267 lines
9.5 KiB
Python
267 lines
9.5 KiB
Python
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# coding=utf-8
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# Copyright 2022 The OpenBMB Team and The HuggingFace Inc. team. All rights reserved.
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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 CPMAnt."""
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import collections
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import os
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from typing import List, Optional, Tuple
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from transformers.utils import is_jieba_available, requires_backends
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if is_jieba_available():
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import jieba
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from ...tokenization_utils import PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
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def load_vocab(vocab_file):
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"""Loads a vocabulary file into a dictionary."""
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vocab = collections.OrderedDict()
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with open(vocab_file, "r", encoding="utf-8") as reader:
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tokens = reader.readlines()
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for index, token in enumerate(tokens):
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token = token.rstrip("\n")
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vocab[token] = index
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return vocab
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class WordpieceTokenizer(object):
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def __init__(self, vocab, unk_token="<unk>", max_input_chars_per_word=200):
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self.vocab = vocab
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self.unk_token = unk_token
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self.max_input_chars_per_word = max_input_chars_per_word
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def tokenize(self, token):
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chars = list(token)
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if len(chars) > self.max_input_chars_per_word:
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return [self.unk_token]
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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 = "".join(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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sub_tokens.append(self.unk_token)
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start += 1
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else:
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sub_tokens.append(cur_substr)
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start = end
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return sub_tokens
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class CpmAntTokenizer(PreTrainedTokenizer):
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"""
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Construct a CPMAnt tokenizer. Based on byte-level Byte-Pair-Encoding.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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bod_token (`str`, *optional*, defaults to `"<d>"`):
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The beginning of document token.
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eod_token (`str`, *optional*, defaults to `"</d>"`):
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The end of document token.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token.
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token.
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line_token (`str`, *optional*, defaults to `"</n>"`):
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The line token.
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space_token (`str`, *optional*, defaults to `"</_>"`):
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The space token.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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add_prefix_space = False
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def __init__(
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self,
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vocab_file,
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bod_token="<d>",
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eod_token="</d>",
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bos_token="<s>",
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eos_token="</s>",
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pad_token="<pad>",
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unk_token="<unk>",
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line_token="</n>",
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space_token="</_>",
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padding_side="left",
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**kwargs,
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):
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requires_backends(self, ["jieba"])
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self.bod_token = bod_token
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self.eod_token = eod_token
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self.encoder = load_vocab(vocab_file)
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self.encoder[" "] = self.encoder[space_token]
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self.encoder["\n"] = self.encoder[line_token]
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del self.encoder[space_token]
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del self.encoder[line_token]
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self.encoder = collections.OrderedDict(sorted(self.encoder.items(), key=lambda x: x[1]))
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self.decoder = {v: k for k, v in self.encoder.items()}
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.encoder, unk_token=unk_token)
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super().__init__(
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bod_token=bod_token,
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eod_token=eod_token,
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bos_token=bos_token,
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eos_token=eos_token,
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pad_token=pad_token,
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unk_token=unk_token,
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line_token=line_token,
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space_token=space_token,
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padding_side=padding_side,
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**kwargs,
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)
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@property
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def bod_token_id(self):
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return self.encoder[self.bod_token]
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@property
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def eod_token_id(self):
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return self.encoder[self.eod_token]
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@property
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def newline_id(self):
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return self.encoder["\n"]
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@property
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def vocab_size(self) -> int:
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return len(self.encoder)
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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def _tokenize(self, text):
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"""Tokenize a string."""
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output_tokens = []
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for x in jieba.cut(text, cut_all=False):
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output_tokens.extend(self.wordpiece_tokenizer.tokenize(x))
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return output_tokens
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def _decode(self, token_ids, **kwargs):
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"""Decode ids into a string."""
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token_ids = [i for i in token_ids if i >= 0]
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token_ids = [
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x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
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]
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return super()._decode(token_ids, **kwargs)
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def check(self, token):
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return token in self.encoder
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def convert_tokens_to_string(self, tokens: List[str]) -> str:
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return "".join(tokens)
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.decoder.get(index, self.unk_token)
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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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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index = 0
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if " " in self.encoder:
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self.encoder["</_>"] = self.encoder[" "]
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del self.encoder[" "]
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if "\n" in self.encoder:
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self.encoder["</n>"] = self.encoder["\n"]
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del self.encoder["\n"]
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self.encoder = collections.OrderedDict(sorted(self.encoder.items(), key=lambda x: x[1]))
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with open(vocab_file, "w", encoding="utf-8") as writer:
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for token, token_index in self.encoder.items():
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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(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: List[int], token_ids_1: List[int] = None) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A CPMAnt sequence has the following format:
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- single sequence: `[BOS] Sequence`.
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Args:
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token_ids_0 (`List[int]`): The first tokenized sequence that special tokens will be added.
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token_ids_1 (`List[int]`): The optional second tokenized sequence that special tokens will be added.
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Returns:
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`List[int]`: The model input with special tokens.
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"""
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if token_ids_1 is None:
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return [self.bos_token_id] + token_ids_0
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return [self.bos_token_id] + token_ids_0 + [self.bos_token_id] + 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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Retrieve 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` method.
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Args:
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token_ids_0 (`List[int]`): List of IDs.
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token_ids_1 (`List[int]`, *optional*): 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 token_ids_1 is not None:
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
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return [1] + ([0] * len(token_ids_0))
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