add and fix the

src/gfn/containers/trajectories.py

@@ -5,9 +5,8 @@
 if TYPE_CHECKING:
     from gfn.actions import Actions
     from gfn.env import Env
-    from gfn.states import States, DiscreteStates
+    from gfn.states import States
 
-import numpy as np
 import torch
 
 from gfn.containers.base import Container
@@ -428,6 +427,68 @@ def to_non_initial_intermediary_and_terminating_states(
             conditioning,
         )
 
+    @staticmethod
+    def reverse_backward_trajectories(trajectories: Trajectories) -> Trajectories:
+        """Reverses a backward trajectory"""
+        # FIXME: This method is not compatible with continuous GFN.
+
+        assert trajectories.is_backward, "Trajectories must be backward."
+        new_actions = torch.full(
+            (
+                trajectories.max_length + 1,
+                len(trajectories),
+                *trajectories.actions.action_shape,
+            ),
+            -1,
+        )
+
+        # env.sf should never be None unless something went wrong during class
+        # instantiation.
+        if trajectories.env.sf is None:
+            raise AttributeError(
+                "Something went wrong during the instantiation of environment {}".format(
+                    trajectories.env
+                )
+            )
+
+        new_when_is_done = trajectories.when_is_done + 1
+        new_states = trajectories.env.sf.repeat(
+            new_when_is_done.max() + 1, len(trajectories), 1
+        )
+
+        # FIXME: Can we vectorize this?
+        # FIXME: Also, loop over batch or sequence?
+        for i in range(len(trajectories)):
+            new_actions[trajectories.when_is_done[i], i] = (
+                trajectories.env.n_actions - 1
+            )
+            new_actions[
+                : trajectories.when_is_done[i], i
+            ] = trajectories.actions.tensor[: trajectories.when_is_done[i], i].flip(0)
+
+            new_states[
+                : trajectories.when_is_done[i] + 1, i
+            ] = trajectories.states.tensor[: trajectories.when_is_done[i] + 1, i].flip(
+                0
+            )
+
+        trajectories_states = trajectories.env.States(new_states)
+        trajectories_actions = trajectories.env.Actions(new_actions)
+
+        return Trajectories(
+            env=trajectories.env,
+            states=trajectories_states,
+            conditioning=trajectories.conditioning,
+            actions=trajectories_actions,
+            when_is_done=new_when_is_done,
+            is_backward=False,
+            log_rewards=trajectories.log_rewards,
+            log_probs=None,  # We can't simply pass the trajectories.log_probs
+            # Since `log_probs` is assumed to be the forward log probabilities.
+            # FIXME: To resolve this, we can save log_pfs and log_pbs in the trajectories object.
+            estimator_outputs=None,  # Same as `log_probs`.
+        )
+
 
 def pad_dim0_to_target(a: torch.Tensor, target_dim0: int) -> torch.Tensor:
     """Pads tensor a to match the dimention of b."""