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import os
import zipfile
from unittest.mock import patch
from torchtext.datasets.enwik9 import EnWik9
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, "EnWik9")
temp_dataset_dir = os.path.join(base_dir, "temp_dataset_dir")
os.makedirs(temp_dataset_dir, exist_ok=True)
seed = 1
file_name = "enwik9"
txt_file = os.path.join(temp_dataset_dir, file_name)
mocked_data = []
with open(txt_file, "w", encoding="utf-8") as f:
for i in range(5):
rand_string = "<" + get_random_unicode(seed) + ">"
dataset_line = f"'{rand_string}'"
f.write(f"'{rand_string}'\n")
# append line to correct dataset split
mocked_data.append(dataset_line)
seed += 1
compressed_dataset_path = os.path.join(base_dir, "enwik9.zip")
# create zip file from dataset folder
with zipfile.ZipFile(compressed_dataset_path, "w") as zip_file:
txt_file = os.path.join(temp_dataset_dir, file_name)
zip_file.write(txt_file, arcname=file_name)
return mocked_data
class TestEnWik9(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()
def test_enwik9(self) -> None:
dataset = EnWik9(root=self.root_dir)
samples = list(dataset)
expected_samples = self.samples
for sample, expected_sample in zip_equal(samples, expected_samples):
self.assertEqual(sample, expected_sample)
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