style: fix pre-commit

.pre-commit-config.yaml

@@ -29,7 +29,7 @@ repos:
       - id: debug-statements
       - id: double-quote-string-fixer
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.4.2
+    rev: v0.4.4
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]

omnisafe/adapter/modelbased_adapter.py

@@ -269,8 +269,8 @@ def rollout(  # pylint: disable=too-many-arguments,too-many-locals
 
         update_actor_critic_time = 0.0
         update_dynamics_time = 0.0
-        if use_eval:
-            eval_time = 0.0
+
+        eval_time = 0.0
 
         epoch_steps = 0
 

omnisafe/common/robust_barrier_solver.py

@@ -237,7 +237,6 @@ def get_cbf_qp_constraints(
         mean_pred_batch = torch.unsqueeze(mean_pred_batch, -1).to(self.device)
         sigma_pred_batch = torch.unsqueeze(sigma_pred_batch, -1).to(self.device)
         if self.env.dynamics_mode == 'Unicycle':
-
             num_cbfs = len(self.env.hazards)
             l_p = self.l_p
             buffer = 0.1
@@ -299,6 +298,8 @@ def get_cbf_qp_constraints(
                 .to(self.device)
             )
             q = torch.zeros((batch_size, n_u + 1)).to(self.device)
+        else:
+            raise NotImplementedError
 
         n_u = action_batch.shape[1]
 

omnisafe/envs/safety_gymnasium_modelbased.py

@@ -181,6 +181,8 @@ def get_cost_from_obs_tensor(self, obs: torch.Tensor, is_binary: bool = True) ->
         elif len(obs.shape) == 3:
             batch_size = obs.shape[0] * obs.shape[1]
             hazard_obs = obs[:, :, hazards_key].reshape(batch_size, -1, 2)
+        else:
+            raise NotImplementedError
         hazards_dist = torch.sqrt(torch.sum(torch.square(hazard_obs), dim=2)).reshape(
             batch_size,
             -1,
@@ -497,8 +499,10 @@ def reset(
             self.get_lidar_from_coordinate(flat_coordinate_obs)
             info['obs_original'] = obs_original
             info['goal_met'] = False
-
             obs = torch.as_tensor(flat_coordinate_obs, dtype=torch.float32, device=self._device)
+        else:
+            obs = torch.as_tensor(obs_original, dtype=torch.float32, device=self._device)
+
         return obs, info
 
     def set_seed(self, seed: int) -> None:

omnisafe/utils/plotter.py

@@ -118,8 +118,7 @@ def plot_data(
                 smoothed_x = np.convolve(x, y, 'same') / np.convolve(z, y, 'same')
                 datum['Costs'] = smoothed_x
 
-        if isinstance(data, list):
-            data_to_plot = pd.concat(data, ignore_index=True)
+        data_to_plot = pd.concat(data, ignore_index=True)
         sns.lineplot(
             data=data_to_plot,
             x=xaxis,