230 lines
8.4 KiB
Python
230 lines
8.4 KiB
Python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for RWKV5."""
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import os
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import re
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from typing import TYPE_CHECKING, List, Optional, Tuple
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from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
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from transformers.utils import logging
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if TYPE_CHECKING:
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pass
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.txt",
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}
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PRETRAINED_VOCAB_FILES_MAP = {
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"vocab_file": {
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"ArthurZ/rwkv-5-utf": "https://huggingface.co/ArthurZ/rwkv-5-utf/blob/main/vocab.txt",
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},
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}
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def whitespace_tokenize(text):
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"""Runs basic whitespace cleaning and splitting on a piece of text.
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The separators are kept
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"""
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text = text.strip()
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if not text:
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return []
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tokens = re.split(b"(?= )", text)
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return tokens
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class WordpieceTokenizer(object):
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"""Runs WordPiece tokenization."""
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def __init__(self, vocab, unk_token):
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self.vocab = vocab
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self.unk_token = unk_token
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def tokenize(self, text):
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"""
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Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
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tokenization using the given vocabulary.
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For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
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Args:
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text: A single token or whitespace separated tokens. This should have
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already been passed through *BasicTokenizer*.
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Returns:
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A list of wordpiece tokens.
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"""
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output_tokens = []
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for token in whitespace_tokenize(text):
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chars = list(token)
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is_bad = False
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start = 0
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sub_tokens = []
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while start < len(chars):
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end = len(chars)
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cur_substr = None
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while start < end:
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substr = bytes(chars[start:end])
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if substr in self.vocab:
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cur_substr = substr
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break
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end -= 1
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if cur_substr is None:
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is_bad = True
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break
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try:
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cur_substr = cur_substr.decode()
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except UnicodeDecodeError:
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cur_substr = str(cur_substr)
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sub_tokens.append(cur_substr)
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start = end
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if is_bad:
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output_tokens.append(self.unk_token)
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else:
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output_tokens.extend(sub_tokens)
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return output_tokens
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class Rwkv5Tokenizer(PreTrainedTokenizer):
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vocab_files_names = VOCAB_FILES_NAMES
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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max_model_input_sizes = {"ArthurZ/rwkv-5-utf": 2048}
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(self, vocab_file, bos_token="<s>", eos_token="<s>", unk_token="<s>", **kwargs):
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if not os.path.isfile(vocab_file):
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raise ValueError(
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f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
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" model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
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)
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with open(vocab_file, "r") as reader:
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tokens = reader.readlines()
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vocab = {}
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for index, token in enumerate(tokens):
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token = eval(token.rstrip("\n"))
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vocab[token] = index
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self.add_bos_token = True
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self.encoder = vocab
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self.decoder = {v: k for k, v in vocab.items()}
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.encoder, unk_token=str(unk_token))
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self._added_tokens_decoder = {0: AddedToken(str(bos_token))}
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super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
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@property
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def vocab_size(self):
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return len(self.encoder)
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def get_vocab(self):
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vocab = {str(self.convert_ids_to_tokens(i)): i for i in range(self.vocab_size)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def _tokenize(self, text, split_special_tokens=False):
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return self.wordpiece_tokenizer.tokenize(text.encode("utf-8"))
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def _convert_token_to_id(self, token):
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"""Converts a token (byte) to an id using the vocab."""
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if token.startswith("b'\\"):
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token = eval(token)
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elif not isinstance(token, bytes):
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token = token.encode("utf-8", errors="replace")
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return self.encoder.get(token, self.unk_token_id)
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (byte) using the vocab."""
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token = self.decoder.get(index, self.unk_token)
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if isinstance(token, (bytes)):
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token = token.decode("utf-8", errors="replace")
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return token
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (bytes) in a single string. Additional tokens are encoded to bytes"""
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out_string = b"".join([k.encode(errors="replace") if isinstance(k, str) else k for k in tokens]).decode(
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"utf-8"
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)
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return out_string
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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index = 0
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if os.path.isdir(save_directory):
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vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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else:
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vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
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with open(vocab_file, "w") as writer:
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for token, token_index in sorted(self.encoder.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning(
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f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
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" Please check that the vocabulary is not corrupted!"
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)
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index = token_index
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writer.write(str(token) + "\n")
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index += 1
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return (vocab_file,)
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
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if self.add_bos_token:
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bos_token_ids = [self.bos_token_id]
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else:
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bos_token_ids = []
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output = bos_token_ids + token_ids_0
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if token_ids_1 is None:
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return output
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return output + bos_token_ids + token_ids_1
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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if not self.add_bos_token:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False
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)
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if token_ids_1 is None:
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return [1] + ([0] * len(token_ids_0))
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
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