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from torch.utils.data import TensorDataset |
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import numpy as np |
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import logging |
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import os |
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import random |
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import torch |
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import time |
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from tqdm import tqdm |
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from _utils import * |
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logger = logging.getLogger(__name__) |
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def load_and_cache_gen_data(args, filename, pool, tokenizer, split_tag, only_src=False, is_sample=False): |
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data_tag = '_all' if args.data_num == -1 else '_%d' % args.data_num |
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cache_fn = '{}/{}.pt'.format(args.cache_path, split_tag + ('_src' if only_src else '') + data_tag) |
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examples = read_examples(filename, args.data_num, args.task) |
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if is_sample: |
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examples = random.sample(examples, min(5000, len(examples))) |
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if split_tag == 'train': |
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calc_stats(examples, tokenizer, is_tokenize=True) |
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else: |
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calc_stats(examples) |
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if os.path.exists(cache_fn) and not is_sample: |
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logger.info("Load cache data from %s", cache_fn) |
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data = torch.load(cache_fn) |
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else: |
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if is_sample: |
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logger.info("Sample 5k data for computing bleu from %s", filename) |
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else: |
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logger.info("Create cache data into %s", cache_fn) |
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tuple_examples = [(example, idx, tokenizer, args, split_tag) for idx, example in enumerate(examples)] |
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features = pool.map(convert_examples_to_features, tqdm(tuple_examples, total=len(tuple_examples))) |
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all_source_ids = torch.tensor([f.source_ids for f in features], dtype=torch.long) |
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if split_tag == 'test' or only_src: |
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data = TensorDataset(all_source_ids) |
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else: |
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all_target_ids = torch.tensor([f.target_ids for f in features], dtype=torch.long) |
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data = TensorDataset(all_source_ids, all_target_ids) |
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if args.local_rank in [-1, 0] and not is_sample: |
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torch.save(data, cache_fn) |
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return examples, data |
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def load_and_cache_clone_data(args, filename, pool, tokenizer, split_tag, is_sample=False): |
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cache_fn = '{}/{}.pt'.format(args.cache_path, split_tag + '_all' if args.data_num == -1 else '_%d' % args.data_num) |
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examples = read_examples(filename, args.data_num, args.task) |
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if is_sample: |
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examples = random.sample(examples, int(len(examples) * 0.1)) |
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calc_stats(examples, tokenizer, is_tokenize=True) |
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if os.path.exists(cache_fn): |
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logger.info("Load cache data from %s", cache_fn) |
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data = torch.load(cache_fn) |
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else: |
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if is_sample: |
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logger.info("Sample 10 percent of data from %s", filename) |
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elif args.data_num == -1: |
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logger.info("Create cache data into %s", cache_fn) |
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tuple_examples = [(example, idx, tokenizer, args) for idx, example in enumerate(examples)] |
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features = pool.map(convert_clone_examples_to_features, tqdm(tuple_examples, total=len(tuple_examples))) |
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all_source_ids = torch.tensor([f.source_ids for f in features], dtype=torch.long) |
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all_labels = torch.tensor([f.label for f in features], dtype=torch.long) |
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data = TensorDataset(all_source_ids, all_labels) |
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if args.local_rank in [-1, 0] and args.data_num == -1: |
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torch.save(data, cache_fn) |
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return examples, data |
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def load_and_cache_defect_data(args, filename, pool, tokenizer, split_tag, is_sample=False): |
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cache_fn = os.path.join(args.cache_path, split_tag) |
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examples = read_examples(filename, args.data_num, args.task) |
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if is_sample: |
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examples = random.sample(examples, int(len(examples) * 0.1)) |
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calc_stats(examples, tokenizer, is_tokenize=True) |
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if os.path.exists(cache_fn): |
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logger.info("Load cache data from %s", cache_fn) |
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data = torch.load(cache_fn) |
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else: |
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if is_sample: |
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logger.info("Sample 10 percent of data from %s", filename) |
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elif args.data_num == -1: |
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logger.info("Create cache data into %s", cache_fn) |
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tuple_examples = [(example, idx, tokenizer, args) for idx, example in enumerate(examples)] |
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features = pool.map(convert_defect_examples_to_features, tqdm(tuple_examples, total=len(tuple_examples))) |
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all_source_ids = torch.tensor([f.source_ids for f in features], dtype=torch.long) |
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all_labels = torch.tensor([f.label for f in features], dtype=torch.long) |
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data = TensorDataset(all_source_ids, all_labels) |
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if args.local_rank in [-1, 0] and args.data_num == -1: |
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torch.save(data, cache_fn) |
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return examples, data |
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def load_and_cache_multi_gen_data(args, pool, tokenizer, split_tag, only_src=False, is_sample=False): |
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cache_fn = os.path.join(args.cache_path, split_tag) |
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if os.path.exists(cache_fn) and not is_sample: |
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logger.info("Load cache data from %s", cache_fn) |
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examples_data_dict = torch.load(cache_fn) |
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else: |
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examples_data_dict = {} |
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task_list = ['summarize', 'translate', 'refine', 'concode', 'defect'] |
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for task in task_list: |
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if task == 'summarize': |
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sub_tasks = ['ruby', 'javascript', 'go', 'python', 'java', 'php'] |
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elif task == 'translate': |
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sub_tasks = ['java-cs', 'cs-java'] |
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elif task == 'refine': |
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sub_tasks = ['small', 'medium'] |
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else: |
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sub_tasks = ['none'] |
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args.task = task |
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for sub_task in sub_tasks: |
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args.sub_task = sub_task |
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if task == 'summarize': |
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args.max_source_length = 256 |
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args.max_target_length = 128 |
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elif task == 'translate': |
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args.max_source_length = 320 |
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args.max_target_length = 256 |
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elif task == 'refine': |
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if sub_task == 'small': |
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args.max_source_length = 130 |
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args.max_target_length = 120 |
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else: |
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args.max_source_length = 240 |
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args.max_target_length = 240 |
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elif task == 'concode': |
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args.max_source_length = 320 |
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args.max_target_length = 150 |
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elif task == 'defect': |
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args.max_source_length = 512 |
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args.max_target_length = 3 |
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filename = get_filenames(args.data_dir, args.task, args.sub_task, split_tag) |
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examples = read_examples(filename, args.data_num, args.task) |
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if is_sample: |
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examples = random.sample(examples, min(5000, len(examples))) |
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if split_tag == 'train': |
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calc_stats(examples, tokenizer, is_tokenize=True) |
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else: |
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calc_stats(examples) |
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tuple_examples = [(example, idx, tokenizer, args, split_tag) for idx, example in enumerate(examples)] |
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if args.data_num == -1: |
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features = pool.map(convert_examples_to_features, tqdm(tuple_examples, total=len(tuple_examples))) |
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else: |
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features = [convert_examples_to_features(x) for x in tuple_examples] |
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all_source_ids = torch.tensor([f.source_ids for f in features], dtype=torch.long) |
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if only_src: |
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data = TensorDataset(all_source_ids) |
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else: |
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all_target_ids = torch.tensor([f.target_ids for f in features], dtype=torch.long) |
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data = TensorDataset(all_source_ids, all_target_ids) |
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examples_data_dict['{}_{}'.format(task, sub_task) if sub_task != 'none' else task] = (examples, data) |
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if args.local_rank in [-1, 0] and not is_sample: |
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torch.save(examples_data_dict, cache_fn) |
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logger.info("Save data into %s", cache_fn) |
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return examples_data_dict |
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def get_filenames(data_root, task, sub_task, split=''): |
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if task == 'concode': |
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data_dir = '{}/{}'.format(data_root, task) |
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train_fn = '{}/train.json'.format(data_dir) |
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dev_fn = '{}/dev.json'.format(data_dir) |
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test_fn = '{}/test.json'.format(data_dir) |
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elif task == 'summarize': |
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data_dir = '{}/{}/{}'.format(data_root, task, sub_task) |
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train_fn = '{}/train.jsonl'.format(data_dir) |
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dev_fn = '{}/valid.jsonl'.format(data_dir) |
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test_fn = '{}/test.jsonl'.format(data_dir) |
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elif task == 'refine': |
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data_dir = '{}/{}/{}'.format(data_root, task, sub_task) |
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train_fn = '{}/train.buggy-fixed.buggy,{}/train.buggy-fixed.fixed'.format(data_dir, data_dir) |
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dev_fn = '{}/valid.buggy-fixed.buggy,{}/valid.buggy-fixed.fixed'.format(data_dir, data_dir) |
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test_fn = '{}/test.buggy-fixed.buggy,{}/test.buggy-fixed.fixed'.format(data_dir, data_dir) |
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elif task == 'translate': |
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data_dir = '{}/{}'.format(data_root, task) |
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if sub_task == 'cs-java': |
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train_fn = '{}/train.java-cs.txt.cs,{}/train.java-cs.txt.java'.format(data_dir, data_dir) |
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dev_fn = '{}/valid.java-cs.txt.cs,{}/valid.java-cs.txt.java'.format(data_dir, data_dir) |
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test_fn = '{}/test.java-cs.txt.cs,{}/test.java-cs.txt.java'.format(data_dir, data_dir) |
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else: |
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train_fn = '{}/train.java-cs.txt.java,{}/train.java-cs.txt.cs'.format(data_dir, data_dir) |
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dev_fn = '{}/valid.java-cs.txt.java,{}/valid.java-cs.txt.cs'.format(data_dir, data_dir) |
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test_fn = '{}/test.java-cs.txt.java,{}/test.java-cs.txt.cs'.format(data_dir, data_dir) |
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elif task == 'clone': |
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data_dir = '{}/{}'.format(data_root, task) |
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train_fn = '{}/train.txt'.format(data_dir) |
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dev_fn = '{}/valid.txt'.format(data_dir) |
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test_fn = '{}/test.txt'.format(data_dir) |
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elif task == 'defect': |
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data_dir = '{}/{}'.format(data_root, task) |
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train_fn = '{}/train.jsonl'.format(data_dir) |
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dev_fn = '{}/valid.jsonl'.format(data_dir) |
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test_fn = '{}/test.jsonl'.format(data_dir) |
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if split == 'train': |
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return train_fn |
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elif split == 'dev': |
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return dev_fn |
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elif split == 'test': |
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return test_fn |
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else: |
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return train_fn, dev_fn, test_fn |
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def read_examples(filename, data_num, task): |
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read_example_dict = { |
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'summarize': read_summarize_examples, |
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'refine': read_refine_examples, |
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'translate': read_translate_examples, |
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'concode': read_concode_examples, |
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'clone': read_clone_examples, |
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'defect': read_defect_examples, |
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} |
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return read_example_dict[task](filename, data_num) |
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def calc_stats(examples, tokenizer=None, is_tokenize=False): |
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avg_src_len = [] |
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avg_trg_len = [] |
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avg_src_len_tokenize = [] |
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avg_trg_len_tokenize = [] |
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for ex in examples: |
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if is_tokenize: |
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avg_src_len.append(len(ex.source.split())) |
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avg_trg_len.append(len(str(ex.target).split())) |
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avg_src_len_tokenize.append(len(tokenizer.tokenize(ex.source))) |
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avg_trg_len_tokenize.append(len(tokenizer.tokenize(str(ex.target)))) |
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else: |
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avg_src_len.append(len(ex.source.split())) |
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avg_trg_len.append(len(str(ex.target).split())) |
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if is_tokenize: |
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logger.info("Read %d examples, avg src len: %d, avg trg len: %d, max src len: %d, max trg len: %d", |
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len(examples), np.mean(avg_src_len), np.mean(avg_trg_len), max(avg_src_len), max(avg_trg_len)) |
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logger.info("[TOKENIZE] avg src len: %d, avg trg len: %d, max src len: %d, max trg len: %d", |
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np.mean(avg_src_len_tokenize), np.mean(avg_trg_len_tokenize), max(avg_src_len_tokenize), |
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max(avg_trg_len_tokenize)) |
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else: |
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logger.info("Read %d examples, avg src len: %d, avg trg len: %d, max src len: %d, max trg len: %d", |
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len(examples), np.mean(avg_src_len), np.mean(avg_trg_len), max(avg_src_len), max(avg_trg_len)) |
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def get_elapse_time(t0): |
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elapse_time = time.time() - t0 |
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if elapse_time > 3600: |
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hour = int(elapse_time // 3600) |
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minute = int((elapse_time % 3600) // 60) |
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return "{}h{}m".format(hour, minute) |
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else: |
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minute = int((elapse_time % 3600) // 60) |
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return "{}m".format(minute) |
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