1     ):
            SCREAMING_SNAKE_CASE		=						[]
            SCREAMING_SNAKE_CASE		=						numerator
            for _ in range(1			,						digit + 1     ):
                  if now_divide in has_been_divided:
                        if longest_list_length < len(_SCREAMING_SNAKE_CASE     ):
                              SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE     )
                              SCREAMING_SNAKE_CASE		=						divide_by_number
                  else:
                        has_been_divided.append(_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						now_divide * 10 % divide_by_number

      return the_digit


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()


