





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

UpperCAmelCase						:   Dict			  =     '''pt''' if is_torch_available() else '''tf'''





@require_sentencepiece
@require_tokenizers
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  List[str]					    =	CamembertTokenizer
				UpperCamelCase    :  Any					    =	CamembertTokenizerFast
				UpperCamelCase    :  Dict					    =	True
				UpperCamelCase    :  Optional[Any]					    =	True



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

									# We have a SentencePiece fixture for testing
									__A	:						Any       =				CamembertTokenizer(_A     )
									tokenizer.save_pretrained(self.tmpdirname     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				'<pad>'
									__A	:						Optional[Any]       =				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     ):
									__A	:						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(_A     )     ,					1004     )



				def 					UpperCAmelCase_		(			self     ):
									self.assertEqual(self.get_tokenizer().vocab_size     ,					1005     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				CamembertTokenizer(_A     )
									tokenizer.save_pretrained(self.tmpdirname     )
									__A	:						Tuple       =				CamembertTokenizerFast.from_pretrained(self.tmpdirname     )

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

									__A	:						Optional[int]       =				tokenizer.encode(_A     )
									__A	:						Tuple       =				rust_tokenizer.encode(_A     )
									self.assertListEqual(_A     ,					_A     )

									__A	:						Optional[Any]       =				tokenizer.encode(_A     ,					add_special_tokens=_A     )
									__A	:						List[Any]       =				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)
									__A	:						Optional[Any]       =				tokenizer.convert_ids_to_tokens(_A     )
									__A	:						int       =				rust_tokenizer.tokenize(_A     )
									self.assertListEqual(_A     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									if not self.test_rust_tokenizer:
														return

									__A	:						List[Any]       =				self.get_tokenizer()
									__A	:						Union[str, Any]       =				self.get_rust_tokenizer()

									__A	:						Tuple       =				'I was born in 92000, and this is falsé.'

									__A	:						Optional[Any]       =				tokenizer.tokenize(_A     )
									__A	:						List[Any]       =				rust_tokenizer.tokenize(_A     )
									self.assertListEqual(_A     ,					_A     )

									__A	:						int       =				tokenizer.encode(_A     ,					add_special_tokens=_A     )
									__A	:						Dict       =				rust_tokenizer.encode(_A     ,					add_special_tokens=_A     )
									self.assertListEqual(_A     ,					_A     )

									__A	:						Tuple       =				self.get_rust_tokenizer()
									__A	:						Tuple       =				tokenizer.encode(_A     )
									__A	:						Optional[int]       =				rust_tokenizer.encode(_A     )
									self.assertListEqual(_A     ,					_A     )





				@slow
				def 					UpperCAmelCase_		(			self     ):
									# fmt: off
									__A	:						List[str]       =				{'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.
									__A	:						int       =				[
									    '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     ,					)











from __future__ import annotations







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      float:
					if days_between_payments <= 0:
										raise ValueError('days_between_payments must be > 0'  )
					if daily_interest_rate < 0:
										raise ValueError('daily_interest_rate must be >= 0'  )
					if principal <= 0:
										raise ValueError('principal must be > 0'  )
					return principal * daily_interest_rate * days_between_payments







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						)      ->      float:
					if number_of_compounding_periods <= 0:
										raise ValueError('number_of_compounding_periods must be > 0'  )
					if nominal_annual_interest_rate_percentage < 0:
										raise ValueError('nominal_annual_interest_rate_percentage must be >= 0'  )
					if principal <= 0:
										raise ValueError('principal must be > 0'  )

					return principal * (
					    (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
					    - 1
					)







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						)      ->      float:
					if number_of_years <= 0:
										raise ValueError('number_of_years must be > 0'  )
					if nominal_annual_percentage_rate < 0:
										raise ValueError('nominal_annual_percentage_rate must be >= 0'  )
					if principal <= 0:
										raise ValueError('principal must be > 0'  )

					return compound_interest(
					    a					,						nominal_annual_percentage_rate / 3_65					,						number_of_years * 3_65  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




