


"""simple docstring"""






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case		=       get_tests_dir('''fixtures/test_sentencepiece.model''')
__snake_case		=       get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

__snake_case		=       '''pt''' if is_torch_available() else '''tf'''


@require_sentencepiece
@require_tokenizers
class     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Dict    =		CamembertTokenizer
     A_       :	int    =		CamembertTokenizerFast
     A_       :	int    =		True
     A_       :	int    =		True


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          super().setUp()

          # We have a SentencePiece fixture for testing
          _a    	=				CamembertTokenizer(__UpperCAmelCase							)
          tokenizer.save_pretrained(self.tmpdirname							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				'''<pad>'''
          _a    	=				1

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


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				list(self.get_tokenizer().get_vocab().keys()							)

          self.assertEqual(vocab_keys[0]    ,				'''<s>NOTUSED'''							)
          self.assertEqual(vocab_keys[1]    ,				'''<pad>'''							)
          self.assertEqual(vocab_keys[-1]    ,				'''<mask>'''							)
          self.assertEqual(len(__UpperCAmelCase							)    ,				1004							)


     def 							_UpperCAmelCase      (      self							)       ->							int:
          self.assertEqual(self.get_tokenizer().vocab_size    ,				1005							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				CamembertTokenizer(__UpperCAmelCase							)
          tokenizer.save_pretrained(self.tmpdirname							)
          _a    	=				CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

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

          _a    	=				tokenizer.encode(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          # <unk> tokens are not the same for `rust` than for `slow`.
          # Because spm gives back raw token instead of `unk` in EncodeAsPieces
          # tokens = tokenizer.tokenize(sequence)
          _a    	=				tokenizer.convert_ids_to_tokens(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.tokenize(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          if not self.test_rust_tokenizer:
               return

          _a    	=				self.get_tokenizer()
          _a    	=				self.get_rust_tokenizer()

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

          _a    	=				tokenizer.tokenize(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.tokenize(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				self.get_rust_tokenizer()
          _a    	=				tokenizer.encode(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							int:
          # fmt: off
          _a    	=				{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''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, 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]]}  # noqa: E501
          # fmt: on

          # camembert is a french model. So we also use french texts.
          _a    	=				[
              '''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=__UpperCAmelCase    ,				model_name='''camembert-base'''    ,				revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''    ,				sequences=__UpperCAmelCase    ,				)



"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					int = 10**12       ):






     """simple docstring"""

     _a    	=				1
     _a    	=				0

     _a    	=				1
     _a    	=				1

     while numerator <= 2 * min_total - 1:
          prev_numerator += 2 * numerator
          numerator += 2 * prev_numerator

          prev_denominator += 2 * denominator
          denominator += 2 * prev_denominator

     return (denominator + 1) // 2


if __name__ == "__main__":
   print(f'{solution() = }')