





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowercase__						:      Optional[int]	=    logging.get_logger(__name__)

lowercase__						:      List[str]	=    {'vocab_file': 'spiece.model'}

lowercase__						:      Optional[Any]	=    {
    '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',
    }
}

lowercase__						:      Dict	=    {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
lowercase__						:      Tuple	=    0
lowercase__						:      Any	=    1
lowercase__						:      Optional[int]	=    2
lowercase__						:      int	=    3
lowercase__						:      List[Any]	=    4
class     __lowerCAmelCase					(   __lowercase			):






       """simple docstring"""
       _snake_case     :					List[str]						    =   VOCAB_FILES_NAMES
       _snake_case     :					int						    =   PRETRAINED_VOCAB_FILES_MAP
       _snake_case     :					Optional[int]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _snake_case     :					int						    =   '''left'''





       def __init__(							self       :		int	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		int=False	,			lowerCAmelCase__       :		int=True	,			lowerCAmelCase__       :		List[str]=False	,			lowerCAmelCase__       :		Any="<s>"	,			lowerCAmelCase__       :		List[str]="</s>"	,			lowerCAmelCase__       :		int="<unk>"	,			lowerCAmelCase__       :		Dict="<sep>"	,			lowerCAmelCase__       :		str="<pad>"	,			lowerCAmelCase__       :		Tuple="<cls>"	,			lowerCAmelCase__       :		Optional[Any]="<mask>"	,			lowerCAmelCase__       :		List[str]=["<eop>", "<eod>"]	,			lowerCAmelCase__       :		Optional[Dict[str, Any]] = None	,			**lowerCAmelCase__       :		Dict	,			)   ->					None:

            '''simple docstring'''




            _UpperCamelCase       =	AddedToken(lowerCAmelCase__	,			lstrip=lowerCAmelCase__	,			rstrip=lowerCAmelCase__					) if isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) else mask_token

            _UpperCamelCase       =	{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                do_lower_case=lowerCAmelCase__	,			remove_space=lowerCAmelCase__	,			keep_accents=lowerCAmelCase__	,			bos_token=lowerCAmelCase__	,			eos_token=lowerCAmelCase__	,			unk_token=lowerCAmelCase__	,			sep_token=lowerCAmelCase__	,			pad_token=lowerCAmelCase__	,			cls_token=lowerCAmelCase__	,			mask_token=lowerCAmelCase__	,			additional_special_tokens=lowerCAmelCase__	,			sp_model_kwargs=self.sp_model_kwargs	,			**lowerCAmelCase__	,			)

            _UpperCamelCase       =	3

            _UpperCamelCase       =	do_lower_case
            _UpperCamelCase       =	remove_space
            _UpperCamelCase       =	keep_accents
            _UpperCamelCase       =	vocab_file

            _UpperCamelCase       =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(lowerCAmelCase__					)





       @property
       def snake_case__						(							self       :		Tuple					)   ->					str:

            '''simple docstring'''




            return len(self.sp_model					)





       def snake_case__						(							self       :		List[Any]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	{self.convert_ids_to_tokens(lowerCAmelCase__					): i for i in range(self.vocab_size					)}
            vocab.update(self.added_tokens_encoder					)
            return vocab





       def __getstate__(							self       :		Tuple					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.__dict__.copy()
            _UpperCamelCase       =	None
            return state





       def __setstate__(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		Optional[int]					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	d

            # for backward compatibility
            if not hasattr(self	,			'''sp_model_kwargs'''					):
                 _UpperCamelCase       =	{}

            _UpperCamelCase       =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(self.vocab_file					)





       def snake_case__						(							self       :		Dict	,			lowerCAmelCase__       :		str					)   ->					List[str]:

            '''simple docstring'''




            if self.remove_space:
                 _UpperCamelCase       =	""" """.join(inputs.strip().split()					)
            else:
                 _UpperCamelCase       =	inputs
            _UpperCamelCase       =	outputs.replace('''``'''	,			'''\"'''					).replace('''\'\''''	,			'''\"'''					)

            if not self.keep_accents:
                 _UpperCamelCase       =	unicodedata.normalize('''NFKD'''	,			lowerCAmelCase__					)
                 _UpperCamelCase       =	"""""".join([c for c in outputs if not unicodedata.combining(lowerCAmelCase__					)]					)
            if self.do_lower_case:
                 _UpperCamelCase       =	outputs.lower()

            return outputs





       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		str					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.preprocess_text(lowerCAmelCase__					)
            _UpperCamelCase       =	self.sp_model.encode(lowerCAmelCase__	,			out_type=lowerCAmelCase__					)
            _UpperCamelCase       =	[]
            for piece in pieces:
                 if len(lowerCAmelCase__					) > 1 and piece[-1] == str(''','''					) and piece[-2].isdigit():
                      _UpperCamelCase       =	self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase__	,			''''''					)					)
                      if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                           if len(cur_pieces[0]					) == 1:
                                _UpperCamelCase       =	cur_pieces[1:]
                           else:
                                _UpperCamelCase       =	cur_pieces[0][1:]
                      cur_pieces.append(piece[-1]					)
                      new_pieces.extend(lowerCAmelCase__					)
                 else:
                      new_pieces.append(lowerCAmelCase__					)

            return new_pieces





       def snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		str					)   ->					Any:

            '''simple docstring'''




            return self.sp_model.PieceToId(lowerCAmelCase__					)





       def snake_case__						(							self       :		Any	,			lowerCAmelCase__       :		Optional[int]					)   ->					int:

            '''simple docstring'''




            return self.sp_model.IdToPiece(lowerCAmelCase__					)





       def snake_case__						(							self       :		str	,			lowerCAmelCase__       :		Optional[Any]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	"""""".join(lowerCAmelCase__					).replace(lowerCAmelCase__	,			''' '''					).strip()
            return out_string





       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		List[int]	,			lowerCAmelCase__       :		bool = False	,			lowerCAmelCase__       :		bool = None	,			lowerCAmelCase__       :		bool = True	,			**lowerCAmelCase__       :		Optional[int]	,			)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	kwargs.pop('''use_source_tokenizer'''	,			lowerCAmelCase__					)

            _UpperCamelCase       =	self.convert_ids_to_tokens(lowerCAmelCase__	,			skip_special_tokens=lowerCAmelCase__					)

            # To avoid mixing byte-level and unicode for byte-level BPT
            # we need to build string separately for added tokens and byte-level tokens
            # cf. https://github.com/huggingface/transformers/issues/1133
            _UpperCamelCase       =	[]
            _UpperCamelCase       =	[]
            for token in filtered_tokens:
                 if skip_special_tokens and token in self.all_special_ids:
                      continue
                 if token in self.added_tokens_encoder:
                      if current_sub_text:
                           sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__					)					)
                           _UpperCamelCase       =	[]
                      sub_texts.append(lowerCAmelCase__					)
                 else:
                      current_sub_text.append(lowerCAmelCase__					)
            if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__					)					)

            # Mimic the behavior of the Rust tokenizer:
            # By default, there are no spaces between special tokens
            _UpperCamelCase       =	"""""".join(lowerCAmelCase__					)

            _UpperCamelCase       =	(
                clean_up_tokenization_spaces
                if clean_up_tokenization_spaces is not None
                else self.clean_up_tokenization_spaces
            )
            if clean_up_tokenization_spaces:
                 _UpperCamelCase       =	self.clean_up_tokenization(lowerCAmelCase__					)
                 return clean_text
            else:
                 return text





       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		List[int]	,			lowerCAmelCase__       :		Optional[List[int]] = None					)   ->					List[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[self.sep_token_id]
            _UpperCamelCase       =	[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 snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		List[int]	,			lowerCAmelCase__       :		Optional[List[int]] = None	,			lowerCAmelCase__       :		bool = False					)   ->					List[int]:

            '''simple docstring'''




            if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=lowerCAmelCase__	,			token_ids_a=lowerCAmelCase__	,			already_has_special_tokens=lowerCAmelCase__					)

            if token_ids_a is not None:
                 return ([0] * len(lowerCAmelCase__					)) + [1] + ([0] * len(lowerCAmelCase__					)) + [1, 1]
            return ([0] * len(lowerCAmelCase__					)) + [1, 1]





       def snake_case__						(							self       :		int	,			lowerCAmelCase__       :		List[int]	,			lowerCAmelCase__       :		Optional[List[int]] = None					)   ->					List[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[self.sep_token_id]
            _UpperCamelCase       =	[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 snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Optional[str] = None					)   ->					Tuple[str]:

            '''simple docstring'''




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

            if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase__					) and os.path.isfile(self.vocab_file					):
                 copyfile(self.vocab_file	,			lowerCAmelCase__					)
            elif not os.path.isfile(self.vocab_file					):
                 with open(lowerCAmelCase__	,			'''wb'''					) as fi:
                      _UpperCamelCase       =	self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__					)

            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


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

lowercase__						:      Tuple	=    'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   CamembertTokenizer
       _snake_case     :					str						    =   CamembertTokenizerFast
       _snake_case     :					int						    =   True
       _snake_case     :					List[str]						    =   True





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            super().setUp()

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





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	'''<pad>'''
            _UpperCamelCase       =	1

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





       def snake_case__						(							self       :		Dict					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	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 snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	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)
            _UpperCamelCase       =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	self.get_rust_tokenizer()

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.tokenize(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	self.get_rust_tokenizer()
            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)







       @slow
       def snake_case__						(							self       :		Any					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	{'''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.
            _UpperCamelCase       =	[
                '''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__	,			)



