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from typing import List, Tuple
from collections import Counter
import torch
from tqdm import tqdm
import os
from transformers import PreTrainedTokenizer
class GECToRDataset:
def __init__(
self,
srcs: List[str],
d_labels: List[List[int]]=None,
labels: List[List[int]]=None,
word_masks: List[List[int]]=None,
tokenizer: PreTrainedTokenizer=None,
max_length:int=128
):
self.tokenizer = tokenizer
self.srcs = srcs
self.d_labels = d_labels
self.labels = labels
self.word_masks = word_masks
self.max_length = max_length
self.label2id = None
self.d_label2id = None
def __len__(self):
return len(self.srcs)
def __getitem__(self, idx):
src = self.srcs[idx]
d_labels = self.d_labels[idx]
labels = self.labels[idx]
wmask = self.word_masks[idx]
encode = self.tokenizer(
src,
return_tensors='pt',
max_length=self.max_length,
padding='max_length',
truncation=True,
is_split_into_words=True
)
return {
'input_ids': encode['input_ids'].squeeze(),
'attention_mask': encode['attention_mask'].squeeze(),
'd_labels': torch.tensor(d_labels).squeeze(),
'labels': torch.tensor(labels).squeeze(),
'word_masks': torch.tensor(wmask).squeeze()
}
def append_vocab(self, label2id, d_label2id):
self.label2id = label2id
self.d_label2id = d_label2id
for i in range(len(self.labels)):
self.labels[i] = [self.label2id.get(l, self.label2id['<OOV>']) for l in self.labels[i]]
self.d_labels[i] = [self.d_label2id[l] for l in self.d_labels[i]]
def get_labels_freq(self, exluded_labels: List[str] = []):
assert(self.labels is not None and self.d_labels is not None)
flatten_labels = [ll for l in self.labels for ll in l if ll not in exluded_labels]
flatten_d_labels = [ll for l in self.d_labels for ll in l if ll not in exluded_labels]
return Counter(flatten_labels), Counter(flatten_d_labels)
def align_labels_to_subwords(
srcs: List[str],
word_labels: List[List[str]],
tokenizer: PreTrainedTokenizer,
batch_size: int=100000,
max_length: int=128,
keep_label: str='$KEEP',
pad_token: str='<PAD>',
correct_label: str='$CORRECT',
incorrect_label: str='$INCORRECT'
):
itr = list(range(0, len(srcs), batch_size))
subword_labels = []
subword_d_labels = []
word_masks = []
for i in tqdm(itr):
encode = tokenizer(
srcs[i:i+batch_size],
max_length=max_length,
return_tensors='pt',
padding='max_length',
truncation=True,
is_split_into_words=True
)
for i, wlabels in enumerate(word_labels[i:i+batch_size]):
d_labels = []
labels = []
wmask = []
word_ids = encode.word_ids(i)
previous_word_idx = None
for word_idx in word_ids:
if word_idx is None:
labels.append(pad_token)
d_labels.append(pad_token)
wmask.append(0)
elif word_idx != previous_word_idx:
l = wlabels[word_idx]
labels.append(l)
wmask.append(1)
if l != keep_label:
d_labels.append(incorrect_label)
else:
d_labels.append(correct_label)
else:
labels.append(pad_token)
d_labels.append(pad_token)
wmask.append(0)
previous_word_idx = word_idx
subword_d_labels.append(d_labels)
subword_labels.append(labels)
word_masks.append(wmask)
return subword_d_labels, subword_labels, word_masks
def load_gector_format(
input_file: str,
delimeter: str='SEPL|||SEPR',
additional_delimeter: str='SEPL__SEPR'
):
srcs = []
word_level_labels = [] # the size will be (#sents, seq_length) if not get_interactive_tags,
# (#iteration, #sents, seq_length) if get_interactive_tags
with open(input_file) as f:
for line in f:
src = [x.split(delimeter)[0] for x in line.split()]
labels = [x.split(delimeter)[1] for x in line.split()]
# Use only first tags. E.g. $REPLACE_meSEPL__SEPR$APPEND_too → $REPLACE_me
labels = [l.split(additional_delimeter)[0] for l in labels]
srcs.append(src)
word_level_labels.append(labels)
return srcs, word_level_labels
def load_dataset(
input_file: str,
tokenizer: PreTrainedTokenizer,
delimeter: str='SEPL|||SEPR',
additional_delimeter: str='SEPL__SEPR',
batch_size: int=50000, # avoid too heavy computation in the tokenization
max_length: int=128
):
srcs, word_level_labels = load_gector_format(
input_file,
delimeter=delimeter,
additional_delimeter=additional_delimeter
)
d_labels, labels, word_masks = align_labels_to_subwords(
srcs,
word_level_labels,
tokenizer=tokenizer,
batch_size=batch_size,
max_length=max_length
)
return GECToRDataset(
srcs=srcs,
d_labels=d_labels,
labels=labels,
word_masks=word_masks,
tokenizer=tokenizer,
max_length=max_length
)
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