



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		=  get_tests_dir("fixtures/test_sentencepiece.model")
_snake_case		=  get_tests_dir("fixtures/test_sentencepiece_bpe.model")

_snake_case		=  "pt" if is_torch_available() else "tf"



@require_sentencepiece
@require_tokenizers
class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		CamembertTokenizer
							lowerCamelCase__   			=		CamembertTokenizerFast
							lowerCamelCase__   			=		True
							lowerCamelCase__   			=		True







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														# We have a SentencePiece fixture for testing
														_lowerCAmelCase     :							Optional[int]    =    CamembertTokenizer(__a)
														tokenizer.save_pretrained(self.tmpdirname)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    "<pad>"
														_lowerCAmelCase     :							Optional[int]    =    1

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, 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    snake_case__      (   self):

														'''simple docstring'''

														self.assertEqual(self.get_tokenizer().vocab_size,			1005)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    CamembertTokenizer(__a)
														tokenizer.save_pretrained(self.tmpdirname)
														_lowerCAmelCase     :							Tuple    =    CamembertTokenizerFast.from_pretrained(self.tmpdirname)

														_lowerCAmelCase     :							Optional[int]    =    "I was born in 92000, and this is falsé."

														_lowerCAmelCase     :							Optional[int]    =    tokenizer.encode(__a)
														_lowerCAmelCase     :							List[Any]    =    rust_tokenizer.encode(__a)
														self.assertListEqual(__a,			__a)

														_lowerCAmelCase     :							str    =    tokenizer.encode(__a,			add_special_tokens=__a)
														_lowerCAmelCase     :							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)
														_lowerCAmelCase     :							List[str]    =    tokenizer.convert_ids_to_tokens(__a)
														_lowerCAmelCase     :							Dict    =    rust_tokenizer.tokenize(__a)
														self.assertListEqual(__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														if not self.test_rust_tokenizer:
																					return

														_lowerCAmelCase     :							int    =    self.get_tokenizer()
														_lowerCAmelCase     :							Dict    =    self.get_rust_tokenizer()

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

														_lowerCAmelCase     :							Tuple    =    tokenizer.tokenize(__a)
														_lowerCAmelCase     :							Optional[int]    =    rust_tokenizer.tokenize(__a)
														self.assertListEqual(__a,			__a)

														_lowerCAmelCase     :							Any    =    tokenizer.encode(__a,			add_special_tokens=__a)
														_lowerCAmelCase     :							Optional[Any]    =    rust_tokenizer.encode(__a,			add_special_tokens=__a)
														self.assertListEqual(__a,			__a)

														_lowerCAmelCase     :							int    =    self.get_rust_tokenizer()
														_lowerCAmelCase     :							List[str]    =    tokenizer.encode(__a)
														_lowerCAmelCase     :							Optional[int]    =    rust_tokenizer.encode(__a)
														self.assertListEqual(__a,			__a)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    {"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.
														_lowerCAmelCase     :							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=__a,			model_name="camembert-base",			revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf",			sequences=__a,			)





def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if density <= 0:
														raise ValueError("Impossible fluid density"  )
							if bulk_modulus <= 0:
														raise ValueError("Impossible bulk modulus"  )

							return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()
