|
| 1 | +import os |
| 2 | +import random |
| 3 | +import string |
| 4 | +import lzma |
| 5 | +from parameterized import parameterized |
| 6 | +from collections import defaultdict |
| 7 | +from unittest.mock import patch |
| 8 | + |
| 9 | +from torchtext.datasets import CC100 |
| 10 | + |
| 11 | +from ..common.case_utils import TempDirMixin, zip_equal |
| 12 | +from ..common.torchtext_test_case import TorchtextTestCase |
| 13 | + |
| 14 | +from torchtext.datasets.cc100 import VALID_CODES |
| 15 | + |
| 16 | + |
| 17 | +def _get_mock_dataset(root_dir): |
| 18 | + """ |
| 19 | + root_dir: directory to the mocked dataset |
| 20 | + """ |
| 21 | + base_dir = os.path.join(root_dir, "CC100") |
| 22 | + os.makedirs(base_dir, exist_ok=True) |
| 23 | + |
| 24 | + seed = 1 |
| 25 | + mocked_data = defaultdict(list) |
| 26 | + |
| 27 | + for language_code in VALID_CODES: |
| 28 | + file_name = f"{language_code}.txt.xz" |
| 29 | + compressed_file = os.path.join(base_dir, file_name) |
| 30 | + with lzma.open(compressed_file, "wt") as f: |
| 31 | + for i in range(5): |
| 32 | + rand_string = " ".join( |
| 33 | + random.choice(string.ascii_letters) for i in range(seed) |
| 34 | + ) |
| 35 | + content = f"{rand_string}\n" |
| 36 | + f.write(content) |
| 37 | + mocked_data[language_code].append((language_code, rand_string)) |
| 38 | + seed += 1 |
| 39 | + |
| 40 | + return mocked_data |
| 41 | + |
| 42 | + |
| 43 | +class TestCC100(TempDirMixin, TorchtextTestCase): |
| 44 | + @classmethod |
| 45 | + def setUpClass(cls): |
| 46 | + super().setUpClass() |
| 47 | + cls.root_dir = cls.get_base_temp_dir() |
| 48 | + cls.samples = _get_mock_dataset(cls.root_dir) |
| 49 | + cls.patcher = patch( |
| 50 | + "torchdata.datapipes.iter.util.cacheholder._hash_check", return_value=True |
| 51 | + ) |
| 52 | + cls.patcher.start() |
| 53 | + |
| 54 | + @classmethod |
| 55 | + def tearDownClass(cls): |
| 56 | + cls.patcher.stop() |
| 57 | + super().tearDownClass() |
| 58 | + |
| 59 | + @parameterized.expand(VALID_CODES) |
| 60 | + def test_cc100(self, language_code): |
| 61 | + dataset = CC100(root=self.root_dir, split="train", language_code=language_code) |
| 62 | + |
| 63 | + samples = list(dataset) |
| 64 | + expected_samples = self.samples[language_code] |
| 65 | + for sample, expected_sample in zip_equal(samples, expected_samples): |
| 66 | + self.assertEqual(sample, expected_sample) |
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