57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
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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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import numpy as np
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class TokenIDConverter:
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def __init__(
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self,
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token_list: Union[Path, str, Iterable[str]],
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unk_symbol: str = "<unk>",
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):
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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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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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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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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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self.unk_symbol = unk_symbol
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if self.unk_symbol not in self.token2id:
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raise RuntimeError(f"Unknown symbol '{unk_symbol}' doesn't exist in the token_list")
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self.unk_id = self.token2id[self.unk_symbol]
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def get_num_vocabulary_size(self) -> int:
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return len(self.token_list)
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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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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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