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from transformers import PreTrainedTokenizer, AddedToken |
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from typing import List, Optional, Union, Dict, Sequence, Tuple |
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from pathlib import Path |
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import json |
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import os |
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class HyenaDNATokenizer(PreTrainedTokenizer): |
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model_input_names = ["input_ids"] |
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def __init__(self, |
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model_max_length: int, |
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bos_token="[BOS]", |
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eos_token="[SEP]", |
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sep_token="[SEP]", |
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cls_token="[CLS]", |
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pad_token="[PAD]", |
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mask_token="[MASK]", |
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unk_token="[UNK]", |
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**kwargs): |
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"""Character tokenizer for Hugging Face transformers. |
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Args: |
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characters (Sequence[str]): List of desired characters. Any character which |
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is not included in this list will be replaced by a special token called |
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[UNK] with id=6. Following are list of all of the special tokens with |
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their corresponding ids: |
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"[CLS]": 0 |
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"[SEP]": 1 |
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"[BOS]": 2 |
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"[MASK]": 3 |
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"[PAD]": 4 |
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"[RESERVED]": 5 |
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"[UNK]": 6 |
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an id (starting at 7) will be assigned to each character. |
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model_max_length (int): Model maximum sequence length. |
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""" |
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self.characters = ('A', 'C', 'G', 'T', 'N') |
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self.model_max_length = model_max_length |
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self._vocab_str_to_int = { |
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"[CLS]": 0, |
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"[SEP]": 1, |
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"[BOS]": 2, |
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"[MASK]": 3, |
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"[PAD]": 4, |
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"[RESERVED]": 5, |
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"[UNK]": 6, |
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**{ch: i + 7 for i, ch in enumerate(self.characters)}, |
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} |
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self._vocab_int_to_str = {v: k for k, v in self._vocab_str_to_int.items()} |
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add_prefix_space = kwargs.pop("add_prefix_space", False) |
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padding_side = kwargs.pop("padding_side", "left") |
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super().__init__( |
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bos_token=bos_token, |
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eos_token=eos_token, |
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sep_token=sep_token, |
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cls_token=cls_token, |
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pad_token=pad_token, |
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mask_token=mask_token, |
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unk_token=unk_token, |
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add_prefix_space=add_prefix_space, |
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model_max_length=model_max_length, |
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padding_side=padding_side, |
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**kwargs, |
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) |
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@property |
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def vocab_size(self) -> int: |
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return len(self._vocab_str_to_int) |
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def _tokenize(self, text: str) -> List[str]: |
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return list(text) |
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def _convert_token_to_id(self, token: str) -> int: |
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return self._vocab_str_to_int.get(token, self._vocab_str_to_int["[UNK]"]) |
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def _convert_id_to_token(self, index: int) -> str: |
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return self._vocab_int_to_str[index] |
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def convert_tokens_to_string(self, tokens): |
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return "".join(tokens) |
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def get_special_tokens_mask( |
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self, |
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token_ids_0: List[int], |
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token_ids_1: Optional[List[int]] = None, |
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already_has_special_tokens: bool = False, |
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) -> List[int]: |
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if already_has_special_tokens: |
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return super().get_special_tokens_mask( |
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token_ids_0=token_ids_0, |
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token_ids_1=token_ids_1, |
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already_has_special_tokens=True, |
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) |
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result = ([0] * len(token_ids_0)) + [1] |
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if token_ids_1 is not None: |
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result += ([0] * len(token_ids_1)) + [1] |
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return result |
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def build_inputs_with_special_tokens( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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sep = [self.sep_token_id] |
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result = token_ids_0 + sep |
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if token_ids_1 is not None: |
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result += token_ids_1 + sep |
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return result |
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def get_vocab(self) -> Dict[str, int]: |
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return self._vocab_str_to_int |
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple: |
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return () |
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