[FORMATTING] Update formatting for dataset tests

test/datasets/test_cc100.py

@@ -1,18 +1,17 @@
+import lzma
 import os
 import random
 import string
-import lzma
-from parameterized import parameterized
 from collections import defaultdict
 from unittest.mock import patch
 
+from parameterized import parameterized
 from torchtext.datasets import CC100
+from torchtext.datasets.cc100 import VALID_CODES
 
 from ..common.case_utils import TempDirMixin, zip_equal
 from ..common.torchtext_test_case import TorchtextTestCase
 
-from torchtext.datasets.cc100 import VALID_CODES
-
 
 def _get_mock_dataset(root_dir):
     """

test/datasets/test_conll2000chunking.py

@@ -1,7 +1,7 @@
+import gzip
 import os
 import random
 import string
-import gzip
 from collections import defaultdict
 from unittest.mock import patch
 
@@ -27,11 +27,19 @@ def _get_mock_dataset(root_dir):
         mocked_lines = mocked_data[os.path.splitext(file_name)[0]]
         with open(txt_file, "w") as f:
             for i in range(5):
-                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)]
+                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):
+                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)
@@ -41,7 +49,9 @@ def _get_mock_dataset(root_dir):
 
         # create gz file from dataset folder
         compressed_dataset_path = os.path.join(base_dir, f"{file_name}.gz")
-        with gzip.open(compressed_dataset_path, "wb") as gz_file, open(txt_file, "rb") as file_in:
+        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

test/datasets/test_enwik9.py

@@ -24,10 +24,12 @@ def _get_mock_dataset(root_dir):
     mocked_data = []
     with open(txt_file, "w") as f:
         for i in range(5):
-            rand_string = "<" + " ".join(
-                random.choice(string.ascii_letters) for i in range(seed)
-            ) + ">"
-            dataset_line = (f"'{rand_string}'")
+            rand_string = (
+                "<"
+                + " ".join(random.choice(string.ascii_letters) for i in range(seed))
+                + ">"
+            )
+            dataset_line = f"'{rand_string}'"
             f.write(f"'{rand_string}'\n")
 
             # append line to correct dataset split

test/datasets/test_iwslt2016.py

@@ -1,37 +1,54 @@
+import itertools
 import os
 import random
 import shutil
 import string
 import tarfile
-import itertools
 import tempfile
 from collections import defaultdict
 from unittest.mock import patch
 
 from parameterized import parameterized
-from torchtext.datasets.iwslt2016 import DATASET_NAME, IWSLT2016, SUPPORTED_DATASETS, SET_NOT_EXISTS
 from torchtext.data.datasets_utils import _generate_iwslt_files_for_lang_and_split
+from torchtext.datasets.iwslt2016 import (
+    DATASET_NAME,
+    IWSLT2016,
+    SUPPORTED_DATASETS,
+    SET_NOT_EXISTS,
+)
 
 from ..common.case_utils import zip_equal
 from ..common.torchtext_test_case import TorchtextTestCase
 
-SUPPORTED_LANGPAIRS = [(k, e) for k, v in SUPPORTED_DATASETS["language_pair"].items() for e in v]
+SUPPORTED_LANGPAIRS = [
+    (k, e) for k, v in SUPPORTED_DATASETS["language_pair"].items() for e in v
+]
 SUPPORTED_DEVTEST_SPLITS = SUPPORTED_DATASETS["valid_test"]
-DEV_TEST_SPLITS = [(dev, test) for dev, test in itertools.product(SUPPORTED_DEVTEST_SPLITS, repeat=2) if dev != test]
+DEV_TEST_SPLITS = [
+    (dev, test)
+    for dev, test in itertools.product(SUPPORTED_DEVTEST_SPLITS, repeat=2)
+    if dev != test
+]
 
 
 def _generate_uncleaned_train():
     """Generate tags files"""
     file_contents = []
     examples = []
     xml_tags = [
-        '<url', '<keywords', '<talkid', '<description', '<reviewer',
-        '<translator', '<title', '<speaker', '<doc', '</doc'
+        "<url",
+        "<keywords",
+        "<talkid",
+        "<description",
+        "<reviewer",
+        "<translator",
+        "<title",
+        "<speaker",
+        "<doc",
+        "</doc",
     ]
     for i in range(100):
-        rand_string = " ".join(
-            random.choice(string.ascii_letters) for i in range(10)
-        )
+        rand_string = " ".join(random.choice(string.ascii_letters) for i in range(10))
         # With a 10% change, add one of the XML tags which is cleaned
         # to ensure cleaning happens appropriately
         if random.random() < 0.1:
@@ -79,7 +96,7 @@ def _get_mock_dataset(root_dir, split, src, tgt, valid_set, test_set):
     """
 
     base_dir = os.path.join(root_dir, DATASET_NAME)
-    temp_dataset_dir = os.path.join(base_dir, 'temp_dataset_dir')
+    temp_dataset_dir = os.path.join(base_dir, "temp_dataset_dir")
     outer_temp_dataset_dir = os.path.join(temp_dataset_dir, f"texts/{src}/{tgt}/")
     inner_temp_dataset_dir = os.path.join(outer_temp_dataset_dir, f"{src}-{tgt}")
 
@@ -88,7 +105,9 @@ def _get_mock_dataset(root_dir, split, src, tgt, valid_set, test_set):
 
     mocked_data = defaultdict(lambda: defaultdict(list))
 
-    cleaned_file_names, uncleaned_file_names = _generate_iwslt_files_for_lang_and_split(16, src, tgt, valid_set, test_set)
+    cleaned_file_names, uncleaned_file_names = _generate_iwslt_files_for_lang_and_split(
+        16, src, tgt, valid_set, test_set
+    )
     uncleaned_src_file = uncleaned_file_names[src][split]
     uncleaned_tgt_file = uncleaned_file_names[tgt][split]
 
@@ -97,7 +116,7 @@ def _get_mock_dataset(root_dir, split, src, tgt, valid_set, test_set):
 
     for (unclean_file_name, clean_file_name) in [
         (uncleaned_src_file, cleaned_src_file),
-        (uncleaned_tgt_file, cleaned_tgt_file)
+        (uncleaned_tgt_file, cleaned_tgt_file),
     ]:
         # Get file extension (i.e., the language) without the . prefix (.en -> en)
         lang = os.path.splitext(unclean_file_name)[1][1:]
@@ -144,20 +163,31 @@ def tearDownClass(cls):
         cls.patcher.stop()
         super().tearDownClass()
 
-    @parameterized.expand([
-        (split, src, tgt, dev_set, test_set)
-        for split in ("train", "valid", "test")
-        for dev_set, test_set in DEV_TEST_SPLITS
-        for src, tgt in SUPPORTED_LANGPAIRS
-        if (dev_set not in SET_NOT_EXISTS[(src, tgt)] and test_set not in SET_NOT_EXISTS[(src, tgt)])
-    ])
+    @parameterized.expand(
+        [
+            (split, src, tgt, dev_set, test_set)
+            for split in ("train", "valid", "test")
+            for dev_set, test_set in DEV_TEST_SPLITS
+            for src, tgt in SUPPORTED_LANGPAIRS
+            if (
+                dev_set not in SET_NOT_EXISTS[(src, tgt)]
+                and test_set not in SET_NOT_EXISTS[(src, tgt)]
+            )
+        ]
+    )
     def test_iwslt2016(self, split, src, tgt, dev_set, test_set):
 
         with tempfile.TemporaryDirectory() as root_dir:
-            expected_samples = _get_mock_dataset(root_dir, split, src, tgt, dev_set, test_set)
+            expected_samples = _get_mock_dataset(
+                root_dir, split, src, tgt, dev_set, test_set
+            )
 
             dataset = IWSLT2016(
-                root=root_dir, split=split, language_pair=(src, tgt), valid_set=dev_set, test_set=test_set
+                root=root_dir,
+                split=split,
+                language_pair=(src, tgt),
+                valid_set=dev_set,
+                test_set=test_set,
             )
 
             samples = list(dataset)
@@ -171,9 +201,23 @@ def test_iwslt2016_split_argument(self, split):
             language_pair = ("de", "en")
             valid_set = "tst2013"
             test_set = "tst2014"
-            _ = _get_mock_dataset(root_dir, split, language_pair[0], language_pair[1], valid_set, test_set)
-            dataset1 = IWSLT2016(root=root_dir, split=split, language_pair=language_pair, valid_set=valid_set, test_set=test_set)
-            (dataset2,) = IWSLT2016(root=root_dir, split=(split,), language_pair=language_pair, valid_set=valid_set, test_set=test_set)
+            _ = _get_mock_dataset(
+                root_dir, split, language_pair[0], language_pair[1], valid_set, test_set
+            )
+            dataset1 = IWSLT2016(
+                root=root_dir,
+                split=split,
+                language_pair=language_pair,
+                valid_set=valid_set,
+                test_set=test_set,
+            )
+            (dataset2,) = IWSLT2016(
+                root=root_dir,
+                split=(split,),
+                language_pair=language_pair,
+                valid_set=valid_set,
+                test_set=test_set,
+            )
 
             for d1, d2 in zip_equal(dataset1, dataset2):
                 self.assertEqual(d1, d2)

test/datasets/test_iwslt2017.py

@@ -8,27 +8,40 @@
 from unittest.mock import patch
 
 from parameterized import parameterized
-from torchtext.datasets.iwslt2017 import DATASET_NAME, IWSLT2017, SUPPORTED_DATASETS, _PATH
 from torchtext.data.datasets_utils import _generate_iwslt_files_for_lang_and_split
+from torchtext.datasets.iwslt2017 import (
+    DATASET_NAME,
+    IWSLT2017,
+    SUPPORTED_DATASETS,
+    _PATH,
+)
 
 from ..common.case_utils import zip_equal
 from ..common.torchtext_test_case import TorchtextTestCase
 
-SUPPORTED_LANGPAIRS = [(k, e) for k, v in SUPPORTED_DATASETS["language_pair"].items() for e in v]
+SUPPORTED_LANGPAIRS = [
+    (k, e) for k, v in SUPPORTED_DATASETS["language_pair"].items() for e in v
+]
 
 
 def _generate_uncleaned_train():
     """Generate tags files"""
     file_contents = []
     examples = []
     xml_tags = [
-        '<url', '<keywords', '<talkid', '<description', '<reviewer',
-        '<translator', '<title', '<speaker', '<doc', '</doc'
+        "<url",
+        "<keywords",
+        "<talkid",
+        "<description",
+        "<reviewer",
+        "<translator",
+        "<title",
+        "<speaker",
+        "<doc",
+        "</doc",
     ]
     for i in range(100):
-        rand_string = " ".join(
-            random.choice(string.ascii_letters) for i in range(10)
-        )
+        rand_string = " ".join(random.choice(string.ascii_letters) for i in range(10))
         # With a 10% change, add one of the XML tags which is cleaned
         # to ensure cleaning happens appropriately
         if random.random() < 0.1:
@@ -76,16 +89,22 @@ def _get_mock_dataset(root_dir, split, src, tgt, valid_set, test_set):
     """
 
     base_dir = os.path.join(root_dir, DATASET_NAME)
-    temp_dataset_dir = os.path.join(base_dir, 'temp_dataset_dir')
-    outer_temp_dataset_dir = os.path.join(temp_dataset_dir, "texts/DeEnItNlRo/DeEnItNlRo")
-    inner_temp_dataset_dir = os.path.join(outer_temp_dataset_dir, "DeEnItNlRo-DeEnItNlRo")
+    temp_dataset_dir = os.path.join(base_dir, "temp_dataset_dir")
+    outer_temp_dataset_dir = os.path.join(
+        temp_dataset_dir, "texts/DeEnItNlRo/DeEnItNlRo"
+    )
+    inner_temp_dataset_dir = os.path.join(
+        outer_temp_dataset_dir, "DeEnItNlRo-DeEnItNlRo"
+    )
 
     os.makedirs(outer_temp_dataset_dir, exist_ok=True)
     os.makedirs(inner_temp_dataset_dir, exist_ok=True)
 
     mocked_data = defaultdict(lambda: defaultdict(list))
 
-    cleaned_file_names, uncleaned_file_names = _generate_iwslt_files_for_lang_and_split(17, src, tgt, valid_set, test_set)
+    cleaned_file_names, uncleaned_file_names = _generate_iwslt_files_for_lang_and_split(
+        17, src, tgt, valid_set, test_set
+    )
     uncleaned_src_file = uncleaned_file_names[src][split]
     uncleaned_tgt_file = uncleaned_file_names[tgt][split]
 
@@ -94,7 +113,7 @@ def _get_mock_dataset(root_dir, split, src, tgt, valid_set, test_set):
 
     for (unclean_file_name, clean_file_name) in [
         (uncleaned_src_file, cleaned_src_file),
-        (uncleaned_tgt_file, cleaned_tgt_file)
+        (uncleaned_tgt_file, cleaned_tgt_file),
     ]:
         # Get file extension (i.e., the language) without the . prefix (.en -> en)
         lang = os.path.splitext(unclean_file_name)[1][1:]
@@ -141,15 +160,19 @@ def tearDownClass(cls):
         cls.patcher.stop()
         super().tearDownClass()
 
-    @parameterized.expand([
-        (split, src, tgt)
-        for split in ("train", "valid", "test")
-        for src, tgt in SUPPORTED_LANGPAIRS
-    ])
+    @parameterized.expand(
+        [
+            (split, src, tgt)
+            for split in ("train", "valid", "test")
+            for src, tgt in SUPPORTED_LANGPAIRS
+        ]
+    )
     def test_iwslt2017(self, split, src, tgt):
 
         with tempfile.TemporaryDirectory() as root_dir:
-            expected_samples = _get_mock_dataset(root_dir, split, src, tgt, "dev2010", "tst2010")
+            expected_samples = _get_mock_dataset(
+                root_dir, split, src, tgt, "dev2010", "tst2010"
+            )
 
             dataset = IWSLT2017(root=root_dir, split=split, language_pair=(src, tgt))
 
@@ -164,9 +187,15 @@ def test_iwslt2017_split_argument(self, split):
             language_pair = ("de", "en")
             valid_set = "dev2010"
             test_set = "tst2010"
-            _ = _get_mock_dataset(root_dir, split, language_pair[0], language_pair[1], valid_set, test_set)
-            dataset1 = IWSLT2017(root=root_dir, split=split, language_pair=language_pair)
-            (dataset2,) = IWSLT2017(root=root_dir, split=(split,), language_pair=language_pair)
+            _ = _get_mock_dataset(
+                root_dir, split, language_pair[0], language_pair[1], valid_set, test_set
+            )
+            dataset1 = IWSLT2017(
+                root=root_dir, split=split, language_pair=language_pair
+            )
+            (dataset2,) = IWSLT2017(
+                root=root_dir, split=(split,), language_pair=language_pair
+            )
 
             for d1, d2 in zip_equal(dataset1, dataset2):
                 self.assertEqual(d1, d2)