from pathlib import Path
from typing import Dict, Iterable, List, Union
import numpy as np
from typeguard import typechecked
[docs]class TokenIDConverter:
@typechecked
def __init__(
self,
token_list: Union[Path, str, Iterable[str]],
unk_symbol: str = "<unk>",
):
if isinstance(token_list, (Path, str)):
token_list = Path(token_list)
self.token_list_repr = str(token_list)
self.token_list: List[str] = []
with token_list.open("r", encoding="utf-8") as f:
for idx, line in enumerate(f):
line = line[0] + line[1:].rstrip()
self.token_list.append(line)
else:
self.token_list: List[str] = list(token_list)
self.token_list_repr = ""
for i, t in enumerate(self.token_list):
if i == 3:
break
self.token_list_repr += f"{t}, "
self.token_list_repr += f"... (NVocab={(len(self.token_list))})"
self.token2id: Dict[str, int] = {}
for i, t in enumerate(self.token_list):
if t in self.token2id:
raise RuntimeError(f'Symbol "{t}" is duplicated')
self.token2id[t] = i
self.unk_symbol = unk_symbol
if self.unk_symbol not in self.token2id:
raise RuntimeError(
f"Unknown symbol '{unk_symbol}' doesn't exist in the token_list"
)
self.unk_id = self.token2id[self.unk_symbol]
[docs] def get_num_vocabulary_size(self) -> int:
return len(self.token_list)
[docs] def ids2tokens(self, integers: Union[np.ndarray, Iterable[int]]) -> List[str]:
if isinstance(integers, np.ndarray) and integers.ndim != 1:
raise ValueError(f"Must be 1 dim ndarray, but got {integers.ndim}")
return [self.token_list[i] for i in integers]
[docs] def tokens2ids(self, tokens: Iterable[str]) -> List[int]:
return [self.token2id.get(i, self.unk_id) for i in tokens]