
'''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     lowercase_			(_SCREAMING_SNAKE_CASE       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[int]           =    BarthezTokenizer
  SCREAMING_SNAKE_CASE					:						Dict           =    BarthezTokenizerFast
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    True
  SCREAMING_SNAKE_CASE					:						Dict           =    True






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

   __lowercase													=				BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''    )
   tokenizer.save_pretrained(self.tmpdirname    )
   tokenizer.save_pretrained(self.tmpdirname      ,legacy_format=A_    )
   __lowercase													=				tokenizer






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				'''<pad>'''
   __lowercase													=				1

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				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_    )      ,1_0_1_1_2_2    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   self.assertEqual(self.get_tokenizer().vocab_size      ,1_0_1_1_2_2    )






  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
   __lowercase													=				[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

   __lowercase													=				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    )
   __lowercase													=				batch.input_ids.tolist()[0]
   self.assertListEqual(A_      ,A_    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   if not self.test_rust_tokenizer:
    return

   __lowercase													=				self.get_tokenizer()
   __lowercase													=				self.get_rust_tokenizer()

   __lowercase													=				'''I was born in 92000, and this is falsé.'''

   __lowercase													=				tokenizer.tokenize(A_    )
   __lowercase													=				rust_tokenizer.tokenize(A_    )
   self.assertListEqual(A_      ,A_    )

   __lowercase													=				tokenizer.encode(A_      ,add_special_tokens=A_    )
   __lowercase													=				rust_tokenizer.encode(A_      ,add_special_tokens=A_    )
   self.assertListEqual(A_      ,A_    )

   __lowercase													=				self.get_rust_tokenizer()
   __lowercase													=				tokenizer.encode(A_    )
   __lowercase													=				rust_tokenizer.encode(A_    )
   self.assertListEqual(A_      ,A_    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				{'''input_ids''': [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 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.
   __lowercase													=				[
       '''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 	_A  (					):






 """simple docstring"""



 for n in range(1  ,       1000000   ):
  yield n * (n + 1) // 2


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				1
 __lowercase													=				2
 while i * i <= n:
  __lowercase													=				0
  while n % i == 0:
   n //= i
   multiplicity += 1
  divisors_count *= multiplicity + 1
  i += 1
 if n > 1:
  divisors_count *= 2
 return divisors_count


def 	_A  (					):






 """simple docstring"""



 return next(i for i in triangle_number_generator() if count_divisors(A__   ) > 500   )


if __name__ == "__main__":
 print(solution())

