





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


__UpperCAmelCase	:       List[str]    	=							get_tests_dir("fixtures/test_sentencepiece.model")
__UpperCAmelCase	:       Tuple    	=							get_tests_dir("fixtures/test_sentencepiece_bpe.model")

__UpperCAmelCase	:       str    	=							"pt" if is_torch_available() else "tf"







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





       '''simple docstring'''







       lowerCAmelCase__						=					CamembertTokenizer
       lowerCAmelCase__						=					CamembertTokenizerFast
       lowerCAmelCase__						=					True
       lowerCAmelCase__						=					True




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         super().setUp()

         # We have a SentencePiece fixture for testing
         __snake_case:						Union[str, Any]			  =						CamembertTokenizer(A		)
         tokenizer.save_pretrained(self.tmpdirname		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Optional[Any]			  =						"""<pad>"""
         __snake_case:						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 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Any			  =						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(A		)							,    1_004		)




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         self.assertEqual(self.get_tokenizer().vocab_size							,    1_005		)




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						int			  =						CamembertTokenizer(A		)
         tokenizer.save_pretrained(self.tmpdirname		)
         __snake_case:						Dict			  =						CamembertTokenizerFast.from_pretrained(self.tmpdirname		)

         __snake_case:						Union[str, Any]			  =						"""I was born in 92000, and this is falsé."""

         __snake_case:						List[Any]			  =						tokenizer.encode(A		)
         __snake_case:						Tuple			  =						rust_tokenizer.encode(A		)
         self.assertListEqual(A							,    A		)

         __snake_case:						Dict			  =						tokenizer.encode(A							,    add_special_tokens=A		)
         __snake_case:						Union[str, Any]			  =						rust_tokenizer.encode(A							,    add_special_tokens=A		)
         self.assertListEqual(A							,    A		)

         # <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)
         __snake_case:						Tuple			  =						tokenizer.convert_ids_to_tokens(A		)
         __snake_case:						Any			  =						rust_tokenizer.tokenize(A		)
         self.assertListEqual(A							,    A		)




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

         __snake_case:						Any			  =						self.get_tokenizer()
         __snake_case:						Tuple			  =						self.get_rust_tokenizer()

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

         __snake_case:						Optional[int]			  =						tokenizer.tokenize(A		)
         __snake_case:						str			  =						rust_tokenizer.tokenize(A		)
         self.assertListEqual(A							,    A		)

         __snake_case:						str			  =						tokenizer.encode(A							,    add_special_tokens=A		)
         __snake_case:						Dict			  =						rust_tokenizer.encode(A							,    add_special_tokens=A		)
         self.assertListEqual(A							,    A		)

         __snake_case:						Tuple			  =						self.get_rust_tokenizer()
         __snake_case:						str			  =						tokenizer.encode(A		)
         __snake_case:						Any			  =						rust_tokenizer.encode(A		)
         self.assertListEqual(A							,    A		)






       @slow
       def 							UpperCAmelCase__			(  self			:   List[str]		):
         # fmt: off
         __snake_case:						Tuple			  =						{"""input_ids""": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 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, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 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.
         __snake_case:						Optional[Any]			  =						[
             """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="""camembert-base"""							,    revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""							,    sequences=A							,    )













import math







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   int:

  if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]			  =						F'''Input value of [number={number}] must be an integer'''
    raise TypeError(SCREAMING_SNAKE_CASE__)

  if number < 1:
    __snake_case:						Optional[int]			  =						F'''Input value of [number={number}] must be > 0'''
    raise ValueError(SCREAMING_SNAKE_CASE__)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    __snake_case:						List[Any]			  =						int(math.log(number // 3 , 2)) + 2

    __snake_case:						str			  =						[3, 5]
    __snake_case:						int			  =						2
    __snake_case:						List[str]			  =						3
    for block in range(1 , SCREAMING_SNAKE_CASE__):
      for _ in range(SCREAMING_SNAKE_CASE__):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1])
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      for number in range(11):
            __UpperCAmelCase	:       Optional[int]    	=							0
            try:
                  __UpperCAmelCase	:       int    	=							proth(number)
            except ValueError:
                  print(f'ValueError: there is no {number}th Proth number')
                  continue

            print(f'The {number}th Proth number: {value}')






