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from torch.utils.data.dataset import Dataset
from transformers.tokenization_utils import PreTrainedTokenizer
from tqdm import tqdm
import json
import random
import itertools
random.seed(3435)
import torch
from dataclasses import dataclass
label_mapping = json.load(open("data/preprocessed_data/bart_parser_label_mapping_2.json"))
def pad_and_tensorize_sequence(sequences, padding_value):
max_size = max([len(sequence) for sequence in sequences])
padded_sequences = []
for sequence in sequences:
padded_sequence = sequence + [padding_value] * (max_size - len(sequence))
padded_sequences.append(padded_sequence)
return torch.tensor(padded_sequences, dtype=torch.long)
class ColumnInferringDataset(Dataset):
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, local_rank=-1):
self.examples = []
with open(file_path, encoding="utf-8") as f:
for line in tqdm(f):
example = json.loads(line)
# header = example["header"]
# table_value = example["table"]
# table_name = example["table_name"]
# column_type = example["column_type"]
# caption = example["caption"]
self.examples.append(example)
def __len__(self):
return len(self.examples)
def __getitem__(self, i):
return self.examples[i]
@dataclass
class DataCollatorForColumnInferring:
tokenizer: PreTrainedTokenizer
keywords = label_mapping["keyword"]
label_padding_id = label_mapping["keyword"].index(label_mapping["label_padding_token"])
label_eos_id = label_mapping["keyword"].index(label_mapping["label_eos_token"])
label_bos_id = label_mapping["keyword"].index(label_mapping["label_bos_token"])
def collate_batch(self, examples):
training_examples = self.create_example(examples)
training_tensors = self.create_tensor(training_examples)
batched_training_examples = self.create_batch(training_tensors)
return batched_training_examples
def create_batch(self, examples):
input_ids_sequences = [example["input_ids"] for example in examples]
column_spans_sequences = [example["column_spans"] for example in examples]
label_ids_sequences = [example["output_ids"] for example in examples]
copy_span = [example["copy_span"] for example in examples]
padded_input_ids_tensor = pad_and_tensorize_sequence(
input_ids_sequences, padding_value=self.tokenizer.pad_token_id)
padded_column_spans_tensor = pad_and_tensorize_sequence(
column_spans_sequences, padding_value=(0, 1))
label_ids_tensor = pad_and_tensorize_sequence(
label_ids_sequences, padding_value=self.label_padding_id)
return {
"input_ids": padded_input_ids_tensor,
"column_spans": padded_column_spans_tensor,
"copy_span": copy_span,
"labels": label_ids_tensor,
"input_padding_id": self.tokenizer.pad_token_id,
"label_padding_id": self.label_padding_id,
"label_eos_id": self.label_eos_id,
"label_bos_id": self.label_bos_id
}
def create_tensor(self, examples):
processed_examples = []
for example in examples:
input_seq = example["input"]
input_words = [self.tokenizer.tokenize(word, add_prefix_space=True) for word in input_seq]
input_tokens = list(itertools.chain.from_iterable(input_words))
columns = example["columns"]
column_start = len(input_tokens) + 2 # add cls and sep
column_tokens = []
column_spans = []
for column in columns:
column_text = column.replace("_", " ").replace(".", " ")
tokens = self.tokenizer.tokenize(column_text, add_prefix_space=True)
column_tokens.extend(tokens)
column_tokens.append(self.tokenizer.sep_token)
column_spans.append((column_start, column_start + len(tokens)))
column_start += len(tokens) + 1
output_seq = example["output"]
output_ids = []
column_count = -1
# for idx, token in enumerate(output_seq):
# if token in columns:
# output_ids.append(len(self.keywords) + len(input_tokens) + columns.index(token))
# column_count += 1
# elif token in self.keywords:
# output_ids.append(self.keywords.index(token))
# else:
# # It should match the column_count-th value in the input_word_ids
# base = len(list(itertools.chain.from_iterable(input_words[:column_count]))) + len(self.keywords)
# for i in range(len(input_words[column_count])):
# output_ids.append(base + i)
for idx, token in enumerate(output_seq):
if token in columns:
output_ids.append(len(self.keywords) + columns.index(token))
column_count += 1
elif token in self.keywords:
output_ids.append(self.keywords.index(token))
else:
raise NotImplementedError()
output_ids = [self.label_bos_id] + output_ids + [self.label_eos_id]
input_ids = self.tokenizer.convert_tokens_to_ids(
[self.tokenizer.cls_token] + input_tokens + [self.tokenizer.sep_token] + column_tokens)
if len(input_ids) > 800:
continue
processed_examples.append(
{
"input_ids": input_ids,
"output_ids": output_ids,
"column_spans": column_spans,
"copy_span": (1, len(input_tokens) + 1),
"input_tokens": [self.tokenizer.cls_token] + input_tokens + [self.tokenizer.sep_token] + column_tokens,
})
return processed_examples
def create_example(self, examples):
# We will mix several examples together.
training_examples = []
batch_start = 0
while batch_start < len(examples):
samples = examples[batch_start:batch_start + 2]
columns = list(itertools.chain.from_iterable([sample["header"] for sample in samples]))
augmented_columns = list(itertools.chain.from_iterable([sample["augmented_columns"] for sample in samples]))
column_idx_to_sample_ids = list(
itertools.chain.from_iterable([[idx] * len(sample["header"]) for idx, sample in enumerate(samples)]))
column_idx_to_column_seq = list(
itertools.chain.from_iterable(list(range(len(sample["header"]))) for sample in samples))
column_size = len(columns)
selected_columns = random.sample(range(column_size), int(column_size * 0.4))
batch_start += 3
processed_example = []
column_candidates = []
for column_idx in range(column_size):
sample_idx = column_idx_to_sample_ids[column_idx]
table_name = samples[sample_idx]["table_name"]
column_candidates.append("{}.{}".format(table_name.lower().replace(" ", "_"),
columns[column_idx].lower().replace(" ", "_")))
if column_idx in selected_columns:
if random.random() < 0.3:
column_text = augmented_columns[column_idx].lower()
else:
column_text = columns[column_idx].lower()
processed_example.append(("column", column_text, table_name, columns[column_idx]))
else:
column_seq = column_idx_to_column_seq[column_idx]
row_size = len(samples[sample_idx]["table"])
value = ""
try_count = 0
while not (len(value) > 0 and value != "-"):
selected_row = random.choice(range(row_size))
value = samples[sample_idx]["table"][selected_row][column_seq]
if try_count > 5:
break
try_count += 1
if len(value) > 0 and value != "-":
processed_example.append(("value", value, table_name, columns[column_idx]))
random.shuffle(processed_example)
input_sequence, output_sequence = [], []
for item in processed_example:
if item[0] == "column":
input_sequence.append(item[1])
output_sequence.append(item[2].lower().replace(" ", "_") + "." + item[3].lower().replace(" ", "_"))
else:
input_sequence.append(item[1])
output_sequence.append(item[2].lower().replace(" ", "_") + "." + item[3].lower().replace(" ", "_"))
# output_sequence.append("=")
# output_sequence.append(item[1])
training_examples.append({
"input": input_sequence,
"output": output_sequence,
"columns": column_candidates
})
return training_examples
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