"""Evaluation protocol (Sec 5.2 / App C-D): pairwise win-rate vs expert reference
with 3 judges x 2 order-swaps (6 votes/dim), MOScore (Eq 14), and computable
novelty / diversity metrics (all-MiniLM-L6-v2)."""
from __future__ import annotations
import numpy as np
from . import llm
from .tournament import judge_stage, PF_DIMS, PS_DIMS
from .flow import idea_to_text, motivation_text
from . import supernet as _sn

# MOScore weights (NOT STATED in paper -> uniform, documented)
W_PF = np.array([1 / 3, 1 / 3, 1 / 3])
W_PS = np.array([1 / 3, 1 / 3, 1 / 3])


def moscore(s, w):
    s = np.clip(np.asarray(s, dtype=float), 1e-6, 1.0)
    w = np.asarray(w, dtype=float)
    w = w / w.sum()
    arith = float((w * s).sum())
    geo = float(np.prod(s ** w))
    return 0.5 * (arith + geo)


def _method_win(winner, method_is):
    """winner is 'A'/'B'/None; method_is is which slot the method occupies."""
    if winner is None:
        return 0.5
    return 1.0 if winner == method_is else 0.0


def winrate_vs_reference(method_idea, ref_idea, context, judges, seed=0):
    """Return dict of 6 dims -> win-rate in [0,1] via 3 judges x 2 order swaps.
    The (judge, order, stage) calls are independent -> run concurrently."""
    votes = {"PF_" + d: [] for d in PF_DIMS}
    votes.update({"PS_" + d: [] for d in PS_DIMS})
    tasks = []  # (ji, swap, stage)
    for ji, jm in enumerate(judges):
        for swap in (False, True):
            for stage in ("motivation", "idea"):
                tasks.append((ji, jm, swap, stage))

    def _do(t):
        ji, jm, swap, stage = t
        A, B = (ref_idea, method_idea) if swap else (method_idea, ref_idea)
        off = 7 if stage == "idea" else 0
        res = judge_stage(A, B, context, stage, model=jm, seed=seed + ji * 13 + off + swap)
        return (swap, stage, res)

    outs = llm.parallel_map(_do, tasks, workers=min(12, len(tasks)))
    for swap, stage, res in outs:
        method_is = "B" if swap else "A"
        dims = PF_DIMS if stage == "motivation" else PS_DIMS
        pref = "PF_" if stage == "motivation" else "PS_"
        for d in dims:
            votes[pref + d].append(_method_win(res.get(d), method_is))
    return {k: float(np.mean(v)) for k, v in votes.items()}


def aggregate_winrates(per_query_winrates):
    """per_query_winrates: list of dicts -> mean per dim + MOScores + overall."""
    dims = list(per_query_winrates[0].keys())
    mean = {d: float(np.mean([q[d] for q in per_query_winrates])) for d in dims}
    s_pf = [mean["PF_Novelty"], mean["PF_Significance"], mean["PF_Timeliness"]]
    s_ps = [mean["PS_Novelty"], mean["PS_Effectiveness"], mean["PS_Feasibility"]]
    mo_pf = moscore(s_pf, W_PF)
    mo_ps = moscore(s_ps, W_PS)
    overall = 0.5 * (mo_pf + mo_ps)
    out = dict(mean)
    out["MOScore_PF"] = mo_pf
    out["MOScore_PS"] = mo_ps
    out["Overall"] = overall
    return out


# ---- computable metrics -------------------------------------------------------

def _emb(texts):
    return _sn.encode(texts, normalize=True)


def novelty(idea, related_works):
    """1 - mean cosine of (motivation, method) vs related works (Eq 17-18)."""
    if not related_works:
        return float("nan")
    rw = _emb(list(related_works))
    mot = _emb([motivation_text(idea)])[0]
    met = _emb([idea.get("method", "")])[0]
    n_m = 1.0 - float(np.mean(rw @ mot))
    n_s = 1.0 - float(np.mean(rw @ met))
    return 0.5 * (n_m + n_s)


def diversity(ideas):
    """1 - mean pairwise cosine among ideas (motivation & method averaged)."""
    if len(ideas) < 2:
        return float("nan")
    mot = _emb([motivation_text(i) for i in ideas])
    met = _emb([i.get("method", "") for i in ideas])
    def _div(E):
        S = E @ E.T
        n = len(E)
        off = (S.sum() - np.trace(S)) / (n * (n - 1))
        return 1.0 - float(off)
    return 0.5 * (_div(mot) + _div(met))