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5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb b77d27f 5bde0cb | 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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | """NucEL k-mer tokenizer.
This is the single source of truth for the NucEL tokenizer. It is identical to
the ``tokenizer.py`` shipped with ``FreakingPotato/NucEL`` on the Hugging Face
Hub. The vocabulary follows the layout reported in the paper (Section 3.3):
7 special tokens + 4 nucleotides + 4**k k-mers + 16 reserved tokens
For the published checkpoint, ``k = 1`` so the vocabulary size is::
7 + 4 + 4 + 16 = 31 (vocab indices 0..30, model ``vocab_size`` is 27 +
padding to the next embedding row).
The discriminator's ``vocab_size`` in ``config.json`` is 27; the 4 trailing
reserved slots exist on the tokenizer side but are not addressed by the model
weights. They are reserved for future extensions to the alphabet.
"""
from itertools import product
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
import json
import os
from transformers import PreTrainedTokenizer
class NucEL_Tokenizer(PreTrainedTokenizer):
"""k-mer tokenizer for DNA sequences used by NucEL."""
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
k: int = 1,
model_max_length: int = 2048,
pad_token: str = "[PAD]",
unk_token: str = "[UNK]",
sep_token: str = "[SEP]",
cls_token: str = "[CLS]",
mask_token: str = "[MASK]",
bos_token: str = "[BOS]",
eos_token: str = "[EOS]",
num_reserved_tokens: int = 16,
**kwargs: Any,
) -> None:
self.k = k
self.nucleotides = ["A", "C", "G", "T"]
self.num_reserved_tokens = num_reserved_tokens
self.special_tokens = {
"pad_token": pad_token,
"unk_token": unk_token,
"sep_token": sep_token,
"cls_token": cls_token,
"mask_token": mask_token,
"bos_token": bos_token,
"eos_token": eos_token,
}
self._init_vocabulary()
super().__init__(
model_max_length=model_max_length,
pad_token=pad_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
mask_token=mask_token,
bos_token=bos_token,
eos_token=eos_token,
**kwargs,
)
def _init_vocabulary(self) -> None:
special_tokens = [
self.special_tokens["pad_token"],
self.special_tokens["unk_token"],
self.special_tokens["cls_token"],
self.special_tokens["sep_token"],
self.special_tokens["mask_token"],
self.special_tokens["bos_token"],
self.special_tokens["eos_token"],
]
kmers = ["".join(p) for p in product(self.nucleotides, repeat=self.k)]
reserved = [f"[RESERVED_{i}]" for i in range(self.num_reserved_tokens)]
all_tokens = special_tokens + self.nucleotides + kmers + reserved
self.vocab = {token: idx for idx, token in enumerate(all_tokens)}
self.ids_to_tokens = {idx: token for token, idx in self.vocab.items()}
@property
def vocab_size(self) -> int:
return len(self.vocab)
def get_vocab(self) -> Dict[str, int]:
return self.vocab.copy()
def _tokenize(self, text: str) -> List[str]:
text = text.upper().strip()
tokens = [self.cls_token]
i = 0
while i < len(text):
if i <= len(text) - self.k:
kmer = text[i : i + self.k]
if kmer in self.vocab:
tokens.append(kmer)
i += self.k
continue
if i < len(text):
nt = text[i]
tokens.append(nt if nt in self.nucleotides else self.unk_token)
i += 1
return tokens
def _convert_token_to_id(self, token: str) -> int:
return self.vocab.get(token, self.vocab[self.unk_token])
def _convert_id_to_token(self, index: int) -> str:
return self.ids_to_tokens.get(index, self.unk_token)
def save_vocabulary(
self,
save_directory: str,
filename_prefix: Optional[str] = None,
) -> Tuple[str]:
prefix = filename_prefix or "vocab"
vocab_file = os.path.join(save_directory, f"{prefix}.json")
with open(vocab_file, "w", encoding="utf-8") as f:
json.dump(self.vocab, f, ensure_ascii=False, indent=2)
return (vocab_file,)
def save_pretrained(
self,
save_directory: str,
legacy_format: bool = True,
filename_prefix: Optional[str] = None,
**kwargs: Any,
):
vocab_files = self.save_vocabulary(save_directory, filename_prefix=filename_prefix)
return super().save_pretrained(
save_directory,
legacy_format=legacy_format,
**kwargs,
) or vocab_files
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
*init_inputs: Any,
**kwargs: Any,
) -> "NucEL_Tokenizer":
path = Path(pretrained_model_name_or_path)
if not path.is_dir():
from huggingface_hub import snapshot_download
local_dir = snapshot_download(
repo_id=str(pretrained_model_name_or_path),
allow_patterns=[
"tokenizer_config.json",
"vocab.json",
"special_tokens_map.json",
"tokenizer.py",
],
)
path = Path(local_dir)
with open(path / "tokenizer_config.json", "r", encoding="utf-8") as f:
config = json.load(f)
with open(path / "vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokenizer = cls(
k=config.get("k", 1),
model_max_length=config.get("model_max_length", 2048),
pad_token=config.get("pad_token", "[PAD]"),
unk_token=config.get("unk_token", "[UNK]"),
sep_token=config.get("sep_token", "[SEP]"),
cls_token=config.get("cls_token", "[CLS]"),
mask_token=config.get("mask_token", "[MASK]"),
bos_token=config.get("bos_token", "[BOS]"),
eos_token=config.get("eos_token", "[EOS]"),
**kwargs,
)
tokenizer.vocab = vocab
tokenizer.ids_to_tokens = {idx: token for token, idx in vocab.items()}
return tokenizer
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