fix error in backward_masks

src/gfn/env.py

@@ -449,7 +449,7 @@ def reset(
         return states
 
     @abstractmethod
-    def update_masks(self, states: type[States]) -> None:
+    def update_masks(self, states: States) -> None:
         """Updates the masks in States.
 
         Called automatically after each step for discrete environments.

src/gfn/gym/discrete_ebm.py

@@ -113,7 +113,7 @@ def __init__(
             preprocessor=preprocessor,
         )
 
-    def update_masks(self, states: type[States]) -> None:
+    def update_masks(self, states: States) -> None:
         states.forward_masks[..., : self.ndim] = states.tensor == -1
         states.forward_masks[..., self.ndim : 2 * self.ndim] = states.tensor == -1
         states.forward_masks[..., -1] = torch.all(states.tensor != -1, dim=-1)

src/gfn/gym/hypergrid.py

@@ -82,7 +82,7 @@ def __init__(
             preprocessor=preprocessor,
         )
 
-    def update_masks(self, states: type[DiscreteStates]) -> None:
+    def update_masks(self, states: DiscreteStates) -> None:
         """Update the masks based on the current states."""
         # Not allowed to take any action beyond the environment height, but
         # allow early termination.

src/gfn/samplers.py

@@ -358,7 +358,6 @@ def local_search(
         )  # Episodic reward is assumed.
         trajectories_logprobs = None  # TODO
 
-        len_recon = recon_trajectories.states.tensor.shape[0]
         for i in range(bs):
             # Backward part
             back_source_idx = backward_trajectories.when_is_done[i]
@@ -373,10 +372,11 @@ def local_search(
                 back_source_idx - n_back : back_source_idx, i
             ].flip(0)
 
+            len_recon = recon_trajectories.when_is_done[i] + 1
             # Forward part
             trajectories_states_tsr[
                 n_back : n_back + len_recon, i
-            ] = recon_trajectories.states.tensor[:, i]
+            ] = recon_trajectories.states.tensor[:len_recon, i]
             trajectories_actions_tsr[
                 n_back : n_back + len_recon - 1, i
             ] = recon_trajectories.actions.tensor[: len_recon - 1, i]
@@ -385,7 +385,7 @@ def local_search(
 
         new_trajectories = Trajectories(
             env=env,
-            states=env.States(trajectories_states_tsr),
+            states=env.states_from_tensor(trajectories_states_tsr),
             conditioning=conditioning,
             actions=env.Actions(trajectories_actions_tsr),
             when_is_done=trajectories_dones,