File size: 2,694 Bytes
35fd7fa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | """SentencePiece tokenizer wrapper for Hugging Face AutoTokenizer."""
from __future__ import annotations
import shutil
from pathlib import Path
from transformers import PreTrainedTokenizer
class VortexTokenizer(PreTrainedTokenizer):
vocab_files_names = {"vocab_file": "tokenizer.model"}
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file: str,
unk_token: str = "<unk>",
eos_token: str = "</s>",
**kwargs,
) -> None:
import sentencepiece as spm
self.vocab_file = str(vocab_file)
self.sp_model = spm.SentencePieceProcessor(model_file=self.vocab_file)
super().__init__(unk_token=unk_token, eos_token=eos_token, **kwargs)
@property
def vocab_size(self) -> int:
return int(self.sp_model.vocab_size())
def get_vocab(self) -> dict[str, int]:
vocab = {self.sp_model.id_to_piece(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text: str, **kwargs) -> list[str]:
return list(self.sp_model.encode(text, out_type=str))
def _convert_token_to_id(self, token: str) -> int:
token_id = int(self.sp_model.piece_to_id(token))
return self.unk_token_id if token_id < 0 else token_id
def _convert_id_to_token(self, index: int) -> str:
return self.sp_model.id_to_piece(int(index))
def convert_tokens_to_string(self, tokens: list[str]) -> str:
return self.sp_model.decode_pieces(tokens)
def build_inputs_with_special_tokens(
self,
token_ids_0: list[int],
token_ids_1: list[int] | None = None,
) -> list[int]:
if token_ids_1 is None:
return list(token_ids_0)
return list(token_ids_0) + list(token_ids_1)
def get_special_tokens_mask(
self,
token_ids_0: list[int],
token_ids_1: list[int] | None = None,
already_has_special_tokens: bool = False,
) -> list[int]:
if already_has_special_tokens:
return [0] * len(token_ids_0)
total = len(token_ids_0) + (len(token_ids_1) if token_ids_1 is not None else 0)
return [0] * total
def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]:
directory = Path(save_directory)
directory.mkdir(parents=True, exist_ok=True)
name = (filename_prefix + "-" if filename_prefix else "") + "tokenizer.model"
output = directory / name
if Path(self.vocab_file).resolve() != output.resolve():
shutil.copyfile(self.vocab_file, output)
return (str(output),)
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