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import os
import zipfile
from collections import defaultdict
from unittest.mock import patch
from parameterized import parameterized
from torchtext.datasets.mnli import MNLI
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, "MNLI")
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 file_name in ["multinli_1.0_train.txt", "multinli_1.0_dev_matched.txt", "multinli_1.0_dev_mismatched.txt"]:
txt_file = os.path.join(temp_dataset_dir, file_name)
with open(txt_file, "w", encoding="utf-8") as f:
f.write(
"gold_label\tsentence1_binary_parse\tsentence2_binary_parse\tsentence1_parse\tsentence2_parse\tsentence1\tsentence2\tpromptID\tpairID\tgenre\tlabel1\tlabel2\tlabel3\tlabel4\tlabel5"
)
for i in range(5):
label = seed % 3
rand_string = get_random_unicode(seed)
dataset_line = (label, rand_string, rand_string)
f.write(
f"{label}\t{rand_string}\t{rand_string}\t{rand_string}\t{rand_string}\t{rand_string}\t{rand_string}\t{i}\t{i}\t{i}\t{i}\t{i}\t{i}\t{i}\t{i}\n"
)
# append line to correct dataset split
mocked_data[os.path.splitext(file_name)[0]].append(dataset_line)
seed += 1
compressed_dataset_path = os.path.join(base_dir, "multinli_1.0.zip")
# create zip file from dataset folder
with zipfile.ZipFile(compressed_dataset_path, "w") as zip_file:
for file_name in ("multinli_1.0_train.txt", "multinli_1.0_dev_matched.txt", "multinli_1.0_dev_mismatched.txt"):
txt_file = os.path.join(temp_dataset_dir, file_name)
zip_file.write(txt_file, arcname=os.path.join("multinli_1.0", file_name))
return mocked_data
class TestMNLI(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", "dev_matched", "dev_mismatched"])
def test_mnli(self, split):
dataset = MNLI(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", "dev_matched", "dev_mismatched"])
def test_sst2_split_argument(self, split):
dataset1 = MNLI(root=self.root_dir, split=split)
(dataset2,) = MNLI(root=self.root_dir, split=(split,))
for d1, d2 in zip_equal(dataset1, dataset2):
self.assertEqual(d1, d2)
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