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import os |
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from typing import Optional, Tuple, List |
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from collections import OrderedDict |
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from torch.utils.data import Dataset |
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from transformers import PreTrainedTokenizer |
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def load_vocab(vocab_file): |
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vocab = 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 CharTokenizer(PreTrainedTokenizer): |
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vocab_files_names = {"vocab_file": "vocab.txt"} |
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def __init__( |
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self, |
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vocab_file=None, |
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pad_token="[PAD]", |
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unk_token="[UNK]", |
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bos_token="[BOS]", |
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eos_token="[EOS]", |
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*args, |
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**kwargs |
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): |
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super().__init__( |
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pad_token=pad_token, |
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unk_token=unk_token, |
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bos_token=bos_token, |
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eos_token=eos_token, |
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**kwargs |
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) |
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if not vocab_file or not os.path.isfile(vocab_file): |
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self.vocab = OrderedDict() |
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self.ids_to_tokens = OrderedDict() |
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else: |
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self.vocab = load_vocab(vocab_file) |
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self.ids_to_tokens = OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) |
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def train(self, file_path): |
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vocab = set() |
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with open(file_path) as r: |
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for line in r: |
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word = line.strip() |
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vocab |= set(word) |
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vocab = list(vocab) |
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vocab.sort() |
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special_tokens = [self.pad_token, self.unk_token, self.bos_token, self.eos_token] |
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vocab = special_tokens + vocab |
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for i, ch in enumerate(vocab): |
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self.vocab[ch] = i |
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self.ids_to_tokens = vocab |
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@property |
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def vocab_size(self): |
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return len(self.vocab) |
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def get_vocab(self): |
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return self.vocab |
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def _convert_token_to_id(self, token): |
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return self.vocab.get(token) |
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def _convert_id_to_token(self, index): |
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return self.ids_to_tokens[index] |
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def _tokenize(self, text): |
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return list(text) |
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def convert_tokens_to_string(self, tokens): |
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return "".join(tokens) |
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def build_inputs_with_special_tokens( |
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self, |
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token_ids_0: List[int], |
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token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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bos = [self.bos_token_id] |
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eos = [self.eos_token_id] |
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return bos + token_ids_0 + eos |
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def get_special_tokens_mask( |
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self, |
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token_ids_0: List[int], |
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token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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return [1] + ([0] * len(token_ids_0)) + [1] |
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def create_token_type_ids_from_sequences( |
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self, |
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token_ids_0: List[int], |
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token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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return (len(token_ids_0) + 2) * [0] |
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def save_vocabulary( |
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self, |
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save_directory: str, |
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filename_prefix: Optional[str] = None |
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) -> Tuple[str]: |
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assert os.path.isdir(save_directory) |
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vocab_file = os.path.join( |
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save_directory, |
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(filename_prefix + "-" if filename_prefix else "") + |
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self.vocab_files_names["vocab_file"] |
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) |
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index = 0 |
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with open(vocab_file, "w", encoding="utf-8") as writer: |
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for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): |
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assert 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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