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00e6e55 d0628b6 00e6e55 d0628b6 00e6e55 d0628b6 00e6e55 d0628b6 00e6e55 d0628b6 00e6e55 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 | import json
import os
from typing import Dict, List, Optional, Union
import torch
from transformers import PreTrainedTokenizer
from transformers.tokenization_utils_base import BatchEncoding
_VOCAB = {
"<cls>": 0,
"<pad>": 1,
"<eos>": 2,
"<unk>": 3,
"A": 4,
"G": 5,
"C": 6,
"U": 7,
"X": 8,
"N": 9,
"-": 10,
"<mask>": 11,
}
class RNAMSMTokenizer(PreTrainedTokenizer):
"""
Tokenizer for RNA-MSM.
Vocabulary: <cls>(0) <pad>(1) <eos>(2) <unk>(3) A(4) G(5) C(6) U(7) X(8) N(9) -(10) <mask>(11)
RNA-MSM is an MSA Transformer: it always expects 3D input
(batch, num_alignments, seqlen). This tokenizer treats each input string
as a single-sequence MSA (1 alignment row), so the standard __call__ API:
enc = tokenizer(["AGCU", "GAUC"], return_tensors="pt", padding=True)
# enc.input_ids: (2, 1, T) -- batch of 2 single-sequence MSAs
For real MSAs (multiple aligned sequences), use encode_msa():
enc = tokenizer.encode_msa([["AGCU--", "AGCUUU"]], return_tensors="pt")
# enc["input_ids"]: (1, 2, T) -- 1 MSA with 2 alignment rows
"""
vocab_files_names = {"vocab_file": "vocab.json"}
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file=None,
cls_token="<cls>",
pad_token="<pad>",
eos_token="<eos>",
unk_token="<unk>",
mask_token="<mask>",
**kwargs,
):
if vocab_file and os.path.isfile(vocab_file):
with open(vocab_file) as f:
self._vocab = json.load(f)
else:
self._vocab = dict(_VOCAB)
self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
super().__init__(
cls_token=cls_token,
pad_token=pad_token,
eos_token=eos_token,
unk_token=unk_token,
mask_token=mask_token,
**kwargs,
)
@property
def vocab_size(self):
return len(self._vocab)
def get_vocab(self):
return dict(self._vocab)
def _tokenize(self, text):
return list(text)
def _convert_token_to_id(self, token):
return self._vocab.get(token, self._vocab["<unk>"])
def _convert_id_to_token(self, index):
return self._ids_to_tokens.get(index, "<unk>")
def save_vocabulary(self, save_directory, filename_prefix=None):
os.makedirs(save_directory, exist_ok=True)
fname = (filename_prefix + "-" if filename_prefix else "") + "vocab.json"
path = os.path.join(save_directory, fname)
with open(path, "w") as f:
json.dump(self._vocab, f, indent=2)
return (path,)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
cls = [self.cls_token_id]
if token_ids_1 is None:
return cls + token_ids_0
return cls + token_ids_0 + cls + token_ids_1
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None,
already_has_special_tokens=False):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0, token_ids_1, already_has_special_tokens=True)
mask = [1] + [0] * len(token_ids_0)
if token_ids_1 is not None:
mask += [1] + [0] * len(token_ids_1)
return mask
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
if token_ids_1 is None:
return [0] + token_ids_0
return [0] + token_ids_0 + [0] + token_ids_1
def __call__(
self,
text,
text_pair=None,
add_special_tokens=True,
padding=False,
truncation=False,
max_length=None,
return_tensors=None,
**kwargs,
):
"""
Tokenize one or more sequences, each treated as a 1-row MSA.
text: str or List[str]
Returns dict with input_ids of shape (batch, 1, seqlen) and
attention_mask of shape (batch, 1, seqlen).
"""
if isinstance(text, str):
sequences = [text]
else:
sequences = list(text)
encoded = []
for seq in sequences:
ids = self._tokenize_single(seq, add_special_tokens)
encoded.append(ids)
if padding and len(encoded) > 1:
max_len = max(len(ids) for ids in encoded)
pad_id = self.pad_token_id
encoded = [ids + [pad_id] * (max_len - len(ids)) for ids in encoded]
input_ids = [[ids] for ids in encoded]
attention_mask = [[[1 if t != self.pad_token_id else 0 for t in ids]]
for ids in encoded]
if return_tensors == "pt":
input_ids = torch.tensor(input_ids, dtype=torch.long)
attention_mask = torch.tensor(attention_mask, dtype=torch.long)
return BatchEncoding({"input_ids": input_ids, "attention_mask": attention_mask})
return BatchEncoding({"input_ids": input_ids, "attention_mask": attention_mask})
def _tokenize_single(self, sequence, add_special_tokens=True):
tokens = list(sequence)
ids = [self._convert_token_to_id(t) for t in tokens]
if add_special_tokens:
ids = [self.cls_token_id] + ids
return ids
def encode_msa(
self,
msas,
add_special_tokens=True,
padding=False,
return_tensors=None,
):
"""
Tokenize a batch of MSAs.
msas: List[List[str]]
Each inner list is one MSA (multiple aligned sequences of equal length).
All sequences within an MSA must have the same length.
Returns dict with:
input_ids: (batch, max_alignments, max_seqlen)
attention_mask: (batch, max_alignments, max_seqlen)
"""
if isinstance(msas[0], str):
msas = [msas]
max_rows = max(len(msa) for msa in msas)
max_seqlen = max(
len(self._tokenize_single(seq, add_special_tokens))
for msa in msas for seq in msa
)
pad_id = self.pad_token_id
batch_ids = []
batch_mask = []
for msa in msas:
msa_ids = []
msa_mask = []
for seq in msa:
ids = self._tokenize_single(seq, add_special_tokens)
if padding:
pad_len = max_seqlen - len(ids)
mask = [1] * len(ids) + [0] * pad_len
ids = ids + [pad_id] * pad_len
else:
mask = [1] * len(ids)
msa_ids.append(ids)
msa_mask.append(mask)
if padding:
pad_row = [pad_id] * max_seqlen
pad_mask_row = [0] * max_seqlen
while len(msa_ids) < max_rows:
msa_ids.append(pad_row)
msa_mask.append(pad_mask_row)
batch_ids.append(msa_ids)
batch_mask.append(msa_mask)
if return_tensors == "pt":
batch_ids = torch.tensor(batch_ids, dtype=torch.long)
batch_mask = torch.tensor(batch_mask, dtype=torch.long)
return BatchEncoding({"input_ids": batch_ids, "attention_mask": batch_mask})
return BatchEncoding({"input_ids": batch_ids, "attention_mask": batch_mask})
def decode(self, token_ids, skip_special_tokens=False, **kwargs):
if isinstance(token_ids, torch.Tensor):
token_ids = token_ids.tolist()
tokens = [self._convert_id_to_token(i) for i in token_ids]
if skip_special_tokens:
special = {self.cls_token, self.pad_token, self.eos_token,
self.unk_token, self.mask_token}
tokens = [t for t in tokens if t not in special]
return "".join(tokens)
def num_special_tokens_to_add(self, pair=False):
return 1
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