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from difflib import Differ
import torch, re
class PllScore:
def __init__(
self,
language_model # LanguageModel class instance
) -> None:
self.tokenizer = language_model.initTokenizer()
self.model = language_model.initModel()
_ = self.model.eval()
self.logSoftmax = torch.nn.LogSoftmax(dim=-1)
def sentIsCorrect(
self,
sent: str
) -> bool:
# Mod
is_correct = True
# Check mark existence
open_mark = sent.count("<")
close_mark = sent.count(">")
total_mark = open_mark + close_mark
if (total_mark == 0) or (open_mark != close_mark):
is_correct = False
# Check existence of twin marks (ie: '<<' or '>>')
if is_correct:
left_twin = sent.count("<<")
rigth_twin = sent.count(">>")
if left_twin + rigth_twin > 0:
is_correct = False
if is_correct:
# Check balanced symbols '<' and '>'
stack = []
for c in sent:
if c == '<':
stack.append('<')
elif c == '>':
if len(stack) == 0:
is_correct = False
break
if stack.pop() != "<":
is_correct = False
break
if len(stack) > 0:
is_correct = False
if is_correct:
for w in re.findall("\<.*?\>", sent):
# Check empty interest words
word = w.replace("<","").replace(">","").strip()
if not word:
is_correct = False
break
# Check if there are any marks inside others (ie: <this is a <sentence>>)
word = w.strip()[1:-1] #Delete the first and last mark
if '<' in word or '>' in word:
is_correct = False
break
if is_correct:
# Check that there is at least one uninteresting word. The next examples should not be allowed
# (ie: <this is a sent>, <this> <is a sent>)
outside_words = re.sub("\<.*?\>", "", sent.replace("<", " < ").replace(">", " > "))
outside_words = [w for w in outside_words.split() if w != ""]
if not outside_words:
is_correct = False
return is_correct
def compute(
self,
sent: str
) -> float:
assert(self.sentIsCorrect(sent)), f"Error: The sentence '{sent}' does not have the correct format!"
outside_words = re.sub("\<.*?\>", "", sent.replace("<", " < ").replace(">", " > "))
outside_words = [w for w in outside_words.split() if w != ""]
all_words = [w.strip() for w in sent.replace("<"," ").replace(">"," ").split() if w != ""]
tks_id_outside_words = self.tokenizer.encode(
" ".join(outside_words),
add_special_tokens=False,
truncation=True
)
tks_id_all_words = self.tokenizer.encode(
" ".join(all_words),
add_special_tokens=False,
truncation=True
)
diff = [(tk[0], tk[2:]) for tk in Differ().compare(tks_id_outside_words, tks_id_all_words)]
cls_tk_id = self.tokenizer.cls_token_id
sep_tk_id = self.tokenizer.sep_token_id
mask_tk_id = self.tokenizer.mask_token_id
all_sent_masked = []
all_tks_id_masked = []
all_tks_position_masked = []
for i in range(0, len(diff)):
current_sent_masked = [cls_tk_id]
add_sent = True
for j, (mark, tk_id) in enumerate(diff):
if j == i:
if mark == '+':
add_sent = False
break
else:
current_sent_masked.append(mask_tk_id)
all_tks_id_masked.append(int(tk_id))
all_tks_position_masked.append(i+1)
else:
current_sent_masked.append(int(tk_id))
if add_sent:
current_sent_masked.append(sep_tk_id)
all_sent_masked.append(current_sent_masked)
inputs_ids = torch.tensor(all_sent_masked)
attention_mask = torch.ones_like(inputs_ids)
with torch.no_grad():
out = self.model(inputs_ids, attention_mask)
logits = out.logits
outputs = self.logSoftmax(logits)
pll_score = 0
for out, tk_pos, tk_id in zip(outputs, all_tks_position_masked, all_tks_id_masked):
probabilities = out[tk_pos]
tk_prob = probabilities[tk_id]
pll_score += tk_prob.item()
return pll_score |