CDL: a few pylint changes

pose_evaluation/metrics/test_embedding_distance_metric.py

@@ -20,7 +20,7 @@
 
 # Device configuration for PyTorch
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-torch.set_default_device(DEVICE) # so that we get arrays on the same device
+torch.set_default_device(DEVICE)  # so that we get arrays on the same device
 
 
 # named the fixture this way to solve many pylint W0621
@@ -36,11 +36,14 @@ def fixture_embeddings() -> List[torch.Tensor]:
     """Fixture to create dummy embeddings for testing."""
     return [random_tensor(768) for _ in range(5)]
 
-def call_and_call_with_inputs_swapped(hyp:torch.Tensor, ref:torch.Tensor, scoring_function:Callable[[torch.Tensor, torch.Tensor], torch.Tensor])->Tuple[torch.Tensor, torch.Tensor]:
+
+def call_and_call_with_inputs_swapped(
+    hyp: torch.Tensor, ref: torch.Tensor, scoring_function: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]
+) -> Tuple[torch.Tensor, torch.Tensor]:
     score1 = scoring_function(hyp, ref)
     score2 = scoring_function(ref, hyp)
     return score1, score2
-    
+
 
 def save_and_plot_distances(distances, matrix_name, num_points, dim):
     """Helper function to save distance matrix and plot distances."""
@@ -211,12 +214,12 @@ def test_score_all_with_one_vs_batch(cosine_metric, distance_range_checker):
 
     # scores = cosine_metric.score_all(hyps, refs)
     scores, scores2 = call_and_call_with_inputs_swapped(hyps, refs, cosine_metric.score_all)
-
 
     assert scores.shape == (len(hyps), 1)
     assert scores2.shape == (1, len(hyps))
     distance_range_checker(scores, min_val=0, max_val=2)
 
+
 def test_score_all_with_different_sizes(cosine_metric, distance_range_checker):
     """Test score_all with different sizes for hypotheses and references."""
     hyps = [np.random.rand(768) for _ in range(3)]
@@ -239,33 +242,33 @@ def test_invalid_input_mismatched_embedding_sizes(cosine_metric: EmbeddingDistan
         # TODO: we should probably raise a more descriptive/helpful error/ ValueError
         call_and_call_with_inputs_swapped(hyp, ref, cosine_metric.score)
 
+
 def test_invalid_input_single_number(cosine_metric: EmbeddingDistanceMetric) -> None:
     hyp = random_tensor(768)
-    for ref in range (-2, 2):
+    for ref in range(-2, 2):
         with pytest.raises(IndexError):
             # TODO: we should probably raise a more descriptive/helpful error/ ValueError
             # IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
             call_and_call_with_inputs_swapped(hyp, ref, cosine_metric.score)
 
     logger.info("Invalid input successfully crashed as expected.")
 
+
 def test_invalid_input_noncontainernonnumber_types(cosine_metric: EmbeddingDistanceMetric) -> None:
     hyp = random_tensor(768)
-    invalid_inputs = [
-        "invalid_input", 
-        True
-        ]
+    invalid_inputs = ["invalid_input", True]
     for ref in invalid_inputs:
         with pytest.raises((TypeError, IndexError)):
             # TypeError: new(): invalid data type 'str'
             # but True gives IndexError
             # TODO: better TypeError, more descriptive
             call_and_call_with_inputs_swapped(hyp, ref, cosine_metric.score)
 
+
 def test_invalid_input_empty_containers(cosine_metric: EmbeddingDistanceMetric) -> None:
     """Test the metric with invalid inputs."""
     emb1 = random_tensor(768)
-    invalid_inputs = ["", list(), dict(), tuple(), set()]
+    invalid_inputs = ["", [], {}, tuple(), set()]
 
     for invalid_input in invalid_inputs:
         with pytest.raises((RuntimeError, TypeError, IndexError)):
@@ -295,14 +298,16 @@ def test_score_ndarray_input(cosine_metric):
     score = cosine_metric.score(emb1, emb2)
     assert isinstance(score, float), "Output should be a float."
 
+
 def test_score_all_list_of_lists_of_floats(cosine_metric):
-    """Does a 2D list of floats work? """
+    """Does a 2D list of floats work?"""
     hyps = [[np.random.rand() for _ in range(768)] for _ in range(5)]
     refs = [[np.random.rand() for _ in range(768)] for _ in range(5)]
     scores = cosine_metric.score_all(hyps, refs)
     assert len(scores) == len(hyps), f"Output row count mismatch for torch.Tensor input. Shape:{scores.shape}"
     assert len(scores[0]) == len(refs), f"Output column count mismatch for torch.Tensor input. Shape:{scores.shape}"
 
+
 def test_score_all_list_of_tensor_input(cosine_metric):
     """Test score_all function with List of torch.Tensor inputs."""
     hyps = [torch.rand(768) for _ in range(5)]
@@ -312,14 +317,15 @@ def test_score_all_list_of_tensor_input(cosine_metric):
     assert len(scores) == len(hyps), f"Output row count mismatch for torch.Tensor input. Shape:{scores.shape}"
     assert len(scores[0]) == len(refs), f"Output column count mismatch for torch.Tensor input. Shape:{scores.shape}"
 
+
 def test_score_all_list_of_ndarray_input(cosine_metric):
     """Test score_all function with List of np.ndarray inputs."""
     hyps = [np.random.rand(768) for _ in range(5)]
     refs = [np.random.rand(768) for _ in range(5)]
 
     scores = cosine_metric.score_all(hyps, refs)
     assert len(scores) == len(hyps), f"Output row count mismatch for torch.Tensor input. Shape:{scores.shape}"
-    assert len(scores[0]) == len(refs), f"Output column count mismatch for torch.Tensor input. Shape:{scores.shape}"    
+    assert len(scores[0]) == len(refs), f"Output column count mismatch for torch.Tensor input. Shape:{scores.shape}"
 
 
 def test_device_handling(cosine_metric):