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import os
import torch
import random
import selfies as sf
from transformers import AutoTokenizer
################################
def getrandomnumber(numbers, k, weights=None):
if k == 1:
return random.choices(numbers, weights=weights, k=k)[0]
else:
return random.choices(numbers, weights=weights, k=k)
# simple smiles tokenizer
# treat every charater as token
def build_simple_smiles_vocab(dir):
assert dir is not None, "dir and smiles_vocab can not be None at the same time."
if not os.path.exists(os.path.join(dir, "simple_smiles_tokenizer_vocab.txt")):
# print('Generating Vocabulary for {} ...'.format(dir))
dirs = list(
os.path.join(dir, i) for i in ["train.txt", "validation.txt", "test.txt"]
)
smiles = []
for idir in dirs:
with open(idir, "r") as f:
for i, line in enumerate(f):
if i == 0:
continue
line = line.split("\t")
assert len(line) == 3, "Dataset format error."
if line[1] != "*":
smiles.append(line[1].strip())
char_set = set()
for smi in smiles:
for c in smi:
char_set.add(c)
vocabstring = "".join(char_set)
with open(os.path.join(dir, "simple_smiles_tokenizer_vocab.txt"), "w") as f:
f.write(os.path.join(vocabstring))
return vocabstring
else:
print("Reading in Vocabulary...")
with open(os.path.join(dir, "simple_smiles_tokenizer_vocab.txt"), "r") as f:
vocabstring = f.readline().strip()
return vocabstring
class Tokenizer:
def __init__(
self,
pretrained_name="QizhiPei/biot5-base-text2mol",
selfies_dict_path=os.path.join("dataset", "selfies_dict.txt"),
):
self.tokenizer = self.get_tokenizer(pretrained_name, selfies_dict_path)
def get_tokenizer(self, pretrained_name, selfies_dict_path):
tokenizer = AutoTokenizer.from_pretrained(pretrained_name, use_fast=True)
tokenizer.model_max_length = int(1e9)
amino_acids = [
"A",
"C",
"D",
"E",
"F",
"G",
"H",
"I",
"K",
"L",
"M",
"N",
"P",
"Q",
"R",
"S",
"T",
"V",
"W",
"Y",
]
prefixed_amino_acids = [f"<p>{aa}" for aa in amino_acids]
tokenizer.add_tokens(prefixed_amino_acids)
selfies_dict_list = [line.strip() for line in open(selfies_dict_path)]
tokenizer.add_tokens(selfies_dict_list)
special_tokens_dict = {
"additional_special_tokens": [
"<bom>",
"<eom>",
"<bop>",
"<eop>",
"MOLECULE NAME",
"DESCRIPTION",
"PROTEIN NAME",
"FUNCTION",
"SUBCELLULAR LOCATION",
"PROTEIN FAMILIES",
]
}
tokenizer.add_special_tokens(special_tokens_dict)
return tokenizer
def __call__(self, *args, **kwds):
return self.tokenizer(*args, **kwds)
def __len__(self):
return len(self.tokenizer)
def corrupt(self, selfies_list: list):
tensors = []
if type(selfies_list) is str:
selfies_list = [selfies_list]
for selfies in selfies_list:
tensors.append(self.corrupt_one(selfies))
return torch.concat(tensors, dim=0)
# TODO: rewrite this for selfies
def corrupt_one(self, selfies):
smi = sf.decoder(selfies)
# res = [self.toktoid[i] for i in self.rg.findall(smi)]
res = [i for i in self.rg.findall(smi)]
total_length = len(res) + 2
if total_length > self.max_len:
return self.encode_one(smi)
######################## start corruption ###########################
r = random.random()
if r < 0.3:
pa, ring = True, True
elif r < 0.65:
pa, ring = True, False
else:
pa, ring = False, True
#########################
max_ring_num = 1
ringpos = []
papos = []
for pos, at in enumerate(res):
if at == "(" or at == ")":
papos.append(pos)
elif at.isnumeric():
max_ring_num = max(max_ring_num, int(at))
ringpos.append(pos)
# ( & ) remove
r = random.random()
if r < 0.3:
remove, padd = True, True
elif r < 0.65:
remove, padd = True, False
else:
remove, padd = False, True
if pa and len(papos) > 0:
if remove:
# remove pa
n_remove = getrandomnumber(
[1, 2, 3, 4], 1, weights=[0.6, 0.2, 0.1, 0.1]
)
p_remove = set(random.choices(papos, weights=None, k=n_remove))
total_length -= len(p_remove)
for p in p_remove:
res[p] = None
# print('debug pa delete {}'.format(p))
# Ring remove
r = random.random()
if r < 0.3:
remove, radd = True, True
elif r < 0.65:
remove, radd = True, False
else:
remove, radd = False, True
if ring and len(ringpos) > 0:
if remove:
# remove ring
n_remove = getrandomnumber(
[1, 2, 3, 4], 1, weights=[0.7, 0.2, 0.05, 0.05]
)
p_remove = set(random.choices(ringpos, weights=None, k=n_remove))
total_length -= len(p_remove)
for p in p_remove:
res[p] = None
# print('debug ring delete {}'.format(p))
# ring add & ( ) add
if pa:
if padd:
n_add = getrandomnumber([1, 2, 3], 1, weights=[0.8, 0.2, 0.1])
n_add = min(self.max_len - total_length, n_add)
for _ in range(n_add):
sele = random.randrange(len(res) + 1)
res.insert(sele, "(" if random.random() < 0.5 else ")")
# print('debug pa add {}'.format(sele))
total_length += 1
if ring:
if radd:
n_add = getrandomnumber([1, 2, 3], 1, weights=[0.8, 0.2, 0.1])
n_add = min(self.max_len - total_length, n_add)
for _ in range(n_add):
sele = random.randrange(len(res) + 1)
res.insert(sele, str(random.randrange(1, max_ring_num + 1)))
# print('debug ring add {}'.format(sele))
total_length += 1
########################## end corruption ###############################
# print('test:',res)
# print('test:',''.join([i for i in res if i is not None]))
res = [self.toktoid[i] for i in res if i is not None]
res = [1] + res + [2]
if len(res) < self.max_len:
res += [0] * (self.max_len - len(res))
else:
res = res[: self.max_len]
res[-1] = 2
return torch.LongTensor([res])
def decode_one(self, sample):
return self.tokenizer.decode(sample)
def decode(self, sample_list):
if len(sample_list.shape)==1:
return [self.decode_one(sample_list)]
return [self.decode_one(sample) for sample in sample_list]
if __name__ == "__main__":
import selfies as sf
tokenizer = Tokenizer(
selfies_dict_path=r"D:\molecule\mol-lang-bridge\dataset\selfies_dict.txt"
)
smiles = [
"[210Po]",
"C[C@H]1C(=O)[C@H]([C@H]([C@H](O1)OP(=O)(O)OP(=O)(O)OC[C@@H]2[C@H](C[C@@H](O2)N3C=C(C(=O)NC3=O)C)O)O)O",
"C(O)P(=O)(O)[O-]",
"CCCCCCCCCCCC(=O)OC(=O)CCCCCCCCCCC",
"C[C@]12CC[C@H](C[C@H]1CC[C@@H]3[C@@H]2CC[C@]4([C@H]3CCC4=O)C)O[C@H]5[C@@H]([C@H]([C@@H]([C@H](O5)C(=O)O)O)O)O",
]
selfies = [sf.encoder(smiles_ele) for smiles_ele in smiles]
output = tokenizer(
selfies,
max_length=512,
truncation=True,
padding="max_length",
add_special_tokens=True,
return_tensors="pt",
return_attention_mask=True,
)
print(output["input_ids"])
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