


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

UpperCAmelCase      =   "pt" if is_torch_available() else "tf"







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowercase_ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  CamembertTokenizer
 __snake_case												=  CamembertTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



 def 						UpperCamelCase__					(     self ):
       super().setUp()

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



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''<pad>'''
       snake_case_	=				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 ):
       snake_case_	=				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__ )		,				10_04 )



 def 						UpperCamelCase__					(     self ):
       self.assertEqual(self.get_tokenizer().vocab_size		,				10_05 )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				CamembertTokenizer(a__ )
       tokenizer.save_pretrained(self.tmpdirname )
       snake_case_	=				CamembertTokenizerFast.from_pretrained(self.tmpdirname )

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

       snake_case_	=				tokenizer.encode(a__ )
       snake_case_	=				rust_tokenizer.encode(a__ )
       self.assertListEqual(a__		,				a__ )

       snake_case_	=				tokenizer.encode(a__		,				add_special_tokens=a__ )
       snake_case_	=				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_	=				tokenizer.convert_ids_to_tokens(a__ )
       snake_case_	=				rust_tokenizer.tokenize(a__ )
       self.assertListEqual(a__		,				a__ )



 def 						UpperCamelCase__					(     self ):




       if not self.test_rust_tokenizer:
             return

       snake_case_	=				self.get_tokenizer()
       snake_case_	=				self.get_rust_tokenizer()

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

       snake_case_	=				tokenizer.tokenize(a__ )
       snake_case_	=				rust_tokenizer.tokenize(a__ )
       self.assertListEqual(a__		,				a__ )

       snake_case_	=				tokenizer.encode(a__		,				add_special_tokens=a__ )
       snake_case_	=				rust_tokenizer.encode(a__		,				add_special_tokens=a__ )
       self.assertListEqual(a__		,				a__ )

       snake_case_	=				self.get_rust_tokenizer()
       snake_case_	=				tokenizer.encode(a__ )
       snake_case_	=				rust_tokenizer.encode(a__ )
       self.assertListEqual(a__		,				a__ )





 @slow
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				{'''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.
       snake_case_	=				[
           '''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__		,				)



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      return x if y == 0 else greatest_common_divisor(SCREAMING_SNAKE_CASE , x % y )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      return (x * y) // greatest_common_divisor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE = 20 )->  int:




      """simple docstring"""

      snake_case_	=				1
      for i in range(1 , n + 1 ):
            snake_case_	=				lcm(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      return g


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