pre-commit cleanup

tests/policies/vla_jepa/test_vla_jepa.py

@@ -403,9 +403,9 @@ def test_postprocessor_unnormalizes_actions(patch_vla_jepa_external_models: None
 def test_postprocessor_clip_clamps_before_unnorm(patch_vla_jepa_external_models: None) -> None:
     """ClipActionsProcessorStep clamps to [-1, 1] before unnormalization."""
     from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+    from lerobot.policies.vla_jepa.processor_vla_jepa import ClipActionsProcessorStep
     from lerobot.processor import UnnormalizerProcessorStep
     from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action
-    from lerobot.policies.vla_jepa.processor_vla_jepa import ClipActionsProcessorStep
     from lerobot.utils.constants import ACTION
 
     dataset_stats = _make_dataset_stats()
@@ -466,6 +466,7 @@ def test_postprocessor_applied_after_predict_action_chunk(
     # Postprocessor applies unnormalization: 0 → (0+1)/2 * (max-min) + min = 5 + i
     unnormed = postprocessor(chunk)
     from lerobot.utils.constants import ACTION
+
     a_min = dataset_stats[ACTION]["min"].numpy()
     a_max = dataset_stats[ACTION]["max"].numpy()
     expected_first = 0.5 * (0.0 + 1.0) * (a_max[0] - a_min[0]) + a_min[0]

tests/policies/vla_jepa/test_world_model.py

@@ -43,7 +43,9 @@ def _make_predictor(
     ],
 )
 def test_predictor_output_shape(batch: int, num_steps: int, tokens_per_frame: int, embed_dim: int) -> None:
-    predictor = _make_predictor(embed_dim=embed_dim, action_embed_dim=_ACTION_EMBED_DIM, tokens_per_frame=tokens_per_frame)
+    predictor = _make_predictor(
+        embed_dim=embed_dim, action_embed_dim=_ACTION_EMBED_DIM, tokens_per_frame=tokens_per_frame
+    )
     frame_tokens = torch.randn(batch, num_steps * tokens_per_frame, embed_dim)
     action_tokens = torch.randn(batch, num_steps * 2, _ACTION_EMBED_DIM)
     out = predictor(frame_tokens, action_tokens)