Add CoNLL2000Checking Mocked Unit Test

test/datasets/test_conll2000chunking.py

@@ -0,0 +1,85 @@
+import os
+import random
+import string
+import gzip
+from collections import defaultdict
+from unittest.mock import patch
+
+from parameterized import parameterized
+from torchtext.datasets.conll2000chunking import CoNLL2000Chunking
+
+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
+    """
+    base_dir = os.path.join(root_dir, "CoNLL2000Chunking")
+    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 ("train.txt", "test.txt"):
+        txt_file = os.path.join(temp_dataset_dir, file_name)
+        mocked_lines = mocked_data[os.path.splitext(file_name)[0]]
+        with open(txt_file, "w") as f:
+            for i in range(5):
+                label = seed % 2
+                rand_strings = [random.choice(string.ascii_letters) for i in range(seed)]
+                rand_label_1 = [random.choice(string.ascii_letters) for i in range(seed)]
+                rand_label_2 = [random.choice(string.ascii_letters) for i in range(seed)]
+                # one token per line (each sample ends with an extra \n)
+                for rand_string, label_1, label_2 in zip(rand_strings, rand_label_1, rand_label_2):
+                    f.write(f"{rand_string} {label_1} {label_2}\n")
+                f.write("\n")
+                dataset_line = (rand_strings, rand_label_1, rand_label_2)
+                # append line to correct dataset split
+                mocked_lines.append(dataset_line)
+                seed += 1
+
+
+        # create gz file from dataset folder
+        compressed_dataset_path = os.path.join(temp_dataset_dir, f"{file_name}.gz")
+        with gzip.open(compressed_dataset_path, "wb") as gz_file, open(txt_file, "rb") as file_in:
+            gz_file.writelines(file_in)
+
+    return mocked_data
+
+
+class TestCoNLL2000Chunking(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", "test"])
+    def test_conll2000chunking(self, split):
+        dataset = CoNLL2000Chunking(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", "test"])
+    def test_conll2000chunking_split_argument(self, split):
+        dataset1 = CoNLL2000Chunking(root=self.root_dir, split=split)
+        (dataset2,) = CoNLL2000Chunking(root=self.root_dir, split=(split,))
+
+        for d1, d2 in zip_equal(dataset1, dataset2):
+            self.assertEqual(d1, d2)