linting

tests/policies/vla_jepa/test_vla_jepa.py

@@ -17,7 +17,6 @@ from lerobot.policies.vla_jepa.configuration_vla_jepa import VLAJEPAConfig
 from lerobot.policies.vla_jepa.modeling_vla_jepa import VLAJEPAPolicy
 from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
 
-
 pytestmark = pytest.mark.filterwarnings(
     "ignore:In CPU autocast, but the target dtype is not supported:UserWarning"
 )
@@ -81,10 +80,7 @@ class _FakeQwenInterface(nn.Module):
 
     def expand_tokenizer(self) -> tuple[list[str], list[int], int]:
         max_action_tokens = self.config.chunk_size * self.config.num_action_tokens_per_timestep
-        action_tokens = [
-            self.config.special_action_token.format(idx)
-            for idx in range(max_action_tokens)
-        ]
+        action_tokens = [self.config.special_action_token.format(idx) for idx in range(max_action_tokens)]
         action_token_ids = list(range(1000, 1000 + max_action_tokens))
         return action_tokens, action_token_ids, 2000
 
@@ -226,9 +222,7 @@ def test_vla_jepa_training_forward_pass(patch_vla_jepa_external_models: None) ->
 
     loss.backward()
     assert any(
-        param.grad is not None
-        for param in policy.model.action_model.parameters()
-        if param.requires_grad
+        param.grad is not None for param in policy.model.action_model.parameters() if param.requires_grad
     )
     assert set(batch) == set(batch_before)
     for key, value in batch.items():
@@ -299,9 +293,7 @@ def test_vla_jepa_pretrained_checkpoint_loads_from_hf_cache() -> None:
             local_files_only=True,
         )
     except LocalEntryNotFoundError:
-        pytest.skip(
-            f"{repo_id}/{checkpoint_filename} is not available in the local Hugging Face cache."
-        )
+        pytest.skip(f"{repo_id}/{checkpoint_filename} is not available in the local Hugging Face cache.")
 
     try:
         checkpoint = torch.load(checkpoint_path, map_location="cpu", mmap=True, weights_only=False)