From c040ccd9ffc0b781d22bf452bdb28e69f6464b2d Mon Sep 17 00:00:00 2001
From: melihyilmaz <yilmazmelih97@gmail.com>
Date: Mon, 12 Feb 2024 16:46:17 -0800
Subject: [PATCH] Fix lint

---
 tests/unit_tests/test_unit.py | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/tests/unit_tests/test_unit.py b/tests/unit_tests/test_unit.py
index 20eca746..61d61efa 100644
--- a/tests/unit_tests/test_unit.py
+++ b/tests/unit_tests/test_unit.py
@@ -426,7 +426,7 @@ def test_beam_search_decode():
 
     # Test _get_topk_beams() with finished beams in the batch.
     model = Spec2Pep(n_beams=1, residues="massivekb", min_peptide_len=3)
-    
+
     # Sizes and other variables.
     batch = 2  # B
     beam = model.n_beams  # S
@@ -440,33 +440,33 @@ def test_beam_search_decode():
         size=(batch, length, vocab, beam), fill_value=torch.nan
     )
     scores = einops.rearrange(scores, "B L V S -> (B S) L V")
-    tokens = torch.zeros(batch * beam, length, dtype=torch.int64)    
-    
+    tokens = torch.zeros(batch * beam, length, dtype=torch.int64)
+
     # Simulate non-zero amino acid-level probability scores.
-    scores[:, :step+1, :] = torch.rand(batch, step+1, vocab)
+    scores[:, : step + 1, :] = torch.rand(batch, step + 1, vocab)
     scores[:, step, range(1, 4)] = torch.tensor([1.0, 2.0, 3.0])
-    
-    # Simulate one finished and one unfinished beam in the same batch
-    tokens[0,:step] = torch.tensor([4, 14, 4, 28])
-    tokens[1,:step] = torch.tensor([4, 14, 4, 1])
-    
-    # Set finished beams array to allow decoding from only one beam
+
+    # Simulate one finished and one unfinished beam in the same batch.
+    tokens[0, :step] = torch.tensor([4, 14, 4, 28])
+    tokens[1, :step] = torch.tensor([4, 14, 4, 1])
+
+    # Set finished beams array to allow decoding from only one beam.
     test_finished_beams = torch.tensor([True, False])
 
     new_tokens, new_scores = model._get_topk_beams(
         tokens, scores, test_finished_beams, batch, step
     )
-    
-    # Only the second peptide should have a new token predicted
+
+    # Only the second peptide should have a new token predicted.
     expected_tokens = torch.tensor(
         [
             [4, 14, 4, 28, 0],
             [4, 14, 4, 1, 3],
-
         ]
     )
-  
-    assert torch.equal(new_tokens[:, : step + 1], expected_tokens)    
+
+    assert torch.equal(new_tokens[:, : step + 1], expected_tokens)
+
 
 def test_eval_metrics():
     """