"""Two-ACT relay experiment for ATEC Task E.

Both policies are stepped on every observation so their temporal histories stay
aligned with the actual rollout.  The primary policy controls the reliable
early phase; after the score reaches the configured band, the secondary policy
may take over the late object_1 phase.
"""

from __future__ import annotations

import os

from solution_act import AlgSolution as _ActSolution


class AlgSolution:
    def __init__(self):
        self._primary_path = os.environ.get("ATEC_DUAL_PRIMARY_POLICY", os.environ.get("ATEC_ACT_POLICY_PATH", ""))
        self._secondary_path = os.environ.get("ATEC_DUAL_SECONDARY_POLICY", "")
        if not self._secondary_path:
            raise RuntimeError("ATEC_DUAL_SECONDARY_POLICY is required for solution_dual_act")

        old_policy_path = os.environ.get("ATEC_ACT_POLICY_PATH")
        try:
            if self._primary_path:
                os.environ["ATEC_ACT_POLICY_PATH"] = self._primary_path
            self.primary = _ActSolution()
            os.environ["ATEC_ACT_POLICY_PATH"] = self._secondary_path
            self.secondary = _ActSolution()
        finally:
            if old_policy_path is None:
                os.environ.pop("ATEC_ACT_POLICY_PATH", None)
            else:
                os.environ["ATEC_ACT_POLICY_PATH"] = old_policy_path

        self._switch_score = float(os.environ.get("ATEC_DUAL_SWITCH_SCORE", "12.0"))
        self._switch_act_steps = int(os.environ.get("ATEC_DUAL_SWITCH_ACT_STEPS", "900"))
        self._mode = "primary"
        self._step = 0

    def reset_episode(self):
        self.primary.reset_episode()
        self.secondary.reset_episode()
        self._mode = "primary"
        self._step = 0

    def get_action_spec(self):
        return None

    def predicts(self, obs, current_score):
        self._step += 1
        primary_resp = self.primary.predicts(obs, current_score)
        secondary_resp = self.secondary.predicts(obs, current_score)
        primary_steps = int(getattr(self.primary, "_ts", 0))
        if (
            self._mode == "primary"
            and float(current_score) >= self._switch_score
            and primary_steps >= self._switch_act_steps
        ):
            self._mode = "secondary"
            print(
                f"[DUAL_ACT] switching to secondary score={float(current_score):.2f} "
                f"primary_steps={primary_steps}",
                flush=True,
            )
        return secondary_resp if self._mode == "secondary" else primary_resp