


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


lowerCAmelCase__  :Optional[int]   			=							get_tests_dir('''fixtures/test_sentencepiece.model''')
lowerCAmelCase__  :int   			=							get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

lowerCAmelCase__  :str   			=							'''pt''' if is_torch_available() else '''tf'''

@require_sentencepiece
@require_tokenizers
class 		__a      (				__lowerCAmelCase    ,      unittest.TestCase			):
		_a	:  int				=	CamembertTokenizer
		_a	:  List[str]				=	CamembertTokenizerFast
		_a	:  Union[str, Any]				=	True
		_a	:  Tuple				=	True
		def 	UpperCAmelCase__				(			self						) ->       Union[str, Any]:




									"""simple docstring"""





									super().setUp()

									# We have a SentencePiece fixture for testing
									_UpperCAmelCase 		=			CamembertTokenizer(_SCREAMING_SNAKE_CASE						)
									tokenizer.save_pretrained(self.tmpdirname						)
		def 	UpperCAmelCase__				(			self						) ->       List[str]:




									"""simple docstring"""





									_UpperCAmelCase 		=			'<pad>'
									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       Any:




									"""simple docstring"""





									_UpperCAmelCase 		=			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						)			,    1004						)
		def 	UpperCAmelCase__				(			self						) ->       Any:




									"""simple docstring"""





									self.assertEqual(self.get_tokenizer().vocab_size			,    1005						)
		def 	UpperCAmelCase__				(			self						) ->       Optional[Any]:




									"""simple docstring"""





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

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

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

									_UpperCAmelCase 		=			tokenizer.encode(_SCREAMING_SNAKE_CASE			,    add_special_tokens=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			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)
									_UpperCAmelCase 		=			tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE						)
									self.assertListEqual(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       Dict:




									"""simple docstring"""





									if not self.test_rust_tokenizer:
																return

									_UpperCAmelCase 		=			self.get_tokenizer()
									_UpperCAmelCase 		=			self.get_rust_tokenizer()

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

									_UpperCAmelCase 		=			tokenizer.tokenize(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE						)
									self.assertListEqual(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)

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

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






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




									"""simple docstring"""





									_UpperCAmelCase 		=			{'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.
									_UpperCAmelCase 		=			[
									    '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			,    )

"""simple docstring"""
import math







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5		,     int(math.sqrt(_SCREAMING_SNAKE_CASE   ) + 1   )		,     6   ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def 						__a   (	_SCREAMING_SNAKE_CASE = 0.1   )	->int:
  a__:     str										=	3
  a__:     Optional[Any]										=	3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1		,     (j + 2) * (j + 2)		,     j + 1   ):
      primes += is_prime(_SCREAMING_SNAKE_CASE   )
    j += 2
  return j


if __name__ == "__main__":
 import doctest

 doctest.testmod()


