"""Eleusis game state machine — pure logic, runs inside Pyodide.

Two variants share one engine:
  - "hf":    the 26-rule benchmark eval set; 10 turns; played card is replaced
             by a draw (the recorded model games are rl4-consistency-step30).
  - "small": the simple-rules era; 6 held-out rules x 4 deals; 8 turns; no
             redraws (recorded games are the step-90 LoRA of the hosted run).

All rule evaluation and guess-checking uses the engine extracted verbatim
from the eleusis environment, so the human faces exactly the checks the
model faced.
"""

import json
import random

from cards import parse_card, deck
from engine import compile_python_rule, guess_matches_target

VARIANTS = {
    "hf": {"max_turns": 10, "redraw": True},
    "small": {"max_turns": 8, "redraw": False},
}

GAMES = {}
STATE = None


def load_games(variant: str, payload: str) -> int:
    GAMES[variant] = json.loads(payload)
    return len(GAMES[variant])


def new_game(variant: str) -> str:
    global STATE
    cfg = VARIANTS[variant]
    games = GAMES[variant]
    task_id = random.choice(list(games.keys()))
    info = games[task_id]
    starter, hand, draws = info["starter"], list(info["hand"]), list(info.get("draws", []))
    if cfg["redraw"]:
        used = {starter, *hand, *draws}
        fallback = [c for c in deck() if c not in used]
        random.Random(task_id).shuffle(fallback)
        draws = draws + fallback
    STATE = {
        "variant": variant,
        "task_id": task_id,
        "hand": hand,
        "mainline": [starter],
        "board": [{"card": starter, "rejects": []}],
        "turn": 0,
        "draws": draws,
        "solved": False,
        "over": False,
        "log": [],
    }
    return _public_state()


def _info():
    return GAMES[STATE["variant"]][STATE["task_id"]]


def _cfg():
    return VARIANTS[STATE["variant"]]


def play_turn(selected_card: str, guess_text: str, no_play: bool) -> str:
    if STATE is None:
        return json.dumps({"error": "no game"})
    s = STATE
    cfg = _cfg()
    if s["over"]:
        return _public_state()
    if not no_play and (not selected_card or selected_card not in s["hand"]):
        return _public_state("Pick a card from your hand first (or declare no-play).")

    target = compile_python_rule(_info()["code"])
    s["turn"] += 1
    mainline_cards = [parse_card(c) for c in s["mainline"]]
    guess = (guess_text or "").strip()
    guess_correct = bool(guess) and guess_matches_target(guess, target)
    guess_error = ""
    if guess and not guess_correct:
        try:
            compile_python_rule(guess)
        except Exception as exc:
            guess_error = f"guess did not compile: {exc}"

    if no_play:
        would_accept = any(target(parse_card(c), mainline_cards) for c in s["hand"])
        result = "missed_play" if would_accept else "correct_no_play"
        card = None
    else:
        card = selected_card
        accepted = target(parse_card(card), mainline_cards)
        s["hand"].remove(card)
        if accepted:
            s["mainline"].append(card)
            s["board"].append({"card": card, "rejects": []})
            result = "accepted"
        else:
            s["board"][-1]["rejects"].append(card)
            result = "rejected"
        if cfg["redraw"] and s["draws"]:
            s["hand"].append(s["draws"].pop(0))

    s["log"].append({"card": card, "result": result, "guess": guess,
                     "guess_correct": guess_correct, "guess_error": guess_error})

    if guess_correct:
        s["solved"], s["over"] = True, True
    elif s["turn"] >= cfg["max_turns"]:
        s["over"] = True
    return _public_state()


def give_up() -> str:
    if STATE is None:
        return json.dumps({"error": "no game"})
    STATE["over"] = True
    return _public_state()


def _public_state(message: str = "") -> str:
    assert STATE is not None
    s: dict = STATE
    out = {k: s[k] for k in ("variant", "hand", "board", "turn", "solved", "over", "log")}
    out["max_turns"] = _cfg()["max_turns"]
    out["message"] = message
    info = _info()
    out["model_solved_count"] = sum(e["solved"] for e in info["episodes"])
    out["model_attempts"] = [
        {"solved": e["solved"], "turns_used": e["turns_used"]} for e in info["episodes"]
    ]
    if s["over"]:
        out["rule_label"] = info["label"]
        out["rule_code"] = info["code"]
        solved_turns = [e["turns_used"] for e in info["episodes"] if e["solved"]]
        out["model_best_turns"] = min(solved_turns) if solved_turns else None
        out["score"] = (_cfg()["max_turns"] + 1 - s["turn"]) if s["solved"] else 0
    return json.dumps(out)


def reveal_attempt(idx: int) -> str:
    if STATE is None:
        return json.dumps({"error": "no game"})
    info = _info()
    ep = info["episodes"][int(idx)]
    return json.dumps({
        "index": int(idx),
        "solved": ep["solved"],
        "turns_used": ep["turns_used"],
        "starter": info["starter"],
        "turns": ep["turns"],
        "model_solved_count": sum(e["solved"] for e in info["episodes"]),
    })