"""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				:		Optional[int]	   =       get_tests_dir('fixtures/test_sentencepiece.model')
_a				:		int	   =       get_tests_dir('fixtures/test_sentencepiece_bpe.model')

_a				:		Union[str, Any]	   =       'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
					_UpperCamelCase							:      str		 =		CamembertTokenizer
					_UpperCamelCase							:      int		 =		CamembertTokenizerFast
					_UpperCamelCase							:      Optional[Any]		 =		True
					_UpperCamelCase							:      Dict		 =		True






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

							# We have a SentencePiece fixture for testing
							_lowerCAmelCase						:	Tuple        =					CamembertTokenizer(a__  )
							tokenizer.save_pretrained(self.tmpdirname  )






					def    __A  (		self  ):
							_lowerCAmelCase						:	Dict        =					"""<pad>"""
							_lowerCAmelCase						:	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    __A  (		self  ):
							_lowerCAmelCase						:	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(a__  )    ,					1004  )






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






					def    __A  (		self  ):
							_lowerCAmelCase						:	List[str]        =					CamembertTokenizer(a__  )
							tokenizer.save_pretrained(self.tmpdirname  )
							_lowerCAmelCase						:	Dict        =					CamembertTokenizerFast.from_pretrained(self.tmpdirname  )

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

							_lowerCAmelCase						:	Union[str, Any]        =					tokenizer.encode(a__  )
							_lowerCAmelCase						:	str        =					rust_tokenizer.encode(a__  )
							self.assertListEqual(a__    ,					a__  )

							_lowerCAmelCase						:	List[Any]        =					tokenizer.encode(a__    ,					add_special_tokens=a__  )
							_lowerCAmelCase						:	Optional[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[Any]        =					tokenizer.convert_ids_to_tokens(a__  )
							_lowerCAmelCase						:	str        =					rust_tokenizer.tokenize(a__  )
							self.assertListEqual(a__    ,					a__  )






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

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

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

							_lowerCAmelCase						:	Any        =					tokenizer.tokenize(a__  )
							_lowerCAmelCase						:	List[str]        =					rust_tokenizer.tokenize(a__  )
							self.assertListEqual(a__    ,					a__  )

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

							_lowerCAmelCase						:	str        =					self.get_rust_tokenizer()
							_lowerCAmelCase						:	Any        =					tokenizer.encode(a__  )
							_lowerCAmelCase						:	Union[str, Any]        =					rust_tokenizer.encode(a__  )
							self.assertListEqual(a__    ,					a__  )






					@slow
					def    __A  (		self  ):
							# fmt: off
							_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, 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.
							_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=a__    ,					model_name="""camembert-base"""    ,					revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""    ,					sequences=a__    ,					)





"""simple docstring"""




from __future__ import annotations





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	list     )	->				float:
  if not nums:
    raise ValueError("""List is empty"""     )
  return sum(_lowerCamelCase     ) / len(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




