


"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_xlnet import XLNetTokenizer
else:
					__magic_name__						: str    =				None


__magic_name__						: Union[str, Any]    =				logging.get_logger(__name__)

__magic_name__						: str    =				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__magic_name__						: Dict    =				{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

__magic_name__						: Union[str, Any]    =				{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

__magic_name__						: Optional[Any]    =				'▁'

# Segments (not really needed)
__magic_name__						: Tuple    =				0
__magic_name__						: List[Any]    =				1
__magic_name__						: Tuple    =				2
__magic_name__						: Any    =				3
__magic_name__						: str    =				4




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       VOCAB_FILES_NAMES
							__a					 =       PRETRAINED_VOCAB_FILES_MAP
							__a					 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a					 =       '''left'''
							__a					 =       XLNetTokenizer




							def __init__(     self:    List[Any]     ,							A_:    Tuple=None     ,							A_:    Optional[Any]=None     ,							A_:    Tuple=False     ,							A_:    Optional[int]=True     ,							A_:    int=False     ,							A_:    Tuple="<s>"     ,							A_:    Dict="</s>"     ,							A_:    Union[str, Any]="<unk>"     ,							A_:    str="<sep>"     ,							A_:    Union[str, Any]="<pad>"     ,							A_:    int="<cls>"     ,							A_:    Dict="<mask>"     ,							A_:    str=["<eop>", "<eod>"]     ,							**A_:    Tuple     ,							):
										# Mask token behave like a normal word, i.e. include the space before it
										__lowerCamelCase          =      AddedToken(A_     ,							lstrip=A_     ,							rstrip=A_   ) if isinstance(A_     ,							A_   ) else mask_token

										super().__init__(
										    vocab_file=A_     ,							tokenizer_file=A_     ,							do_lower_case=A_     ,							remove_space=A_     ,							keep_accents=A_     ,							bos_token=A_     ,							eos_token=A_     ,							unk_token=A_     ,							sep_token=A_     ,							pad_token=A_     ,							cls_token=A_     ,							mask_token=A_     ,							additional_special_tokens=A_     ,							**A_     ,							)

										__lowerCamelCase          =      3
										__lowerCamelCase          =      do_lower_case
										__lowerCamelCase          =      remove_space
										__lowerCamelCase          =      keep_accents
										__lowerCamelCase          =      vocab_file
										__lowerCamelCase          =      False if not self.vocab_file else True




							def   __a   (     self:    List[Any]     ,							A_:    List[int]     ,							A_:    Optional[List[int]] = None   ):
										__lowerCamelCase          =      [self.sep_token_id]
										__lowerCamelCase          =      [self.cls_token_id]
										if token_ids_a is None:
													return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls




							def   __a   (     self:    Tuple     ,							A_:    List[int]     ,							A_:    Optional[List[int]] = None   ):
										__lowerCamelCase          =      [self.sep_token_id]
										__lowerCamelCase          =      [2]

										if token_ids_a is None:
													return len(token_ids_a + sep   ) * [0] + cls_segment_id
										return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1] + cls_segment_id




							def   __a   (     self:    List[Any]     ,							A_:    str     ,							A_:    Optional[str] = None   ):
										if not self.can_save_slow_tokenizer:
													raise ValueError(
													    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
													    """tokenizer."""   )

										if not os.path.isdir(A_   ):
													logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
													return
										__lowerCamelCase          =      os.path.join(
										    A_     ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

										if os.path.abspath(self.vocab_file   ) != os.path.abspath(A_   ):
													copyfile(self.vocab_file     ,							A_   )

										return (out_vocab_file,)








"""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


__magic_name__						: Dict    =				get_tests_dir('fixtures/test_sentencepiece.model')
__magic_name__						: int    =				get_tests_dir('fixtures/test_sentencepiece_bpe.model')

__magic_name__						: Any    =				'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class 			__snake_case   (lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       CamembertTokenizer
							__a					 =       CamembertTokenizerFast
							__a					 =       True
							__a					 =       True




							def   __a   (     self:    List[Any]   ):
										super().setUp()

										# We have a SentencePiece fixture for testing
										__lowerCamelCase          =      CamembertTokenizer(A_   )
										tokenizer.save_pretrained(self.tmpdirname   )




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      """<pad>"""
										__lowerCamelCase          =      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:    Any   ):
										__lowerCamelCase          =      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_   )     ,							10_04   )




							def   __a   (     self:    Any   ):
										self.assertEqual(self.get_tokenizer().vocab_size     ,							10_05   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      CamembertTokenizer(A_   )
										tokenizer.save_pretrained(self.tmpdirname   )
										__lowerCamelCase          =      CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

										__lowerCamelCase          =      """I was born in 92000, and this is falsé."""

										__lowerCamelCase          =      tokenizer.encode(A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      tokenizer.encode(A_     ,							add_special_tokens=A_   )
										__lowerCamelCase          =      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          =      tokenizer.convert_ids_to_tokens(A_   )
										__lowerCamelCase          =      rust_tokenizer.tokenize(A_   )
										self.assertListEqual(A_     ,							A_   )




							def   __a   (     self:    List[str]   ):
										if not self.test_rust_tokenizer:
													return

										__lowerCamelCase          =      self.get_tokenizer()
										__lowerCamelCase          =      self.get_rust_tokenizer()

										__lowerCamelCase          =      """I was born in 92000, and this is falsé."""

										__lowerCamelCase          =      tokenizer.tokenize(A_   )
										__lowerCamelCase          =      rust_tokenizer.tokenize(A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      tokenizer.encode(A_     ,							add_special_tokens=A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_     ,							add_special_tokens=A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      self.get_rust_tokenizer()
										__lowerCamelCase          =      tokenizer.encode(A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_   )
										self.assertListEqual(A_     ,							A_   )




							@slow
							def   __a   (     self:    Optional[Any]   ):
										# fmt: off
										__lowerCamelCase          =      {"""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          =      [
										    """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_     ,							)




