



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


a__   :					Optional[Any] 						=      get_tests_dir("fixtures/test_sentencepiece.model")
a__   :					Dict 						=      get_tests_dir("fixtures/test_sentencepiece_bpe.model")

a__   :					Optional[Any] 						=      "pt" if is_torch_available() else "tf"



@require_sentencepiece
@require_tokenizers
class    UpperCAmelCase__(				__lowercase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Optional[int]												=     CamembertTokenizer
   A     :	int												=     CamembertTokenizerFast
   A     :	Any												=     True
   A     :	Optional[Any]												=     True


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




         """simple docstring"""

         super().setUp()

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


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




         """simple docstring"""

         lowercase__  =	'<pad>'
         lowercase__  =	1

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


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




         """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(lowerCAmelCase)      ,					10_04)


   def       UpperCAmelCase		(    self	:      Dict)						->					List[Any]:




         """simple docstring"""

         self.assertEqual(self.get_tokenizer().vocab_size      ,					10_05)


   def       UpperCAmelCase		(    self	:      List[str])						->					Dict:




         """simple docstring"""

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

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

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

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

         # <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(lowerCAmelCase)
         lowercase__  =	rust_tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)


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




         """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(lowerCAmelCase)
         lowercase__  =	rust_tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)

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

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



   @slow
   def       UpperCAmelCase		(    self	:      Dict)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 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=lowerCAmelCase      ,					model_name='camembert-base'      ,					revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'      ,					sequences=lowerCAmelCase      ,					)


def 				__A(lowerCAmelCase			)		->					bool:

		"""simple docstring"""






		if not isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
				raise ValueError("""check_bouncy() accepts only integer arguments"""			)
		_UpperCamelCase         =						str(lowerCAmelCase			)
		_UpperCamelCase         =						"""""".join(sorted(lowerCAmelCase			)			)
		return sorted_str_n != str_n and sorted_str_n[::-1] != str_n


def 				__A(lowerCAmelCase = 9_9			)		->					int:

		"""simple docstring"""






		if not 0 < percent < 1_0_0:
				raise ValueError("""solution() only accepts values from 0 to 100"""			)
		_UpperCamelCase         =						0
		_UpperCamelCase         =						1

		while True:
				if check_bouncy(lowerCAmelCase			):
						bouncy_num += 1
				if (bouncy_num / num) * 1_0_0 >= percent:
						return num
				num += 1


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print(F"""{solution(99)}""")



