



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


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

__snake_case       :	Union[str, Any]            ="""pt""" if is_torch_available() else """tf"""



@require_sentencepiece
@require_tokenizers
class  lowerCamelCase__    (							lowerCamelCase__   ,       unittest.TestCase):




			'''simple docstring'''



			snake_case_			=CamembertTokenizer
			snake_case_			=CamembertTokenizerFast
			snake_case_			=True
			snake_case_			=True



			def 	lowerCAmelCase__   (self				)  ->    Any:

							"""simple docstring"""






							super().setUp()

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



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

							"""simple docstring"""






							lowerCAmelCase__  :       Tuple				=  '''<pad>'''
							lowerCAmelCase__  :       Optional[int]				=  1

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



			def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

							"""simple docstring"""






							lowerCAmelCase__  :       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(__lowerCamelCase				)	,10_04				)



			def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

							"""simple docstring"""






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



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

							"""simple docstring"""






							lowerCAmelCase__  :       Optional[Any]				=  CamembertTokenizer(__lowerCamelCase				)
							tokenizer.save_pretrained(self.tmpdirname				)
							lowerCAmelCase__  :       str				=  CamembertTokenizerFast.from_pretrained(self.tmpdirname				)

							lowerCAmelCase__  :       Dict				=  '''I was born in 92000, and this is falsé.'''

							lowerCAmelCase__  :       Tuple				=  tokenizer.encode(__lowerCamelCase				)
							lowerCAmelCase__  :       Optional[Any]				=  rust_tokenizer.encode(__lowerCamelCase				)
							self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)

							lowerCAmelCase__  :       Dict				=  tokenizer.encode(__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
							lowerCAmelCase__  :       List[Any]				=  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)
							lowerCAmelCase__  :       Union[str, Any]				=  tokenizer.convert_ids_to_tokens(__lowerCamelCase				)
							lowerCAmelCase__  :       List[Any]				=  rust_tokenizer.tokenize(__lowerCamelCase				)
							self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)



			def 	lowerCAmelCase__   (self				)  ->    List[Any]:

							"""simple docstring"""






							if not self.test_rust_tokenizer:
											return

							lowerCAmelCase__  :       Optional[int]				=  self.get_tokenizer()
							lowerCAmelCase__  :       List[Any]				=  self.get_rust_tokenizer()

							lowerCAmelCase__  :       Dict				=  '''I was born in 92000, and this is falsé.'''

							lowerCAmelCase__  :       Any				=  tokenizer.tokenize(__lowerCamelCase				)
							lowerCAmelCase__  :       Optional[int]				=  rust_tokenizer.tokenize(__lowerCamelCase				)
							self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)

							lowerCAmelCase__  :       Union[str, Any]				=  tokenizer.encode(__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
							lowerCAmelCase__  :       Tuple				=  rust_tokenizer.encode(__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
							self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)

							lowerCAmelCase__  :       List[Any]				=  self.get_rust_tokenizer()
							lowerCAmelCase__  :       int				=  tokenizer.encode(__lowerCamelCase				)
							lowerCAmelCase__  :       Any				=  rust_tokenizer.encode(__lowerCamelCase				)
							self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)



			@slow
			def 	lowerCAmelCase__   (self				)  ->    List[str]:

							"""simple docstring"""






							lowerCAmelCase__  :       Tuple				=  {'''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.
							lowerCAmelCase__  :       Union[str, Any]				=  [
							    '''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 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				if length <= 0 or not isinstance(lowerCamelCase_ ,lowerCamelCase_):
								raise ValueError('''Length must be a positive integer.''')
				return [n * (2 * n - 1) for n in range(lowerCamelCase_)]


if __name__ == "__main__":
							print(hexagonal_numbers(length=5))
							print(hexagonal_numbers(length=1_0))

