





import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


snake_case__					:							List[Any]													=     logging.get_logger(__name__)

snake_case__					:							Optional[Any]													=     {
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

snake_case__					:							str													=     {
    'artists_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
    },
    'genres_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
    },
    'lyrics_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
    },
}

snake_case__					:							Optional[Any]													=     {
    'jukebox': 512,
}





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = VOCAB_FILES_NAMES
   lowerCAmelCase__        = PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase__        = PRETRAINED_LYRIC_TOKENS_SIZES
   lowerCAmelCase__        = ["""input_ids""", """attention_mask"""]




   def __init__(self	:List[str]    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:Any=["v3", "v2", "v2"]    ,   _UpperCamelCase	:Optional[Any]=512    ,   _UpperCamelCase	:List[str]=5    ,   _UpperCamelCase	:Any="<|endoftext|>"    ,   **_UpperCamelCase	:str    ,   )->					List[Any]:
         __A          =				AddedToken(_UpperCamelCase    ,   lstrip=_UpperCamelCase    ,   rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase    ,   _UpperCamelCase ) else unk_token
         super().__init__(
             unk_token=_UpperCamelCase    ,   n_genres=_UpperCamelCase    ,   version=_UpperCamelCase    ,   max_n_lyric_tokens=_UpperCamelCase    ,   **_UpperCamelCase    ,   )
         __A          =				version
         __A          =				max_n_lyric_tokens
         __A          =				n_genres

         with open(_UpperCamelCase    ,   encoding='''utf-8''' ) as vocab_handle:
               __A          =				json.load(_UpperCamelCase )

         with open(_UpperCamelCase    ,   encoding='''utf-8''' ) as vocab_handle:
               __A          =				json.load(_UpperCamelCase )

         with open(_UpperCamelCase    ,   encoding='''utf-8''' ) as vocab_handle:
               __A          =				json.load(_UpperCamelCase )

         __A          =				R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''
         # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
         if len(self.lyrics_encoder ) == 79:
               __A          =				oov.replace(R'''\-\''''    ,   R'''\-+\'''' )

         __A          =				regex.compile(_UpperCamelCase )
         __A          =				{v: k for k, v in self.artists_encoder.items()}
         __A          =				{v: k for k, v in self.genres_encoder.items()}
         __A          =				{v: k for k, v in self.lyrics_encoder.items()}




   @property
   def _lowerCAmelCase      (self	:Optional[Any] )->					Union[str, Any]:
         return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )




   def _lowerCAmelCase      (self	:Dict )->					List[str]:
         return dict(self.artists_encoder    ,   self.genres_encoder    ,   self.lyrics_encoder )




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:List[Any] )->					Any:
         __A          =				[self.artists_encoder.get(_UpperCamelCase    ,   0 ) for artist in list_artists]
         for genres in range(len(_UpperCamelCase ) ):
               __A          =				[self.genres_encoder.get(_UpperCamelCase    ,   0 ) for genre in list_genres[genres]]
               __A          =				list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))

         __A          =				[[self.lyrics_encoder.get(_UpperCamelCase    ,   0 ) for character in list_lyrics[0]], [], []]
         return artists_id, list_genres, lyric_ids




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:Tuple )->					List[str]:
         return list(_UpperCamelCase )




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:List[str]    ,   **_UpperCamelCase	:str )->					Optional[int]:
         __A   ,						__A   ,						__A          =				self.prepare_for_tokenization(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				self._tokenize(_UpperCamelCase )
         return artist, genre, lyrics




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:bool = False )->					Tuple[str, str, str, Dict[str, Any]]:
         for idx in range(len(self.version ) ):
               if self.version[idx] == "v3":
                     __A          =				artists[idx].lower()
                     __A          =				[genres[idx].lower()]
               else:
                     __A          =				self._normalize(artists[idx] ) + '''.v2'''
                     __A          =				[
                         self._normalize(_UpperCamelCase ) + '''.v2''' for genre in genres[idx].split('''_''' )
                     ]  # split is for the full dictionary with combined genres

         if self.version[0] == "v2":
               __A          =				regex.compile(R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+''' )
               __A          =				'''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'''
               __A          =				{vocab[index]: index + 1 for index in range(len(_UpperCamelCase ) )}
               __A          =				0
               __A          =				len(_UpperCamelCase ) + 1
               __A          =				self.vocab
               __A          =				{v: k for k, v in self.vocab.items()}
               __A          =				''''''
         else:
               __A          =				regex.compile(R'''[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+''' )

         __A          =				self._run_strip_accents(_UpperCamelCase )
         __A          =				lyrics.replace('''\\'''    ,   '''\n''' )
         __A          =				self.out_of_vocab.sub(''''''    ,   _UpperCamelCase ), [], []
         return artists, genres, lyrics




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:List[str] )->					int:
         __A          =				unicodedata.normalize('''NFD'''    ,   _UpperCamelCase )
         __A          =				[]
         for char in text:
               __A          =				unicodedata.category(_UpperCamelCase )
               if cat == "Mn":
                     continue
               output.append(_UpperCamelCase )
         return "".join(_UpperCamelCase )




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:str )->					str:
         __A          =				(
             [chr(_UpperCamelCase ) for i in range(ord('''a''' )    ,   ord('''z''' ) + 1 )]
             + [chr(_UpperCamelCase ) for i in range(ord('''A''' )    ,   ord('''Z''' ) + 1 )]
             + [chr(_UpperCamelCase ) for i in range(ord('''0''' )    ,   ord('''9''' ) + 1 )]
             + ['''.''']
         )
         __A          =				frozenset(_UpperCamelCase )
         __A          =				re.compile(R'''_+''' )
         __A          =				''''''.join([c if c in accepted else '''_''' for c in text.lower()] )
         __A          =				pattern.sub('''_'''    ,   _UpperCamelCase ).strip('''_''' )
         return text




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:List[str] )->					str:
         return " ".join(_UpperCamelCase )




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Optional[Union[str, TensorType]] = None    ,   _UpperCamelCase	:bool = False )->					Dict:
         # Convert to TensorType
         if not isinstance(_UpperCamelCase    ,   _UpperCamelCase ):
               __A          =				TensorType(_UpperCamelCase )

         # Get a function reference for the correct framework
         if tensor_type == TensorType.TENSORFLOW:
               if not is_tf_available():
                     raise ImportError(
                         '''Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.''' )
               import tensorflow as tf

               __A          =				tf.constant
               __A          =				tf.is_tensor
         elif tensor_type == TensorType.PYTORCH:
               if not is_torch_available():
                     raise ImportError('''Unable to convert output to PyTorch tensors format, PyTorch is not installed.''' )
               import torch

               __A          =				torch.tensor
               __A          =				torch.is_tensor
         elif tensor_type == TensorType.JAX:
               if not is_flax_available():
                     raise ImportError('''Unable to convert output to JAX tensors format, JAX is not installed.''' )
               import jax.numpy as jnp  # noqa: F811

               __A          =				jnp.array
               __A          =				_is_jax
         else:
               __A          =				np.asarray
               __A          =				_is_numpy

         # Do the tensor conversion in batch

         try:
               if prepend_batch_axis:
                     __A          =				[inputs]

               if not is_tensor(_UpperCamelCase ):
                     __A          =				as_tensor(_UpperCamelCase )
         except:  # noqa E722
               raise ValueError(
                   '''Unable to create tensor, you should probably activate truncation and/or padding '''
                   '''with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.''' )

         return inputs




   def __call__(self	:Any    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:Optional[Any]=""    ,   _UpperCamelCase	:Union[str, Any]="pt" )->					BatchEncoding:
         __A          =				[0, 0, 0]
         __A          =				[artist] * len(self.version )
         __A          =				[genres] * len(self.version )

         __A   ,						__A   ,						__A          =				self.tokenize(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A   ,						__A   ,						__A          =				self._convert_token_to_id(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )

         __A          =				[-INFINITY] * len(full_tokens[-1] )
         __A          =				[
             self.convert_to_tensors(
                 [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]    ,   tensor_type=_UpperCamelCase )
             for i in range(len(self.version ) )
         ]
         return BatchEncoding({'''input_ids''': input_ids, '''attention_masks''': attention_masks} )




   def _lowerCAmelCase      (self	:Tuple    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Optional[str] = None )->					Tuple[str]:
         if not os.path.isdir(_UpperCamelCase ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
               return

         __A          =				os.path.join(
             _UpperCamelCase    ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''artists_file'''] )
         with open(_UpperCamelCase    ,   '''w'''    ,   encoding='''utf-8''' ) as f:
               f.write(json.dumps(self.artists_encoder    ,   ensure_ascii=_UpperCamelCase ) )

         __A          =				os.path.join(
             _UpperCamelCase    ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''genres_file'''] )
         with open(_UpperCamelCase    ,   '''w'''    ,   encoding='''utf-8''' ) as f:
               f.write(json.dumps(self.genres_encoder    ,   ensure_ascii=_UpperCamelCase ) )

         __A          =				os.path.join(
             _UpperCamelCase    ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''lyrics_file'''] )
         with open(_UpperCamelCase    ,   '''w'''    ,   encoding='''utf-8''' ) as f:
               f.write(json.dumps(self.lyrics_encoder    ,   ensure_ascii=_UpperCamelCase ) )

         return (artists_file, genres_file, lyrics_file)






   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Union[str, Any] )->					Dict:
         __A          =				self.artists_decoder.get(_UpperCamelCase )
         __A          =				[self.genres_decoder.get(_UpperCamelCase ) for genre in genres_index]
         __A          =				[self.lyrics_decoder.get(_UpperCamelCase ) for character in lyric_index]
         return artist, genres, lyrics












import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


snake_case__					:							List[Any]													=     logging.get_logger(__name__)

snake_case__					:							Union[str, Any]													=     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case__					:							int													=     {
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

snake_case__					:							int													=     {
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
    'bert-base-german-cased': 512,
    'bert-large-uncased-whole-word-masking': 512,
    'bert-large-cased-whole-word-masking': 512,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
    'bert-base-cased-finetuned-mrpc': 512,
    'bert-base-german-dbmdz-cased': 512,
    'bert-base-german-dbmdz-uncased': 512,
    'TurkuNLP/bert-base-finnish-cased-v1': 512,
    'TurkuNLP/bert-base-finnish-uncased-v1': 512,
    'wietsedv/bert-base-dutch-cased': 512,
}

snake_case__					:							str													=     {
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = VOCAB_FILES_NAMES
   lowerCAmelCase__        = PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase__        = PRETRAINED_INIT_CONFIGURATION
   lowerCAmelCase__        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase__        = BertTokenizer




   def __init__(self	:List[str]    ,   _UpperCamelCase	:List[str]=None    ,   _UpperCamelCase	:Optional[Any]=None    ,   _UpperCamelCase	:str=True    ,   _UpperCamelCase	:Optional[Any]="[UNK]"    ,   _UpperCamelCase	:Tuple="[SEP]"    ,   _UpperCamelCase	:List[Any]="[PAD]"    ,   _UpperCamelCase	:int="[CLS]"    ,   _UpperCamelCase	:Optional[int]="[MASK]"    ,   _UpperCamelCase	:Union[str, Any]=True    ,   _UpperCamelCase	:str=None    ,   **_UpperCamelCase	:List[str]    ,   )->					str:
         super().__init__(
             _UpperCamelCase    ,   tokenizer_file=_UpperCamelCase    ,   do_lower_case=_UpperCamelCase    ,   unk_token=_UpperCamelCase    ,   sep_token=_UpperCamelCase    ,   pad_token=_UpperCamelCase    ,   cls_token=_UpperCamelCase    ,   mask_token=_UpperCamelCase    ,   tokenize_chinese_chars=_UpperCamelCase    ,   strip_accents=_UpperCamelCase    ,   **_UpperCamelCase    ,   )

         __A          =				json.loads(self.backend_tokenizer.normalizer.__getstate__() )
         if (
             normalizer_state.get('''lowercase'''    ,   _UpperCamelCase ) != do_lower_case
             or normalizer_state.get('''strip_accents'''    ,   _UpperCamelCase ) != strip_accents
             or normalizer_state.get('''handle_chinese_chars'''    ,   _UpperCamelCase ) != tokenize_chinese_chars
         ):
               __A          =				getattr(_UpperCamelCase    ,   normalizer_state.pop('''type''' ) )
               __A          =				do_lower_case
               __A          =				strip_accents
               __A          =				tokenize_chinese_chars
               __A          =				normalizer_class(**_UpperCamelCase )

         __A          =				do_lower_case




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]=None )->					List[Any]:
         __A          =				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

         return output




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:List[int]    ,   _UpperCamelCase	:Optional[List[int]] = None )->					List[int]:
         __A          =				[self.sep_token_id]
         __A          =				[self.cls_token_id]
         if token_ids_a is None:
               return len(cls + token_ids_a + sep ) * [0]
         return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Optional[str] = None )->					Tuple[str]:
         __A          =				self._tokenizer.model.save(_UpperCamelCase    ,   name=_UpperCamelCase )
         return tuple(_UpperCamelCase )





