





"""simple docstring"""




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						: List[str]     	=			get_tests_dir('''fixtures/test_sentencepiece.model''')
a						: Dict     	=			get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

a						: List[Any]     	=			'''pt''' if is_torch_available() else '''tf'''


@require_sentencepiece
@require_tokenizers
class        __UpperCamelCase				(						a__		,       unittest.TestCase						):
							lowerCamelCase   :					Tuple      					=CamembertTokenizer
							lowerCamelCase   :					Tuple      					=CamembertTokenizerFast
							lowerCamelCase   :					Any      					=True
							lowerCamelCase   :					Tuple      					=True






							def     __a   (   self      )	-> Dict:
								super().setUp()

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






							def     __a   (   self      )	-> Dict:
								a      :		Dict	=				"<pad>"
								a      :		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     __a   (   self      )	-> Tuple:
								a      :		List[str]	=				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__      )      ,					1004      )






							def     __a   (   self      )	-> str:
								self.assertEqual(self.get_tokenizer().vocab_size      ,					1005      )






							def     __a   (   self      )	-> Any:
								a      :		int	=				CamembertTokenizer(lowerCAmelCase__      )
								tokenizer.save_pretrained(self.tmpdirname      )
								a      :		Dict	=				CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

								a      :		Tuple	=				"I was born in 92000, and this is falsé."

								a      :		Tuple	=				tokenizer.encode(lowerCAmelCase__      )
								a      :		Any	=				rust_tokenizer.encode(lowerCAmelCase__      )
								self.assertListEqual(lowerCAmelCase__      ,					lowerCAmelCase__      )

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






							def     __a   (   self      )	-> Optional[Any]:
								if not self.test_rust_tokenizer:
									return

								a      :		Dict	=				self.get_tokenizer()
								a      :		str	=				self.get_rust_tokenizer()

								a      :		Dict	=				"I was born in 92000, and this is falsé."

								a      :		List[str]	=				tokenizer.tokenize(lowerCAmelCase__      )
								a      :		Optional[Any]	=				rust_tokenizer.tokenize(lowerCAmelCase__      )
								self.assertListEqual(lowerCAmelCase__      ,					lowerCAmelCase__      )

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

								a      :		Tuple	=				self.get_rust_tokenizer()
								a      :		Any	=				tokenizer.encode(lowerCAmelCase__      )
								a      :		str	=				rust_tokenizer.encode(lowerCAmelCase__      )
								self.assertListEqual(lowerCAmelCase__      ,					lowerCAmelCase__      )






							@slow
							def     __a   (   self      )	-> int:
								# 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, 2_7575, 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, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 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      :		Optional[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__      ,					)









"""simple docstring"""




a						: Any     	=			{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


a						: List[Any]     	=			{value: key for key, value in encode_dict.items()}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->str:






	'''simple docstring'''






	a      :		int	=				""
	for letter in word.lower():
		if letter.isalpha() or letter == " ":
			encoded += encode_dict[letter]
		else:
			raise Exception("encode() accepts only letters of the alphabet and spaces"					)
	return encoded






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->str:






	'''simple docstring'''






	if set(_lowercase					) - {"A", "B", " "} != set():
		raise Exception("decode() accepts only 'A', 'B' and spaces"					)
	a      :		Optional[Any]	=				""
	for word in coded.split():
		while len(_lowercase					) != 0:
			decoded += decode_dict[word[:5]]
			a      :		List[Any]	=				word[5:]
		decoded += " "
	return decoded.strip()


if __name__ == "__main__":
		from doctest import testmod

		testmod()


