'''simple docstring'''




import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A								=   get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       str     =    XLMRobertaTokenizer
   A							:       int     =    XLMRobertaTokenizerFast
   A							:       List[str]     =    True
   A							:       Optional[Any]     =    True
   def 		_lowerCamelCase (self				)    ->     int:
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase_					:       str     				=					XLMRobertaTokenizer(_a       ,				keep_accents=_a				)
        tokenizer.save_pretrained(self.tmpdirname				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Any     				=					'<pad>'
        lowercase_					:       str     				=					1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a				)       ,				_a				)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a				)       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       List[Any]     				=					list(self.get_tokenizer().get_vocab().keys()				)

        self.assertEqual(vocab_keys[0]       ,				'<s>'				)
        self.assertEqual(vocab_keys[1]       ,				'<pad>'				)
        self.assertEqual(vocab_keys[-1]       ,				'<mask>'				)
        self.assertEqual(len(_a				)       ,				1_002				)
   def 		_lowerCamelCase (self				)    ->     Any:
        self.assertEqual(self.get_tokenizer().vocab_size       ,				1_002				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Optional[Any]     				=					XLMRobertaTokenizer(_a       ,				keep_accents=_a				)

        lowercase_					:       Union[str, Any]     				=					tokenizer.tokenize('This is a test'				)
        self.assertListEqual(_a       ,				['▁This', '▁is', '▁a', '▁t', 'est']				)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(_a				)       ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,				)

        lowercase_					:       Optional[int]     				=					tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
        self.assertListEqual(
            _a       ,				[
                SPIECE_UNDERLINE + 'I',
                SPIECE_UNDERLINE + 'was',
                SPIECE_UNDERLINE + 'b',
                'or',
                'n',
                SPIECE_UNDERLINE + 'in',
                SPIECE_UNDERLINE + '',
                '9',
                '2',
                '0',
                '0',
                '0',
                ',',
                SPIECE_UNDERLINE + 'and',
                SPIECE_UNDERLINE + 'this',
                SPIECE_UNDERLINE + 'is',
                SPIECE_UNDERLINE + 'f',
                'al',
                's',
                'é',
                '.',
            ]       ,				)
        lowercase_					:       List[str]     				=					tokenizer.convert_tokens_to_ids(_a				)
        self.assertListEqual(
            _a       ,				[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
            ]       ,				)

        lowercase_					:       List[str]     				=					tokenizer.convert_ids_to_tokens(_a				)
        self.assertListEqual(
            _a       ,				[
                SPIECE_UNDERLINE + 'I',
                SPIECE_UNDERLINE + 'was',
                SPIECE_UNDERLINE + 'b',
                'or',
                'n',
                SPIECE_UNDERLINE + 'in',
                SPIECE_UNDERLINE + '',
                '<unk>',
                '2',
                '0',
                '0',
                '0',
                ',',
                SPIECE_UNDERLINE + 'and',
                SPIECE_UNDERLINE + 'this',
                SPIECE_UNDERLINE + 'is',
                SPIECE_UNDERLINE + 'f',
                'al',
                's',
                '<unk>',
                '.',
            ]       ,				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        if not self.test_slow_tokenizer:
             # as we don't have a slow version, we can't compare the outputs between slow and fast versions
             return

        lowercase_					:       Optional[Any]     				=					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
                  lowercase_					:       Union[str, Any]     				=					self.rust_tokenizer_class.from_pretrained(_a       ,				**_a				)
                  lowercase_					:       Union[str, Any]     				=					self.tokenizer_class.from_pretrained(_a       ,				**_a				)

                  lowercase_					:       Optional[int]     				=					tempfile.mkdtemp()

                  lowercase_					:       List[str]     				=					tokenizer_r.save_pretrained(_a				)
                  lowercase_					:       int     				=					tokenizer_p.save_pretrained(_a				)

                  # Checks it save with the same files + the tokenizer.json file for the fast one
                  self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)
                  lowercase_					:       List[str]     				=					tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f				)
                  self.assertSequenceEqual(_a       ,				_a				)

                  # Checks everything loads correctly in the same way
                  lowercase_					:       List[str]     				=					tokenizer_r.from_pretrained(_a				)
                  lowercase_					:       Optional[int]     				=					tokenizer_p.from_pretrained(_a				)

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                       self.assertTrue(hasattr(_a       ,				_a				)				)
                       # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                       # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                  shutil.rmtree(_a				)

                  # Save tokenizer rust, legacy_format=True
                  lowercase_					:       List[Any]     				=					tempfile.mkdtemp()

                  lowercase_					:       Optional[int]     				=					tokenizer_r.save_pretrained(_a       ,				legacy_format=_a				)
                  lowercase_					:       Union[str, Any]     				=					tokenizer_p.save_pretrained(_a				)

                  # Checks it save with the same files
                  self.assertSequenceEqual(_a       ,				_a				)

                  # Checks everything loads correctly in the same way
                  lowercase_					:       int     				=					tokenizer_r.from_pretrained(_a				)
                  lowercase_					:       Optional[Any]     				=					tokenizer_p.from_pretrained(_a				)

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                       self.assertTrue(hasattr(_a       ,				_a				)				)

                  shutil.rmtree(_a				)

                  # Save tokenizer rust, legacy_format=False
                  lowercase_					:       int     				=					tempfile.mkdtemp()

                  lowercase_					:       Union[str, Any]     				=					tokenizer_r.save_pretrained(_a       ,				legacy_format=_a				)
                  lowercase_					:       List[str]     				=					tokenizer_p.save_pretrained(_a				)

                  # Checks it saved the tokenizer.json file
                  self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)

                  # Checks everything loads correctly in the same way
                  lowercase_					:       List[str]     				=					tokenizer_r.from_pretrained(_a				)
                  lowercase_					:       Dict     				=					tokenizer_p.from_pretrained(_a				)

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                       self.assertTrue(hasattr(_a       ,				_a				)				)

                  shutil.rmtree(_a				)
   @cached_property
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        with tempfile.NamedTemporaryFile() as f:
             shutil.copyfile(_a       ,				f.name				)
             lowercase_					:       List[str]     				=					XLMRobertaTokenizer(f.name       ,				keep_accents=_a				)
             lowercase_					:       List[Any]     				=					pickle.dumps(_a				)
        pickle.loads(_a				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        if not self.test_rust_tokenizer:
             return

        lowercase_					:       Any     				=					self.get_tokenizer()
        lowercase_					:       Union[str, Any]     				=					self.get_rust_tokenizer()

        lowercase_					:       Tuple     				=					'I was born in 92000, and this is falsé.'

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

        lowercase_					:       List[str]     				=					tokenizer.encode(_a       ,				add_special_tokens=_a				)
        lowercase_					:       Optional[Any]     				=					rust_tokenizer.encode(_a       ,				add_special_tokens=_a				)
        self.assertListEqual(_a       ,				_a				)

        lowercase_					:       Tuple     				=					self.get_rust_tokenizer()
        lowercase_					:       Union[str, Any]     				=					tokenizer.encode(_a				)
        lowercase_					:       Tuple     				=					rust_tokenizer.encode(_a				)
        self.assertListEqual(_a       ,				_a				)
   @slow
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Optional[Any]     				=					'Hello World!'
        lowercase_					:       List[str]     				=					[0, 35_378, 6_661, 38, 2]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(_a       ,				self.big_tokenizer.encode(_a				)				)
   @slow
   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[int]     				=					(
            'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
            ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
        )
        lowercase_					:       Any     				=					[
            0,
            3_293,
            83,
            10,
            4_552,
            4_989,
            7_986,
            678,
            10,
            5_915,
            111,
            179_459,
            124_850,
            4,
            6_044,
            237,
            12,
            6,
            5,
            6,
            4,
            6_780,
            705,
            15,
            1_388,
            44,
            378,
            10_114,
            711,
            152,
            20,
            6,
            5,
            22_376,
            642,
            1_221,
            15_190,
            34_153,
            450,
            5_608,
            959,
            1_119,
            57_702,
            136,
            186,
            47,
            1_098,
            29_367,
            47,
            # 4426, # What fairseq tokenizes from "<unk>": "_<"
            # 3678, # What fairseq tokenizes from "<unk>": "unk"
            # 2740, # What fairseq tokenizes from "<unk>": ">"
            3,  # What we tokenize from "<unk>": "<unk>"
            6,  # Residue from the tokenization: an extra sentencepiece underline
            4,
            6_044,
            237,
            6_284,
            50_901,
            528,
            31,
            90,
            34,
            927,
            2,
        ]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(_a       ,				self.big_tokenizer.encode(_a				)				)





   @slow
   def 		_lowerCamelCase (self				)    ->     List[str]:
        # fmt: off
        lowercase_					:       Tuple     				=					{'input_ids': [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 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, 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]], '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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=_a       ,				model_name='xlm-roberta-base'       ,				revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'       ,				)





'''simple docstring'''




import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A								=   logging.get_logger(__name__)

_A								=   '▁'

_A								=   {'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

_A								=   {
    'sentencepiece_model_file': 'sentencepiece.bpe.model',
    'vocab_file': 'vocab.txt',
}

_A								=   {
    'vocab_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
    },
    'sentencepiece_model_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
    },
}

_A								=   {
    'ernie-m-base': 5_1_4,
    'ernie-m-large': 5_1_4,
}

_A								=   {
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       List[str]     =    ["input_ids"]

   A							:       Optional[Any]     =    VOCAB_FILES_NAMES
   A							:       Dict     =    PRETRAINED_INIT_CONFIGURATION
   A							:       int     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A							:       List[str]     =    PRETRAINED_VOCAB_FILES_MAP
   A							:       Any     =    RESOURCE_FILES_NAMES
   def __init__(self       ,				_a       ,				_a=None       ,				_a=False       ,				_a="utf8"       ,				_a="[UNK]"       ,				_a="[SEP]"       ,				_a="[PAD]"       ,				_a="[CLS]"       ,				_a="[MASK]"       ,				_a = None       ,				**_a       ,				)    ->     None:
        # Mask token behave like a normal word, i.e. include the space before it and
        # is included in the raw text, there should be a match in a non-normalized sentence.

        lowercase_					:       Optional[Any]     				=					{} if sp_model_kwargs is None else sp_model_kwargs
        super().__init__(
            do_lower_case=_a       ,				unk_token=_a       ,				sep_token=_a       ,				pad_token=_a       ,				cls_token=_a       ,				mask_token=_a       ,				vocab_file=_a       ,				encoding=_a       ,				sp_model_kwargs=self.sp_model_kwargs       ,				**_a       ,				)
        lowercase_					:       int     				=					do_lower_case
        lowercase_					:       str     				=					sentencepiece_model_ckpt
        lowercase_					:       Tuple     				=					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(_a				)

        # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
        if vocab_file is not None:
             lowercase_					:       Union[str, Any]     				=					self.load_vocab(filepath=_a				)
        else:
             lowercase_					:       Any     				=					{self.sp_model.id_to_piece(_a				): id for id in range(self.sp_model.get_piece_size()				)}
        lowercase_					:       Optional[int]     				=					{v: k for k, v in self.vocab.items()}
   def 		_lowerCamelCase (self       ,				_a				)    ->     Any:
        if text is None:
             return None

        lowercase_					:       Dict     				=					self.tokenize(_a				)
        lowercase_				,lowercase_					:       Dict     				=					'', []

        for i, ch in enumerate(_a				):
             if ch in self.SP_CHAR_MAPPING:
                  lowercase_					:       str     				=					self.SP_CHAR_MAPPING.get(_a				)
             else:
                  lowercase_					:       List[str]     				=					unicodedata.normalize('NFKC'       ,				_a				)
             if self.is_whitespace(_a				):
                  continue
             normalized_text += ch
             char_mapping.extend([i] * len(_a				)				)

        lowercase_				,lowercase_				,lowercase_					:       int     				=					normalized_text, [], 0

        if self.do_lower_case:
             lowercase_					:       Optional[Any]     				=					text.lower()

        for token in split_tokens:
             if token[:1] == "▁":
                  lowercase_					:       Tuple     				=					token[1:]
             lowercase_					:       List[str]     				=					text[offset:].index(_a				) + offset
             lowercase_					:       Any     				=					start + len(_a				)

             token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)				)
             lowercase_					:       Tuple     				=					end
        return token_mapping
   @property
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        return len(self.vocab				)
   def 		_lowerCamelCase (self				)    ->     int:
        return dict(self.vocab       ,				**self.added_tokens_encoder				)
   def __getstate__(self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					self.__dict__.copy()
        lowercase_					:       List[str]     				=					None
        return state
   def __setstate__(self       ,				_a				)    ->     Any:
        lowercase_					:       Optional[int]     				=					d

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

        lowercase_					:       Optional[int]     				=					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(self.sentencepiece_model_ckpt				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     str:
        return "".join((self.SP_CHAR_MAPPING.get(_a       ,				_a				) for c in text)				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a=False       ,				_a=64       ,				_a=0.1				)    ->     Optional[int]:

        if self.sp_model_kwargs.get('enable_sampling'				) is True:
             lowercase_					:       Optional[int]     				=					True
        if self.sp_model_kwargs.get('alpha'				) is not None:
             lowercase_					:       Union[str, Any]     				=					self.sp_model_kwargs.get('alpha'				)
        if self.sp_model_kwargs.get('nbest_size'				) is not None:
             lowercase_					:       Optional[int]     				=					self.sp_model_kwargs.get('nbest_size'				)

        if not enable_sampling:
             lowercase_					:       Optional[Any]     				=					self.sp_model.EncodeAsPieces(_a				)
        else:
             lowercase_					:       List[Any]     				=					self.sp_model.SampleEncodeAsPieces(_a       ,				_a       ,				_a				)
        lowercase_					:       int     				=					[]
        for pi, piece in enumerate(_a				):
             if piece == SPIECE_UNDERLINE:
                  if not pieces[pi + 1].startswith(_a				) and pi != 0:
                       new_pieces.append(_a				)
                       continue
                  else:
                       continue
             lowercase_					:       Optional[Any]     				=					0
             for i, chunk in enumerate(_a				):
                  if chunk == SPIECE_UNDERLINE:
                       continue
                  if self.is_ch_char(_a				) or self.is_punct(_a				):
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                            new_pieces.append(piece[lst_i:i]				)
                       new_pieces.append(_a				)
                       lowercase_					:       List[Any]     				=					i + 1
                  elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                            new_pieces.append(piece[lst_i:i]				)
                       lowercase_					:       Optional[Any]     				=					i
                  elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                            new_pieces.append(piece[lst_i:i]				)
                       lowercase_					:       int     				=					i
             if len(_a				) > lst_i:
                  new_pieces.append(piece[lst_i:]				)
        return new_pieces
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       Optional[Any]     				=					''.join(_a				).replace(_a       ,				' '				).strip()
        return out_string
   def 		_lowerCamelCase (self       ,				_a				)    ->     Optional[int]:
        lowercase_					:       Optional[Any]     				=					self.convert_ids_to_tokens(_a				)
        lowercase_					:       Tuple     				=					''.join(_a				).replace(_a       ,				' '				).strip()
        return out_string
   def 		_lowerCamelCase (self       ,				_a				)    ->     Optional[int]:
        return self.vocab.get(_a       ,				self.vocab.get(self.unk_token				)				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        return self.reverse_vocab.get(_a       ,				self.unk_token				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None				)    ->     Any:
        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowercase_					:       Tuple     				=					[self.cls_token_id]
        lowercase_					:       int     				=					[self.sep_token_id]
        return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None				)    ->     Optional[Any]:
        if offset_mapping_a is None:
             return [(0, 0)] + offset_mapping_a + [(0, 0)]

        return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None       ,				_a=False				)    ->     Union[str, Any]:

        if already_has_special_tokens:
             if token_ids_a is not None:
                  raise ValueError(
                      'You should not supply a second sequence if the provided sequence of '
                      'ids is already formatted with special tokens for the model.'				)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
             return [1] + ([0] * len(_a				)) + [1, 1] + ([0] * len(_a				)) + [1]
        return [1] + ([0] * len(_a				)) + [1]
   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     List[int]:
        # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
        if token_ids_a is None:
             # [CLS] X [SEP]
             return (len(_a				) + 2) * [0]

        # [CLS] A [SEP] [SEP] B [SEP]
        return [0] * (len(_a				) + 1) + [1] * (len(_a				) + 3)
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        if "\u4e00" <= char <= "\u9fff":
             return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[str]:
        if ("a" <= char <= "z") or ("A" <= char <= "Z"):
             return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        if char in ",;:.?!~，；：。？！《》【】":
             return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[Any]:
        if char == " " or char == "\t" or char == "\n" or char == "\r":
             return True
        if len(_a				) == 1:
             lowercase_					:       Optional[Any]     				=					unicodedata.category(_a				)
             if cat == "Zs":
                  return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     Tuple:
        lowercase_					:       List[Any]     				=					{}
        with io.open(_a       ,				'r'       ,				encoding='utf-8'				) as f:
             for index, line in enumerate(_a				):
                  lowercase_					:       Dict     				=					line.rstrip('\n'				)
                  lowercase_					:       Dict     				=					int(_a				)

        return token_to_idx





   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     Tuple[str]:
        lowercase_					:       Tuple     				=					0
        if os.path.isdir(_a				):
             lowercase_					:       Any     				=					os.path.join(
                 _a       ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
        else:
             lowercase_					:       Tuple     				=					(filename_prefix + '-' if filename_prefix else '') + save_directory
        with open(_a       ,				'w'       ,				encoding='utf-8'				) as writer:
             for token, token_index in sorted(self.vocab.items()       ,				key=lambda _a				: kv[1]				):
                  if index != token_index:
                       logger.warning(
                           f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                           ' Please check that the vocabulary is not corrupted!'				)
                       lowercase_					:       Tuple     				=					token_index
                  writer.write(token + '\n'				)
                  index += 1

        lowercase_					:       List[str]     				=					os.path.join(_a       ,				'sentencepiece.bpe.model'				)
        with open(_a       ,				'wb'				) as fi:
             lowercase_					:       Tuple     				=					self.sp_model.serialized_model_proto()
             fi.write(_a				)

        return (vocab_file,)




