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| | | from abc import abstractmethod |
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| | | from typing import Iterable |
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| | | from typing import List |
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| | | from pathlib import Path |
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| | | from typing import Dict |
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| | | from typing import Iterable |
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| | | from typing import List |
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| | | from typing import Union |
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| | | |
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| | | import numpy as np |
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| | | |
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| | | class AbsTokenizer(ABC): |
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| | | @abstractmethod |
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| | | def text2tokens(self, line: str) -> List[str]: |
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| | | raise NotImplementedError |
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| | | |
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| | | @abstractmethod |
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| | | def tokens2text(self, tokens: Iterable[str]) -> str: |
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| | | raise NotImplementedError |
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| | | |
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| | | |
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| | | class BaseTokenizer(ABC): |
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| | | def __init__(self, token_list: Union[Path, str, Iterable[str]]=None, |
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| | | unk_symbol: str = "<unk>", |
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| | | **kwargs, |
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| | | ): |
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| | | |
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| | | if token_list is not None: |
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| | | if isinstance(token_list, (Path, str)): |
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| | | token_list = Path(token_list) |
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| | | self.token_list_repr = str(token_list) |
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| | | self.token_list: List[str] = [] |
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| | | |
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| | | with token_list.open("r", encoding="utf-8") as f: |
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| | | for idx, line in enumerate(f): |
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| | | line = line.rstrip() |
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| | | self.token_list.append(line) |
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| | | |
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| | | else: |
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| | | self.token_list: List[str] = list(token_list) |
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| | | self.token_list_repr = "" |
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| | | for i, t in enumerate(self.token_list): |
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| | | if i == 3: |
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| | | break |
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| | | self.token_list_repr += f"{t}, " |
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| | | self.token_list_repr += f"... (NVocab={(len(self.token_list))})" |
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| | | |
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| | | self.token2id: Dict[str, int] = {} |
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| | | for i, t in enumerate(self.token_list): |
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| | | if t in self.token2id: |
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| | | raise RuntimeError(f'Symbol "{t}" is duplicated') |
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| | | self.token2id[t] = i |
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| | | |
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| | | self.unk_symbol = unk_symbol |
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| | | if self.unk_symbol not in self.token2id: |
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| | | raise RuntimeError( |
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| | | f"Unknown symbol '{unk_symbol}' doesn't exist in the token_list" |
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| | | ) |
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| | | self.unk_id = self.token2id[self.unk_symbol] |
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| | | |
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| | | def encode(self, text): |
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| | | tokens = self.text2tokens(text) |
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| | | text_ints = self.tokens2ids(tokens) |
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| | | |
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| | | return text_ints |
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| | | |
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| | | def decode(self, text_ints): |
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| | | return self.ids2tokens(text_ints) |
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| | | |
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| | | def get_num_vocabulary_size(self) -> int: |
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| | | return len(self.token_list) |
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| | | |
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| | | def ids2tokens(self, integers: Union[np.ndarray, Iterable[int]]) -> List[str]: |
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| | | if isinstance(integers, np.ndarray) and integers.ndim != 1: |
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| | | raise ValueError(f"Must be 1 dim ndarray, but got {integers.ndim}") |
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| | | return [self.token_list[i] for i in integers] |
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| | | |
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| | | def tokens2ids(self, tokens: Iterable[str]) -> List[int]: |
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| | | return [self.token2id.get(i, self.unk_id) for i in tokens] |
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| | | |
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| | | @abstractmethod |
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| | | def text2tokens(self, line: str) -> List[str]: |
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| | | raise NotImplementedError |
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| | | |
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| | | @abstractmethod |
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| | | def tokens2text(self, tokens: Iterable[str]) -> str: |
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| | | raise NotImplementedError |
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