tokenization_bashkir_roberta.py

"""SentencePiece tokenizer preserving BashkirRoBERTa's original token IDs."""

from pathlib import Path

import sentencepiece as spm
from transformers import PreTrainedTokenizer


class BashkirRobertaTokenizer(PreTrainedTokenizer):
    vocab_files_names = {"vocab_file": "spm_bashkir_bert_16k.model"}
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(self, vocab_file, **kwargs):
        self.vocab_file = vocab_file
        self.sp_model = spm.SentencePieceProcessor(model_file=vocab_file)
        defaults = {
            "bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>",
            "pad_token": "<pad>", "cls_token": "[CLS]", "sep_token": "[SEP]",
            "mask_token": "[MASK]",
        }
        for key, value in defaults.items():
            kwargs.setdefault(key, value)
        super().__init__(**kwargs)

    @property
    def vocab_size(self):
        return self.sp_model.get_piece_size()

    def get_vocab(self):
        return {self.sp_model.id_to_piece(i): i for i in range(self.vocab_size)}

    def _tokenize(self, text):
        return self.sp_model.encode(text, out_type=str)

    def _convert_token_to_id(self, token):
        return self.sp_model.piece_to_id(token)

    def _convert_id_to_token(self, index):
        return self.sp_model.id_to_piece(index)

    def convert_tokens_to_string(self, tokens):
        return self.sp_model.decode(tokens)

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        if token_ids_1 is not None:
            return [self.bos_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1 + [self.eos_token_id]
        return [self.bos_token_id] + token_ids_0 + [self.eos_token_id]

    def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
        if already_has_special_tokens:
            return super().get_special_tokens_mask(token_ids_0, token_ids_1, True)
        if token_ids_1 is None:
            return [1] + [0] * len(token_ids_0) + [1]
        return [1] + [0] * len(token_ids_0) + [1] + [0] * len(token_ids_1) + [1]

    def save_vocabulary(self, save_directory, filename_prefix=None):
        source = Path(self.vocab_file)
        name = ((filename_prefix + "-") if filename_prefix else "") + self.vocab_files_names["vocab_file"]
        target = Path(save_directory) / name
        target.write_bytes(source.read_bytes())
        return (str(target),)