from __future__ import annotations

from dataclasses import dataclass
from typing import Any, Dict, Iterable, List, Mapping, Sequence, Tuple

import numpy as np
import pandas as pd


@dataclass(frozen=True)
class StoppingControls:
    target_rank_percentile: float = 1.0
    peak_patience: int = 32
    min_improvement_delta: float = 0.02


@dataclass(frozen=True)
class ExplorationControls:
    stagnation_patience: int = 24
    diversity_boost_strength: float = 0.4
    dominant_window: int = 128


@dataclass(frozen=True)
class UncertaintyControls:
    uncertainty_low_threshold: float = 0.35
    uncertainty_patience: int = 32
    expected_gain_threshold: float = 0.03


def enforce_control_limits(cfg: Mapping[str, Any]) -> None:
    """Enforce compact control surface to prevent parameter explosion."""

    groups = {
        "stopping": set((cfg.get("stopping") or {}).keys()),
        "exploration": set((cfg.get("exploration") or {}).keys()),
        "uncertainty": set((cfg.get("uncertainty") or {}).keys()),
    }
    for name, keys in groups.items():
        if len(keys) > 3:
            raise ValueError(f"{name} parameter count exceeds limit (3): {sorted(keys)}")


def controls_from_config(cfg: Mapping[str, Any]) -> tuple[StoppingControls, ExplorationControls, UncertaintyControls]:
    stop_cfg = cfg.get("stopping") or {}
    exp_cfg = cfg.get("exploration") or {}
    unc_cfg = cfg.get("uncertainty") or {}
    return (
        StoppingControls(
            target_rank_percentile=float(stop_cfg.get("target_rank_percentile", 1.0)),
            peak_patience=int(stop_cfg.get("peak_patience", 32)),
            min_improvement_delta=float(stop_cfg.get("min_improvement_delta", 0.02)),
        ),
        ExplorationControls(
            stagnation_patience=int(exp_cfg.get("stagnation_patience", 24)),
            diversity_boost_strength=float(exp_cfg.get("diversity_boost_strength", 0.4)),
            dominant_window=int(exp_cfg.get("dominant_window", 128)),
        ),
        UncertaintyControls(
            uncertainty_low_threshold=float(unc_cfg.get("uncertainty_low_threshold", 0.35)),
            uncertainty_patience=int(unc_cfg.get("uncertainty_patience", 32)),
            expected_gain_threshold=float(unc_cfg.get("expected_gain_threshold", 0.03)),
        ),
    )


def _safe_float(value: Any, default: float = np.nan) -> float:
    try:
        x = float(value)
        return x if np.isfinite(x) else float(default)
    except Exception:
        return float(default)


def _best_rank_percentile(selected_ids: Sequence[str], truth_map: Mapping[str, Mapping[str, float]]) -> float:
    values: List[float] = []
    for lid in selected_ids:
        info = truth_map.get(str(lid))
        if info is None:
            continue
        values.append(float(info.get("rank_percentile", 100.0)))
    if not values:
        return 100.0
    return float(np.min(np.asarray(values, dtype=float)))


def _dominant_cluster_from_recent(selected_rows: Sequence[dict[str, Any]], window: int) -> int | None:
    if not selected_rows:
        return None
    recent = selected_rows[-max(1, int(window)) :]
    cnt: Dict[int, int] = {}
    for row in recent:
        cid = int(_safe_float(row.get("cluster_id", -1), default=-1))
        if cid < 0:
            continue
        cnt[cid] = cnt.get(cid, 0) + 1
    if not cnt:
        return None
    return int(max(cnt.items(), key=lambda kv: kv[1])[0])


def _expected_gain_proxy(remaining: pd.DataFrame, incumbent_best: float) -> float:
    pred = pd.to_numeric(remaining.get("predicted_score_prebatch", np.nan), errors="coerce").dropna()
    if pred.empty:
        return 0.0
    optimistic = float(np.quantile(pred.to_numpy(dtype=float), 0.05))
    # Lower score is better.
    return float(max(0.0, incumbent_best - optimistic))


def simulate_multifidelity_policy(
    order_df: pd.DataFrame,
    *,
    truth_map: Mapping[str, Mapping[str, float]],
    budget: int,
    min_budget: int,
    max_budget: int,
    wall_time_per_dock: float,
    max_wall_time_seconds: float | None,
    stopping: StoppingControls,
    exploration: ExplorationControls,
    uncertainty: UncertaintyControls,
) -> tuple[pd.DataFrame, pd.DataFrame]:
    """Run compact multifidelity policy simulation on a pre-ranked candidate order.

    Default mode exploits dominant cluster. Diversity fallback is enabled only under
    dominant-cluster stagnation, then automatically disabled.
    """

    if order_df.empty:
        return order_df.copy(), pd.DataFrame()

    cap = int(max(1, min(int(budget), int(max_budget), int(order_df.shape[0]))))
    remaining = order_df.sort_values("step").head(cap).copy().reset_index(drop=True)

    selected_rows: List[dict[str, Any]] = []
    selected_ids: List[str] = []
    trace_rows: List[dict[str, Any]] = []

    cluster_selected_counts: Dict[int, int] = {}
    dominant_cluster: int | None = None
    dominant_best_score = np.inf
    dominant_stagnant_rounds = 0
    diversity_steps_left = 0

    rank_history: List[float] = []
    uncertainty_history: List[float] = []

    stop_reason = "budget_cap_reached"
    for i in range(cap):
        if remaining.empty:
            stop_reason = "pool_exhausted"
            break

        if diversity_steps_left > 0:
            cluster_counts_now = {
                int(_safe_float(c, -1)): int(v) for c, v in cluster_selected_counts.items() if int(c) >= 0
            }
            min_seen = min(cluster_counts_now.values()) if cluster_counts_now else 0
            candidate_clusters = {cid for cid, v in cluster_counts_now.items() if v <= min_seen}
            cand = remaining[remaining["cluster_id"].isin(candidate_clusters)].head(1)
            if cand.empty:
                cand = remaining.head(1)
            selection_mode = "diversity_fallback"
            diversity_steps_left -= 1
        else:
            dominant_cluster = _dominant_cluster_from_recent(selected_rows, exploration.dominant_window)
            if dominant_cluster is not None:
                cand = remaining[remaining["cluster_id"] == dominant_cluster].head(1)
                if cand.empty:
                    cand = remaining.head(1)
                    selection_mode = "dominant_unavailable"
                else:
                    selection_mode = "dominant_exploit"
            else:
                cand = remaining.head(1)
                selection_mode = "initial_explore"

        row = cand.iloc[0].to_dict()
        remaining = remaining.drop(index=int(cand.index[0])).reset_index(drop=True)

        lid = str(row.get("ligand_id"))
        cid = int(_safe_float(row.get("cluster_id", -1), -1))
        dscore = _safe_float(row.get("docking_score", np.nan), np.nan)
        unc = _safe_float(row.get("predicted_uncertainty_prebatch", np.nan), np.nan)

        selected_rows.append(row)
        selected_ids.append(lid)
        cluster_selected_counts[cid] = int(cluster_selected_counts.get(cid, 0) + 1)
        if np.isfinite(unc):
            uncertainty_history.append(float(unc))

        if dominant_cluster is not None and cid == dominant_cluster:
            if np.isfinite(dscore) and (dscore < dominant_best_score - stopping.min_improvement_delta):
                dominant_best_score = float(dscore)
                dominant_stagnant_rounds = 0
            else:
                dominant_stagnant_rounds += 1

        best_rank_pct = _best_rank_percentile(selected_ids, truth_map)
        rank_history.append(best_rank_pct)

        patience = max(2, int(stopping.peak_patience))
        if len(rank_history) >= patience:
            slope = float(rank_history[-patience] - rank_history[-1])
        else:
            slope = np.nan

        incumbent_best = float(np.nanmin(pd.to_numeric(pd.DataFrame(selected_rows)["docking_score"], errors="coerce").to_numpy(dtype=float)))
        expected_gain = _expected_gain_proxy(remaining, incumbent_best)

        u_pat = max(2, int(uncertainty.uncertainty_patience))
        recent_unc = uncertainty_history[-u_pat:] if uncertainty_history else []
        mean_unc = float(np.mean(np.asarray(recent_unc, dtype=float))) if recent_unc else np.nan

        est_wall = float((i + 1) * wall_time_per_dock)
        rank_target_hit = bool(best_rank_pct <= float(stopping.target_rank_percentile))
        slope_stagnation = bool(np.isfinite(slope) and abs(float(slope)) <= float(stopping.min_improvement_delta))
        uncertainty_low = bool(np.isfinite(mean_unc) and mean_unc <= float(uncertainty.uncertainty_low_threshold))
        gain_low = bool(float(expected_gain) <= float(uncertainty.expected_gain_threshold))
        dominant_plateau = bool(dominant_stagnant_rounds >= int(exploration.stagnation_patience))

        allow_stop = bool((i + 1) >= int(min_budget))
        stop_now = False

        if allow_stop and rank_target_hit and slope_stagnation and gain_low and uncertainty_low:
            stop_now = True
            stop_reason = "early_peak_dynamic_stop"
        elif max_wall_time_seconds is not None and np.isfinite(float(max_wall_time_seconds)) and est_wall >= float(max_wall_time_seconds):
            stop_now = True
            stop_reason = "max_wall_time_reached"

        # Diversity fallback is only activated on dominant-cluster stagnation.
        fallback_triggered = False
        if (
            not stop_now
            and diversity_steps_left <= 0
            and dominant_plateau
            and (i + 1) >= int(min_budget)
            and not rank_target_hit
        ):
            diversity_steps_left = max(1, int(round(8.0 * float(exploration.diversity_boost_strength))))
            fallback_triggered = True

        trace_rows.append(
            {
                "step": int(i),
                "ligand_id": lid,
                "selection_mode": selection_mode,
                "stop_reason": stop_reason if stop_now else "",
                "best_rank_percentile": float(best_rank_pct),
                "rank_improvement_slope": float(slope) if np.isfinite(slope) else np.nan,
                "expected_gain_proxy": float(expected_gain),
                "mean_uncertainty": float(mean_unc) if np.isfinite(mean_unc) else np.nan,
                "rank_target_hit": bool(rank_target_hit),
                "slope_stagnation": bool(slope_stagnation),
                "uncertainty_low": bool(uncertainty_low),
                "dominant_plateau": bool(dominant_plateau),
                "fallback_triggered": bool(fallback_triggered),
                "diversity_steps_left": int(diversity_steps_left),
                "dominant_cluster": int(dominant_cluster) if dominant_cluster is not None else -1,
                "dynamic_budget_cap": int(cap),
                "estimated_wall_seconds": float(est_wall),
            }
        )

        if stop_now:
            break

    selected_df = pd.DataFrame(selected_rows).reset_index(drop=True)
    if not selected_df.empty:
        selected_df = selected_df.copy()
        selected_df["step"] = np.arange(selected_df.shape[0], dtype=int)
    trace_df = pd.DataFrame(trace_rows)
    return selected_df, trace_df