





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


lowercase_          =		get_tests_dir("""fixtures/test_sentencepiece.model""")
lowercase_          =		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

lowercase_          =		'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class     SCREAMING_SNAKE_CASE       (__SCREAMING_SNAKE_CASE      ,     unittest.TestCase				):
   _UpperCamelCase   :		Union[str, Any]			 =	CamembertTokenizer
   _UpperCamelCase   :		List[str]			 =	CamembertTokenizerFast
   _UpperCamelCase   :		Any			 =	True
   _UpperCamelCase   :		Union[str, Any]			 =	True


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       List[str]:





     """simple docstring"""







     super().setUp()

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Tuple:





     """simple docstring"""







     lowercase__           =  "<pad>"
     lowercase__           =  1

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Tuple:





     """simple docstring"""







     lowercase__           =  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(_SCREAMING_SNAKE_CASE	)      ,					1_004	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Dict:





     """simple docstring"""







     self.assertEqual(self.get_tokenizer().vocab_size      ,					1_005	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Dict:





     """simple docstring"""







     lowercase__           =  CamembertTokenizer(_SCREAMING_SNAKE_CASE	)
     tokenizer.save_pretrained(self.tmpdirname	)
     lowercase__           =  CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

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

     lowercase__           =  tokenizer.encode(_SCREAMING_SNAKE_CASE	)
     lowercase__           =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE	)
     self.assertListEqual(_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE	)

     lowercase__           =  tokenizer.encode(_SCREAMING_SNAKE_CASE      ,					add_special_tokens=_SCREAMING_SNAKE_CASE	)
     lowercase__           =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE      ,					add_special_tokens=_SCREAMING_SNAKE_CASE	)
     self.assertListEqual(_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE	)

     # <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)
     lowercase__           =  tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE	)
     lowercase__           =  rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE	)
     self.assertListEqual(_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       int:





     """simple docstring"""







     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(_SCREAMING_SNAKE_CASE	)
     lowercase__           =  rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE	)
     self.assertListEqual(_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE	)

     lowercase__           =  tokenizer.encode(_SCREAMING_SNAKE_CASE      ,					add_special_tokens=_SCREAMING_SNAKE_CASE	)
     lowercase__           =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE      ,					add_special_tokens=_SCREAMING_SNAKE_CASE	)
     self.assertListEqual(_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE	)

     lowercase__           =  self.get_rust_tokenizer()
     lowercase__           =  tokenizer.encode(_SCREAMING_SNAKE_CASE	)
     lowercase__           =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE	)
     self.assertListEqual(_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE	)


   @slow
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       List[str]:





     """simple docstring"""







     lowercase__           =  {"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.
     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=_SCREAMING_SNAKE_CASE      ,					model_name='camembert-base'      ,					revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'      ,					sequences=_SCREAMING_SNAKE_CASE      ,					)









import sys

lowercase_          =		(
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = N		)    ->       int:
  lowercase__           =  -sys.maxsize - 1
  for i in range(len(_SCREAMING_SNAKE_CASE		) - 12		):
    lowercase__           =  1
    for j in range(13		):
      product *= int(n[i + j]		)
    if product > largest_product:
      lowercase__           =  product
  return largest_product


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


