Add SQuAD1 Mocked Unit Test

test/datasets/test_squad1.py

@@ -0,0 +1,104 @@
+import json
+import os
+import random
+import string
+import uuid
+from collections import defaultdict
+from random import randint
+from unittest.mock import patch
+
+from parameterized import parameterized
+from torchtext.data.datasets_utils import _ParseSQuADQAData
+from torchtext.datasets.squad1 import SQuAD1
+
+from ..common.case_utils import TempDirMixin, zip_equal
+from ..common.torchtext_test_case import TorchtextTestCase
+
+
+def _get_mock_json_data():
+    rand_string = " ".join(random.choice(string.ascii_letters) for i in range(10))
+    mock_json_data = {
+        "data": [
+            {
+                "title": rand_string,
+                "paragraphs": [
+                    {
+                        "context": rand_string,
+                        "qas": [
+                            {
+                                "answers": [
+                                    {
+                                        "answer_start": randint(1, 1000),
+                                        "text": rand_string,
+                                    }
+                                ],
+                                "question": rand_string,
+                                "id": uuid.uuid1().hex,
+                            },
+                        ],
+                    }
+                ],
+            }
+        ]
+    }
+    return mock_json_data
+
+
+def _get_mock_dataset(root_dir):
+    """
+    root_dir: directory to the mocked dataset
+    """
+    base_dir = os.path.join(root_dir, "SQuAD1")
+    os.makedirs(base_dir, exist_ok=True)
+
+    mocked_data = defaultdict(list)
+    for file_name in ("train-v1.1.json", "dev-v1.1.json"):
+        txt_file = os.path.join(base_dir, file_name)
+        with open(txt_file, "w") as f:
+            mock_json_data = _get_mock_json_data()
+            f.write(json.dumps(mock_json_data))
+
+            split = "train" if "train" in file_name else "dev"
+            dataset_line = next(
+                iter(_ParseSQuADQAData([("file_handle", mock_json_data)]))
+            )
+            mocked_data[split].append(dataset_line)
+
+    return mocked_data
+
+
+class TestSQuAD1(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(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", "dev"])
+    def test_squad1(self, split):
+        dataset = SQuAD1(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"])
+    def test_squad1_split_argument(self, split):
+        dataset1 = SQuAD1(root=self.root_dir, split=split)
+        (dataset2,) = SQuAD1(root=self.root_dir, split=(split,))
+
+        for d1, d2 in zip_equal(dataset1, dataset2):
+            self.assertEqual(d1, d2)