mock up AG NEWS test for faster testing.

test/datasets/test_agnews.py

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+import os
+import random
+import string
+from collections import defaultdict
+from unittest.mock import patch
+
+from parameterized import parameterized
+from torchtext.datasets.ag_news import AG_NEWS
+
+from ..common.case_utils import TempDirMixin, zip_equal
+from ..common.torchtext_test_case import TorchtextTestCase
+
+
+def _get_mock_dataset(root_dir):
+    """
+    root_dir: directory to the mocked dataset
+    """
+    temp_dataset_dir = os.path.join(root_dir, "AG_NEWS")
+    os.makedirs(temp_dataset_dir, exist_ok=True)
+
+    seed = 1
+    mocked_data = defaultdict(list)
+    for file_name in ("train.csv", "test.csv"):
+        txt_file = os.path.join(temp_dataset_dir, file_name)
+        with open(txt_file, "w") as f:
+            for i in range(5):
+                label = seed % 4 + 1
+                rand_string = " ".join(
+                    random.choice(string.ascii_letters) for i in range(seed)
+                )
+                dataset_line = (label, f"{rand_string} {rand_string}")
+                # append line to correct dataset split
+                mocked_data[os.path.splitext(file_name)[0]].append(dataset_line)
+                f.write(f'"{label}","{rand_string}","{rand_string}"\n')
+                seed += 1
+
+    return mocked_data
+
+
+class TestAGNews(TempDirMixin, TorchtextTestCase):
+    root_dir = None
+    samples = []
+    patcher = None
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.root_dir = cls.get_base_temp_dir()
+        cls.samples = _get_mock_dataset(cls.root_dir)
+        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_agnews(self, split):
+        dataset = AG_NEWS(root=self.root_dir, split=split)
+
+        samples = list(dataset)
+        expected_samples = self.samples[split]
+        for sample, expected_sample in zip_equal(samples, expected_samples):
+            print(sample, expected_sample)
+            self.assertEqual(sample, expected_sample)
+
+    @parameterized.expand(["train", "test"])
+    def test_agnews_split_argument(self, split):
+        dataset1 = AG_NEWS(root=self.root_dir, split=split)
+        (dataset2,) = AG_NEWS(root=self.root_dir, split=(split,))
+
+        for d1, d2 in zip_equal(dataset1, dataset2):
+            self.assertEqual(d1, d2)