"""ZenithBattleSnake_ClaudeOpus5_5 v1.2.0

Built on PrismBattleSnake_GPT_5_6_Sol v1.5.0. Prism's bitboard engine, duel
search, and adversarial rollouts are inherited unchanged.

Prism only runs the exact duel search on near-tied heuristic candidates
(score gap <= 8). A move outside that window is never checked against every
enemy reply, so a forced loss with a strong heuristic score can still be
played, and a forced win with a weaker score can be skipped.

Z1: Root verdict pass — after Prism decides, every safe duel candidate is
    resolved against all simultaneous enemy replies with the shared
    transposition table (cheap: most subtrees are already cached).
Z2: Proven-loss veto — a candidate whose completed search proves a loss is
    replaced by the best non-losing candidate.
Z3: Proven-win override — a candidate whose completed search proves a win is
    played even if the heuristic preferred another move.
Z4: Elapsed-time rollout gate — Zenith keeps a larger timeout reserve than
    Prism. The inherited multiplayer "deepen when > 250 ms remain" gate is
    lowered by that extra reserve, so 3-snake rollouts deepen after the same
    elapsed time as Prism instead of never reaching depth 3. The duel gate is
    left alone: the verdict pass already spends the duel budget.
"""
from time import perf_counter

from snakes.engine.duel_search import BitboardDuelSearch
from snakes.strategies.zenith_survival import ZenithSurvivalMixin
from snakes.strategies.prism import PrismBattleSnake_GPT_5_6_Sol

# A completed search value beyond half of WIN/LOSS can only come from a terminal.
_PROVEN_WIN = BitboardDuelSearch.WIN / 2
_PROVEN_LOSS = BitboardDuelSearch.LOSS / 2
# Reserve kept for Apex's history bookkeeping after the verdict pass.
_VERDICT_RESERVE_MS = 25.0
_ZENITH_TIMEOUT_BUFFER_MS = 180

class ZenithBattleSnake_ClaudeOpus5_5(ZenithSurvivalMixin, PrismBattleSnake_GPT_5_6_Sol):
  VERSION = "1.2.0"

  def __init__(self) -> None:
    super().__init__()
    self.name = "ZenithBattleSnake"
    self.version = self.VERSION
    # Z4: deepen after the same elapsed time as the inherited Prism gates.
    extra_reserve = self._get_timeout_buffer_ms() - super()._get_timeout_buffer_ms()
    self._rollout_deepen_ms = max(120.0, self._rollout_deepen_ms - extra_reserve)

  def _get_timeout_buffer_ms(self):
    # Leave room for executor scheduling, history, JSON and network delivery.
    return max(_ZENITH_TIMEOUT_BUFFER_MS, super()._get_timeout_buffer_ms())

  def _choose_duel_move(
    self, safe_moves, my_body, my_len, my_health, other_snakes, food_set,
    hazard_set, hazard_damage, hazard_count, previous_hazard_set,
    enemy_attack_map, enemy_can_grow, total_occupancy, width, height, deadline,
  ):
    best_move, scores, depth = super()._choose_duel_move(
      safe_moves=safe_moves, my_body=my_body, my_len=my_len,
      my_health=my_health, other_snakes=other_snakes, food_set=food_set,
      hazard_set=hazard_set, hazard_damage=hazard_damage,
      hazard_count=hazard_count, previous_hazard_set=previous_hazard_set,
      enemy_attack_map=enemy_attack_map, enemy_can_grow=enemy_can_grow,
      total_occupancy=total_occupancy, width=width, height=height,
      deadline=deadline,
    )
    if len(safe_moves) < 2:
      return best_move, scores, depth

    verdicts = self._duel_verdicts(
      safe_moves=safe_moves, my_body=my_body, enemy=other_snakes[0],
      my_health=my_health, food_set=food_set, hazard_set=hazard_set,
      hazard_damage=hazard_damage, hazard_count=hazard_count,
      previous_hazard_set=previous_hazard_set, width=width, height=height,
      deadline=deadline,
    )
    chosen = self._apply_verdicts(best_move, scores, verdicts)
    if chosen != best_move:
      self.add_to_history({
        "turn": self.game_board.get_turn(), "mode": "duel",
        "zenith_verdict_changed_move": True,
        "from": best_move, "to": chosen,
        "verdicts": {m: round(v, 1) for m, (v, _) in verdicts.items()},
      })
    depth = max([depth, *(d for _, d in verdicts.values())])
    return chosen, scores, depth

  def _duel_verdicts(
    self, safe_moves, my_body, enemy, my_health, food_set, hazard_set,
    hazard_damage, hazard_count, previous_hazard_set, width, height, deadline,
  ) -> dict[str, tuple[float, int]]:
    """Return completed exact-search values per candidate (Z1)."""
    verdicts: dict[str, tuple[float, int]] = {}
    # Search every root at equal depth. A full iterative search for the first
    # move could consume the deadline before the other roots were visited.
    search_deadline = None if deadline is None else deadline - _VERDICT_RESERVE_MS / 1000
    if search_deadline is not None and perf_counter() >= search_deadline:
      return verdicts
    search = self._new_duel_search(
      food_set, hazard_set, hazard_count, hazard_damage, width, height, search_deadline,
    )
    search.deadline = search_deadline
    for depth in range(1, self._planning_depth + 1):
      layer = {}
      for move, pos in safe_moves.items():
        value, completed = search.search_candidate(
          my_body, enemy["body"], (pos["x"], pos["y"]),
          my_health, enemy.get("health", 100), depth, previous_hazard_set,
        )
        if completed != depth:
          return verdicts
        layer[move] = (value, completed)
      verdicts = layer
    return verdicts

  @staticmethod
  def _apply_verdicts(
    best_move: str, scores: dict[str, float],
    verdicts: dict[str, tuple[float, int]],
  ) -> str:
    """Apply proven-win override (Z3) and proven-loss veto (Z2)."""
    def rank(move: str) -> tuple[float, float]:
      return verdicts[move][0], scores.get(move, float("-inf"))

    wins = [m for m, (v, _) in verdicts.items() if v >= _PROVEN_WIN]
    if wins and best_move not in wins:
      return max(wins, key=rank)

    best_verdict = verdicts.get(best_move)
    if best_verdict is None or best_verdict[0] > _PROVEN_LOSS:
      return best_move
    alternatives = [m for m, (v, _) in verdicts.items() if v > _PROVEN_LOSS]
    if not alternatives:
      return best_move
    # Prefer Prism's heuristic among surviving moves; fall back to search value.
    return max(alternatives, key=lambda m: (scores.get(m, float("-inf")), verdicts[m][0]))