format utils for summarization

examples/utils_summarization.py

@@ -128,7 +128,7 @@ def build_mask(sequence, pad_token):
     """ Builds the mask. The attention mechanism will only attend to positions
     with value 1. """
     mask = torch.ones_like(sequence)
-    idx_pad_tokens = (sequence == pad_token)
+    idx_pad_tokens = sequence == pad_token
     mask[idx_pad_tokens] = 0
     return mask
 

examples/utils_summarization_test.py

@@ -105,9 +105,7 @@ class SummarizationDataProcessingTest(unittest.TestCase):
     def test_build_mask_no_padding(self):
         sequence = torch.tensor([1, 2, 3, 4])
         expected = torch.tensor([1, 1, 1, 1])
-        np.testing.assert_array_equal(
-            build_mask(sequence, 0).numpy(), expected.numpy()
-        )
+        np.testing.assert_array_equal(build_mask(sequence, 0).numpy(), expected.numpy())
 
     def test_build_mask(self):
         sequence = torch.tensor([1, 2, 3, 4, 23, 23, 23])
@@ -119,9 +117,7 @@ class SummarizationDataProcessingTest(unittest.TestCase):
     def test_build_mask_with_padding_equal_to_one(self):
         sequence = torch.tensor([8, 2, 3, 4, 1, 1, 1])
         expected = torch.tensor([1, 1, 1, 1, 0, 0, 0])
-        np.testing.assert_array_equal(
-            build_mask(sequence, 1).numpy(), expected.numpy()
-        )
+        np.testing.assert_array_equal(build_mask(sequence, 1).numpy(), expected.numpy())
 
     def test_compute_token_type_ids(self):
         separator = 101