



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__								=						get_tests_dir('''fixtures/test_sentencepiece.model''')
A__								=						get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

A__								=						'''pt''' if is_torch_available() else '''tf'''


@require_sentencepiece
@require_tokenizers
class       a							(			__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    Tuple											=					CamembertTokenizer
		__lowerCAmelCase	:    int											=					CamembertTokenizerFast
		__lowerCAmelCase	:    List[str]											=					True
		__lowerCAmelCase	:    Optional[Any]											=					True



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						super().setUp()

						# We have a SentencePiece fixture for testing
						snake_case__	:      Optional[Any]     		=	CamembertTokenizer(__lowercase  )
						tokenizer.save_pretrained(self.tmpdirname  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      str     		=	'''<pad>'''
						snake_case__	:      Optional[int]     		=	1

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



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      List[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(__lowercase  )   ,1_0_0_4  )



		def 	__lowerCamelCase			(     self     :List[str]  ):
						self.assertEqual(self.get_tokenizer().vocab_size   ,1_0_0_5  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      Union[str, Any]     		=	CamembertTokenizer(__lowercase  )
						tokenizer.save_pretrained(self.tmpdirname  )
						snake_case__	:      List[str]     		=	CamembertTokenizerFast.from_pretrained(self.tmpdirname  )

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

						snake_case__	:      Union[str, Any]     		=	tokenizer.encode(__lowercase  )
						snake_case__	:      List[Any]     		=	rust_tokenizer.encode(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      Union[str, Any]     		=	tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						snake_case__	:      List[Any]     		=	rust_tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						# <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__	:      List[str]     		=	tokenizer.convert_ids_to_tokens(__lowercase  )
						snake_case__	:      Optional[Any]     		=	rust_tokenizer.tokenize(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						if not self.test_rust_tokenizer:
										return

						snake_case__	:      List[str]     		=	self.get_tokenizer()
						snake_case__	:      int     		=	self.get_rust_tokenizer()

						snake_case__	:      List[Any]     		=	'''I was born in 92000, and this is falsé.'''

						snake_case__	:      Any     		=	tokenizer.tokenize(__lowercase  )
						snake_case__	:      str     		=	rust_tokenizer.tokenize(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      Optional[int]     		=	tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						snake_case__	:      List[Any]     		=	rust_tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      Optional[Any]     		=	self.get_rust_tokenizer()
						snake_case__	:      Tuple     		=	tokenizer.encode(__lowercase  )
						snake_case__	:      Union[str, Any]     		=	rust_tokenizer.encode(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )






		@slow
		def 	__lowerCamelCase			(     self     :List[str]  ):
						# fmt: off
						snake_case__	:      str     		=	{'''input_ids''': [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 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_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 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__	:      List[str]     		=	[
						    '''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=__lowercase   ,model_name='''camembert-base'''   ,revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''   ,sequences=__lowercase   ,)






import random

from .binary_exp_mod import bin_exp_mod



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase=1000      )   ->					str:


				"""simple docstring"""

				if n < 2:
								return False

				if n % 2 == 0:
								return n == 2

				# this means n is odd
				snake_case__	:      Tuple     		=	n - 1
				snake_case__	:      Tuple     		=	0
				while d % 2 == 0:
								d /= 2
								exp += 1

				# n - 1=d*(2**exp)
				snake_case__	:      List[str]     		=	0
				while count < prec:
								snake_case__	:      List[str]     		=	random.randint(2  ,  n - 1      )
								snake_case__	:      Optional[Any]     		=	bin_exp_mod(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
								if b != 1:
												snake_case__	:      List[Any]     		=	True
												for _ in range(__lowerCAmelCase      ):
																if b == n - 1:
																				snake_case__	:      List[str]     		=	False
																				break
																snake_case__	:      str     		=	b * b
																b %= n
												if flag:
																return False
												count += 1
				return True


if __name__ == "__main__":
				A__								=						abs(int(input('''Enter bound : ''').strip()))
				print('''Here\'s the list of primes:''')
				print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))

