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import os
import tarfile
from collections import defaultdict
from unittest.mock import patch
from parameterized import parameterized
from torchtext.datasets.imdb import IMDB
from ..common.case_utils import TempDirMixin, zip_equal, get_random_unicode
from ..common.torchtext_test_case import TorchtextTestCase
def _get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
base_dir = os.path.join(root_dir, "IMDB")
temp_dataset_dir = os.path.join(base_dir, "temp_dataset_dir")
os.makedirs(temp_dataset_dir, exist_ok=True)
seed = 1
mocked_data = defaultdict(list)
for split in ("train", "test"):
neg_dir = os.path.join(temp_dataset_dir, split, "neg")
pos_dir = os.path.join(temp_dataset_dir, split, "pos")
os.makedirs(neg_dir, exist_ok=True)
os.makedirs(pos_dir, exist_ok=True)
for i in range(5):
# all negative labels are read first before positive labels in the
# IMDB dataset implementation
label = 1 if i < 2 else 2
cur_dir = pos_dir if label == 2 else neg_dir
txt_file = os.path.join(cur_dir, f"{i}{i}_{i}.txt")
with open(txt_file, "w", encoding="utf-8") as f:
rand_string = get_random_unicode(seed)
dataset_line = (label, rand_string)
# append line to correct dataset split
mocked_data[split].append(dataset_line)
f.write(rand_string)
seed += 1
compressed_dataset_path = os.path.join(base_dir, "aclImdb_v1.tar.gz")
# create tar file from dataset folder
with tarfile.open(compressed_dataset_path, "w:gz") as tar:
tar.add(temp_dataset_dir, arcname="aclImdb_v1")
return mocked_data
class TestIMDB(TempDirMixin, TorchtextTestCase):
root_dir = None
samples = []
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.root_dir = cls.get_base_temp_dir()
cls.samples = _get_mock_dataset(os.path.join(cls.root_dir, "datasets"))
cls.patcher = patch("torchdata.datapipes.iter.util.cacheholder._hash_check", return_value=True)
cls.patcher.start()
@classmethod
def tearDownClass(cls):
cls.patcher.stop()
super().tearDownClass()
@parameterized.expand(["train", "test"])
def test_imdb(self, split):
dataset = IMDB(root=self.root_dir, split=split)
samples = list(dataset)
expected_samples = self.samples[split]
for sample, expected_sample in zip_equal(samples, expected_samples):
self.assertEqual(sample, expected_sample)
@parameterized.expand(["train", "test"])
def test_imdb_split_argument(self, split):
dataset1 = IMDB(root=self.root_dir, split=split)
(dataset2,) = IMDB(root=self.root_dir, split=(split,))
for d1, d2 in zip_equal(dataset1, dataset2):
self.assertEqual(d1, d2)
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