"""Hebrew Codenames as a probe for cross-model semantic alignment.

The game is a measurement instrument: a one-word clue transduces a *target set*
into a *guess*. We put two semantic systems on that channel —
  - modern Hebrew ENCODERS (the geometry: cosine over word embeddings), and
  - a Hebrew LLM (the intent: clue-giving / guessing in natural language) —
and read off where their notions of "what this clue points at" agree and diverge.

NOTE on what this measures (Koyyalagunta et al. 2021, critiquing Kim et al. 2019):
agreement between a clue-giver and a guesser that share an embedding is trivially
high; cross-system agreement measures *cooperation / alignment*, NOT clue quality.
So our headline number is an alignment score — with the LLM standing in as the
"human-like intent" reference (Kumar et al. 2021: distributional cosine
systematically under-predicts human word association). The divergences are the finding.

Two directions:
  LLM -> Encoder : LLM gives a clue + names its targets; does the encoder's
                   nearest-neighbour guess recover them?     (intent recovery)
  Encoder -> LLM : encoder picks the best-scoring clue; does the LLM rank its
                   intended targets on top?                  (geometry legibility)

Headline scalar: per clue, Spearman rho between the encoder's cosine ordering of
the 25 board words and the LLM's ordering, averaged over rounds.
"""

from __future__ import annotations

import os
import re
import json
import random
from dataclasses import dataclass, field

import numpy as np

import morph
from deck_he import DECK

DATA = os.path.join(os.path.dirname(__file__), "data")

# --------------------------------------------------------------------------- #
# The bench
# --------------------------------------------------------------------------- #

ENCODERS = {
    # Static subword vectors — the literature-recommended baseline for Hebrew
    # (morphology/OOV); often competitive with contextual encoders for bare-word
    # association. Handles OOV via subwords.
    "fasttext":      dict(kind="fasttext", path=os.path.join(DATA, "cc.he.300.bin")),
    # Hebrew-native, newest Dicta encoder (needs transformers<5).
    "neodictabert":  dict(kind="st", model_id="dicta-il/neodictabert-bilingual-embed"),
    # 2025 multilingual SOTA-small.
    "embeddinggemma": dict(kind="st", model_id="google/embeddinggemma-300m"),
    "qwen3-embed":   dict(kind="st", model_id="Qwen/Qwen3-Embedding-0.6B"),
}

# DictaLM 3.0 (2026-05) via MLX. Swap to the 12B for the quality run.
LLM_FAST = "ssdataanalysis/DictaLM-3.0-1.7B-Instruct-mlx-8Bit"
LLM_BIG  = "ssdataanalysis/DictaLM-3.0-Nemotron-12B-Instruct-mlx-8Bit"

# Standard Codenames split: 25 words, 9 / 8 / 7 / 1.
N_BOARD, N_MY, N_OPP, N_NEUTRAL, N_ASSASSIN = 25, 9, 8, 7, 1


# --------------------------------------------------------------------------- #
# Encoders
# --------------------------------------------------------------------------- #

def _device():
    import torch
    if torch.backends.mps.is_available():
        return "mps"
    if torch.cuda.is_available():
        return "cuda"
    return "cpu"


class Encoder:
    """Embeds bare Hebrew words to L2-normalised vectors (cosine == dot).

    Loads via sentence-transformers when possible; otherwise a raw AutoModel
    with mean pooling over the last hidden state.
    """

    def __init__(self, model_id: str):
        self.model_id = model_id
        self._st = None
        self._tok = self._model = None
        dev = _device()
        try:
            from sentence_transformers import SentenceTransformer
            self._st = SentenceTransformer(model_id, device=dev, trust_remote_code=True)
        except Exception:
            import torch
            from transformers import AutoTokenizer, AutoModel
            self._tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
            self._model = AutoModel.from_pretrained(model_id, trust_remote_code=True).to(dev).eval()
            self._dev = dev

    def embed(self, words) -> np.ndarray:
        words = list(words)
        if self._st is not None:
            V = self._st.encode(words, normalize_embeddings=True,
                                convert_to_numpy=True, show_progress_bar=False)
            return np.nan_to_num(V, nan=0.0, posinf=0.0, neginf=0.0)
        import torch
        out = []
        with torch.no_grad():
            for i in range(0, len(words), 64):
                batch = words[i:i + 64]
                enc = self._tok(batch, padding=True, truncation=True, return_tensors="pt").to(self._dev)
                hs = self._model(**enc).last_hidden_state
                mask = enc["attention_mask"].unsqueeze(-1).float()
                mean = (hs * mask).sum(1) / mask.sum(1).clamp(min=1e-9)
                mean = torch.nn.functional.normalize(mean, p=2, dim=1)
                out.append(mean.cpu().numpy())
        return np.nan_to_num(np.vstack(out), nan=0.0, posinf=0.0, neginf=0.0)


class FastTextEncoder:
    """Static fastText subword vectors (OOV-safe). L2-normalised."""

    def __init__(self, path: str):
        import fasttext
        self.model_id = os.path.basename(path)
        self._m = fasttext.load_model(path)

    def embed(self, words) -> np.ndarray:
        V = np.stack([self._m.get_word_vector(w) for w in words]).astype(np.float32)
        V /= (np.linalg.norm(V, axis=1, keepdims=True) + 1e-9)
        return V


class CompressedFastTextEncoder:
    """A compress-fasttext model (pruned vocab/ngrams + fp16). Same geometry as the full
    cc.he.300.bin (validated loss-free) at ~20x smaller — keeps subword OOV. L2-normalised."""

    def __init__(self, path: str):
        import compress_fasttext
        self.model_id = os.path.basename(path)
        self._m = compress_fasttext.models.CompressedFastTextKeyedVectors.load(path)

    def embed(self, words) -> np.ndarray:
        V = np.stack([self._m[w] for w in words]).astype(np.float32)
        V /= (np.linalg.norm(V, axis=1, keepdims=True) + 1e-9)
        return V


def make_encoder(key: str):
    cfg = ENCODERS[key]
    if cfg["kind"] == "fasttext":
        # Deploy uses the compressed model when FASTTEXT_COMPRESSED points at one; local dev
        # falls back to the full cc.he.300.bin.
        comp = os.environ.get("FASTTEXT_COMPRESSED")
        if comp:
            return CompressedFastTextEncoder(comp)
        return FastTextEncoder(cfg["path"])
    return Encoder(cfg["model_id"])


# --------------------------------------------------------------------------- #
# Clue vocabulary (large, frequency-filtered — clues are NOT drawn from the deck)
# --------------------------------------------------------------------------- #

_HEB_LETTERS = re.compile(r"[א-ת]+$")  # letters incl. final forms, no niqqud/punct


def load_clue_vocab(n: int = 12000, min_len: int = 2, max_len: int = 12, path: str | None = None):
    """Top-n Hebrew words from a frequency list (FrequencyWords `word count` format).

    The only filters are validity, not tuning: pure Hebrew letters (no digits/punct)
    and a sane length. No stopword list / frequency-band skip — broadly-similar common
    words are suppressed by the per-clue mean-centering in `encoder_spymaster`, not by
    hand-maintained lists."""
    path = path or os.path.join(DATA, "he_freq_50k.txt")
    out, seen = [], set()
    with open(path, encoding="utf-8") as f:
        for line in f:
            w = line.split(" ")[0].strip()
            if w in seen or not (min_len <= len(w) <= max_len) or not _HEB_LETTERS.match(w):
                continue
            seen.add(w); out.append(w)
            if len(out) >= n:
                break
    return out


def load_clue_vocab_content(n: int = 1500, source_n: int = 6000, cache: str | None = None):
    """A clue vocabulary of **content-word lemmas**: take the frequency list, keep only
    content POS (noun/adj/verb/proper — drops prepositions, pronouns, conjunctions, adverbs
    via DictaBERT-morph), reduce each to its lemma and de-duplicate (so בתי/בבית/בית collapse
    to בית). Principled clue-quality filter — no stopword list. Cached to disk (computed once)."""
    cache = cache or os.path.join(DATA, f"clue_vocab_content_{n}.json")
    if os.path.exists(cache):
        with open(cache, encoding="utf-8") as f:
            return json.load(f)
    raw = load_clue_vocab(source_n, min_len=2)
    parts = morph.pos(raw)
    lems = morph.lemmas(raw)
    out, seen = [], set()
    for w, p, lem in zip(raw, parts, lems):
        if p not in morph.CONTENT_POS or not _HEB_LETTERS.match(lem) or len(lem) < 2:
            continue
        if lem in seen:
            continue
        seen.add(lem); out.append(lem)            # the lemma is the clue word
        if len(out) >= n:
            break
    with open(cache, "w", encoding="utf-8") as f:
        json.dump(out, f, ensure_ascii=False)
    return out


# --------------------------------------------------------------------------- #
# DETECT-style frequency (Koyyalagunta et al. 2021): a clue should be a *mid*-frequency
# word — neither obscure (rare → bad clue) nor over-common (generic / conversational →
# bad clue). We apply it twice: at vocab-build time (keep only the mid band) and as a
# soft term in the scoring function. Replaces the old "take the most frequent content
# words" pool, whose top is dominated by dialogue verbs (רוצה / יודע / חושב).
# --------------------------------------------------------------------------- #

_FREQ: dict[str, int] | None = None


def load_freqs(path: str | None = None) -> dict[str, int]:
    """Surface-form -> corpus count from the frequency list (loaded once)."""
    global _FREQ
    if _FREQ is None:
        path = path or os.path.join(DATA, "he_freq_50k.txt")
        d: dict[str, int] = {}
        with open(path, encoding="utf-8") as f:
            for line in f:
                p = line.split()
                if len(p) >= 2 and _HEB_LETTERS.match(p[0]):
                    d.setdefault(p[0], int(p[1]))
        _FREQ = d
    return _FREQ


def content_lemma_master(source_n: int = 14000, cache: str | None = None):
    """All content-word lemmas within the top `source_n` of the frequency list, each as
    [lemma, count, pos], sorted by count desc. The DictaBERT POS+lemma pass runs once and
    is cached; clue-vocab bands (by frequency and/or POS) are sliced from this cheaply."""
    cache = cache or os.path.join(DATA, f"content_master_v2_{source_n}.json")
    if os.path.exists(cache):
        return json.load(open(cache, encoding="utf-8"))
    raw, cnt = [], {}
    with open(os.path.join(DATA, "he_freq_50k.txt"), encoding="utf-8") as f:
        for line in f:
            p = line.split()
            if len(p) >= 2 and _HEB_LETTERS.match(p[0]) and 2 <= len(p[0]) <= 12:
                if p[0] not in cnt:
                    raw.append(p[0]); cnt[p[0]] = int(p[1])
            if len(raw) >= source_n:
                break
    parts = morph.pos(raw); lems = morph.lemmas(raw)
    best: dict[str, tuple[int, str]] = {}
    for w, p, lem in zip(raw, parts, lems):
        if p not in morph.CONTENT_POS or not _HEB_LETTERS.match(lem) or len(lem) < 2:
            continue
        c = cnt[w]
        if c > best.get(lem, (0, ""))[0]:
            best[lem] = (c, p)
    data = sorted(([lem, c, p] for lem, (c, p) in best.items()), key=lambda r: -r[1])
    json.dump(data, open(cache, "w", encoding="utf-8"), ensure_ascii=False)
    return data


def clue_vocab_band(n: int = 1800, lo: int = 200, hi: int = 60000,
                    pos: set[str] | None = None, source_n: int = 14000, min_len: int = 3):
    """Clue vocab from a frequency BAND of content lemmas: drop over-common conversational
    words (count > hi) and obscure words (count < lo). `pos` optionally restricts the part
    of speech (e.g. {'NOUN','ADJ'} — nouns/adjectives make cleaner clues than verbs and
    avoid the subtitle proper-name noise). `min_len` drops 1–2 letter tokens, which in the
    frequency list are mostly fragments / mislabeled function words (עו, תר, מה) rather than
    real clue words. Returns (words, counts)."""
    data = content_lemma_master(source_n)
    band = [(w, c) for w, c, p in data
            if lo <= c <= hi and len(w) >= min_len and (pos is None or p in pos)][:n]
    return [w for w, _ in band], np.array([c for _, c in band], dtype=np.float32)


def freq_scores(counts, lo: float = 200.0, hi: float = 60000.0, margin: float = 2.0) -> np.ndarray:
    """DETECT-FREQ preference in [0,1]: ~1 inside the mid-frequency band [lo, hi], with a
    soft log-linear decay over `margin` log-units for words that are too rare or too common.
    `counts` is an array of corpus counts aligned to a clue vocabulary."""
    c = np.asarray(counts, dtype=np.float64)
    x = np.log(np.clip(c, 1.0, None))
    lo_l, hi_l = np.log(lo), np.log(hi)
    below = np.clip(1.0 - (lo_l - x) / margin, 0.0, 1.0)
    above = np.clip(1.0 - (x - hi_l) / margin, 0.0, 1.0)
    s = np.where(x < lo_l, below, np.where(x > hi_l, above, 1.0))
    return np.where(c <= 0, 0.0, s).astype(np.float32)


# --------------------------------------------------------------------------- #
# Board
# --------------------------------------------------------------------------- #

@dataclass
class Board:
    words: list[str]
    role: dict[str, str]  # word -> my | opp | neutral | assassin

    def of(self, r: str) -> list[str]:
        return [w for w in self.words if self.role[w] == r]

    @property
    def my(self):       return self.of("my")
    @property
    def assassin(self):
        a = self.of("assassin")
        return a[0] if a else ""      # tolerate a board the user marked without an assassin
    @property
    def avoid(self):    return [w for w in self.words if self.role[w] != "my"]


def sample_board(rng: random.Random) -> Board:
    words = rng.sample(DECK, N_BOARD)
    roles = (["my"] * N_MY + ["opp"] * N_OPP +
             ["neutral"] * N_NEUTRAL + ["assassin"] * N_ASSASSIN)
    rng.shuffle(roles)
    return Board(words=words, role=dict(zip(words, roles)))


# --------------------------------------------------------------------------- #
# Encoder spymaster + ranking
# --------------------------------------------------------------------------- #

def encoder_rank(enc, board: Board, clue: str):
    """Rank all board words by cosine to the clue. Returns (ordered_words, sims_dict)."""
    W = enc.embed(board.words)
    c = enc.embed([clue])[0]
    sims = W @ c
    order = np.argsort(-sims)
    return [board.words[i] for i in order], {board.words[i]: float(sims[i]) for i in range(len(board.words))}


def cohesion_keep(enc, words, floor: float = 0.24, pin=frozenset(), mode: str = "any"):
    """Greedy intra-cluster cohesion filter. Keep the head (strongest) word, then keep each
    later word only if it coheres (cosine >= floor) with the already-kept set — or is pinned.
    Enforces that a clue names a *cluster*: every counted word must cohere with the others,
    not merely with the clue. Catches a passenger like radio→milk that the clue↔word
    similarity alone lets through (milk is close-ish to 'radio' but far from voice/journalist).
    `words` must be in similarity order (strongest first).

    `mode` sets what "coheres with the kept set" means:
      "any"  — link to *any* kept word (handles transitive a→b→c chains, but a noise pair can
               attach to each other via one borderline link, e.g. food→{beauty,freedom}),
      "head" — link to the *head* (strongest) word (kills noise sub-clusters, but can drop a
               legitimate chain tail that relates to a sibling more than to the head)."""
    if len(words) <= 1:
        return list(words)
    V = enc.embed(list(words))
    V = V / (np.linalg.norm(V, axis=1, keepdims=True) + 1e-9)
    S = V @ V.T
    kept = [0]
    for i in range(1, len(words)):
        link = S[i, 0] if mode == "head" else max(S[i, j] for j in kept)
        if words[i] in pin or link >= floor:
            kept.append(i)
    return [words[i] for i in kept]


def served_count(read, keep_rel: float = 0.66, pin=frozenset(),
                 enc=None, cohesion_floor: float | None = None, cohesion_mode: str = "any"):
    """The words a clue should *claim* and light up, from a board reading.

    `read` = list of {word, role, sim} ordered by sim desc (an encoder's reading of the clue).
    Two stages:
      1. Walk the *safe run* (team words reached before any enemy word) and keep each next word
         while it stays strong: above `keep_rel`× the top target AND no sharp cliff (< 0.5× the
         previous kept word). A pinned word is always kept. This adapts the count to how many
         words are genuinely clustered — a tight trio stays 3, "1 strong + noise tail" shrinks.
      2. Cohesion trim (when `enc` + `cohesion_floor` given): drop any kept word that doesn't
         cohere with the rest of the cluster (see `cohesion_keep`).
    Returns the kept word list (the served `intended`)."""
    safe = []
    for r in read:
        if r["role"] == "my":
            safe.append(r["word"])
        else:
            break
    if not safe:
        return []
    simmap = {r["word"]: r["sim"] for r in read}
    top = simmap[safe[0]]
    kept = [safe[0]]
    prev = top
    for w in safe[1:]:
        s = simmap[w]
        if w in pin:
            kept.append(w); prev = s; continue
        if s < top * keep_rel or s < prev * 0.5:
            break
        kept.append(w); prev = s
    if enc is not None and cohesion_floor is not None and len(kept) > 1:
        kept = cohesion_keep(enc, kept, floor=cohesion_floor, pin=pin, mode=cohesion_mode)
    return kept


@dataclass
class Clue:
    word: str
    count: int
    intended: list[str]
    margin: float                       # the scoring-function value g(c, I)
    assassin_sim: float = field(default=float("nan"))
    reason: str = ""                    # one-line rationale (hybrid / LLM picks)


def encoder_spymaster(enc, board: Board, clue_vocab, clue_emb=None, vocab_lemmas=None,
                      lam_opp: float = 1.0, lam_neu: float = 0.3, lam_a: float = 2.0,
                      lam_f: float = 0.0, vocab_freq=None, m: int = 2) -> Clue:
    """Pick the clue maximising a tiered Codenames scoring function:
        g(c) = sum_{top-m team} s'(c,b)
               - lam_a   * max(0, s'(c, assassin))     # the black card — avoid hardest
               - lam_opp * max(0, max_opp  s'(c,r))    # rival team — avoid strongly
               - lam_neu * max(0, max_neut s'(c,r))    # bystanders — avoid mildly
               + lam_f   * FREQ(c)                      # DETECT-FREQ: prefer mid-frequency
    where s'(c,w) = cos(c,w) - mean_b cos(c,b) is the similarity centred per clue over
    the 25 board words (anisotropy / DETECT-style correction so broadly-similar common
    words don't win). Clues come from `clue_vocab`, never the board (no shared surface
    form). Pass precomputed `clue_emb` (aligned with clue_vocab) to skip re-embedding, and
    `vocab_freq` (FREQ scores in [0,1] aligned with clue_vocab) to enable the FREQ term.
    """
    bw = board.words
    if vocab_lemmas is None:
        vocab_lemmas = morph.lemmas(clue_vocab)
    mask = legal_vocab_mask(clue_vocab, vocab_lemmas, forbidden_lemmas(board),
                            board_root_sigs(board))     # no shared lemma or shoresh
    keep = [i for i, k in enumerate(mask) if k]
    cand = [clue_vocab[i] for i in keep]
    C = enc.embed(cand) if clue_emb is None else clue_emb[keep]

    B = enc.embed(bw)                                # (25, d)
    adj = C @ B.T                                    # (V, 25) cosine to every board word
    adj = adj - adj.mean(1, keepdims=True)           # centre per clue over the board

    roles = np.array([board.role[w] for w in bw])
    is_my, is_opp = roles == "my", roles == "opp"
    is_neu, is_as = roles == "neutral", roles == "assassin"

    def tier_max(mask):
        return np.clip(adj[:, mask].max(1), 0, None) if mask.any() else np.zeros(len(cand))

    adj_my = adj[:, is_my]
    top_my = np.sort(adj_my, axis=1)[:, ::-1][:, :m].sum(1)
    g = top_my - lam_a * tier_max(is_as) - lam_opp * tier_max(is_opp) - lam_neu * tier_max(is_neu)
    if vocab_freq is not None and lam_f:
        g = g + lam_f * np.asarray(vocab_freq, dtype=np.float32)[keep]

    bi = int(np.nanargmax(g))
    my_words = [w for w, mm in zip(bw, is_my) if mm]
    order = np.argsort(-adj_my[bi])[:m]
    return Clue(word=cand[bi], count=m,
                intended=[my_words[j] for j in order], margin=float(g[bi]),
                assassin_sim=float(adj[bi, is_as][0]) if is_as.any() else float("nan"))


def encoder_clue_candidates(enc, board: Board, clue_vocab, clue_emb=None, vocab_lemmas=None,
                            n: int = 10, targets: list[str] | None = None,
                            lam_opp: float = 1.0, lam_neu: float = 0.3, lam_a: float = 2.0,
                            lam_f: float = 0.0, vocab_freq=None, m: int = 2):
    """Top-n legal clue candidates by the tiered mean-centred score, each with the
    team words it would connect. Used to hand a Hybrid spymaster's LLM a vetted shortlist.

    If `targets` (a subset of the team words) is given, every candidate is scored to
    connect *all* of them (the "I want a clue for these specific words" path); otherwise
    the score auto-selects the best-m team words per candidate."""
    bw = board.words
    if vocab_lemmas is None:
        vocab_lemmas = morph.lemmas(clue_vocab)
    mask = legal_vocab_mask(clue_vocab, vocab_lemmas, forbidden_lemmas(board),
                            board_root_sigs(board))
    keep = [i for i, k in enumerate(mask) if k]
    cand = [clue_vocab[i] for i in keep]
    C = enc.embed(cand) if clue_emb is None else clue_emb[keep]
    B = enc.embed(bw); adj = C @ B.T; adj = adj - adj.mean(1, keepdims=True)
    roles = np.array([board.role[w] for w in bw])
    is_opp, is_neu, is_as = roles == "opp", roles == "neutral", roles == "assassin"
    def tmax(mask): return np.clip(adj[:, mask].max(1), 0, None) if mask.any() else np.zeros(len(cand))
    fixed = bool(targets)
    if fixed:
        my_words = [w for w in targets if w in bw]
        adj_my = adj[:, [bw.index(w) for w in my_words]]
        g_team = adj_my.sum(1)                                  # connect ALL chosen targets
    else:
        my_words = [w for w, mm in zip(bw, roles == "my") if mm]
        adj_my = adj[:, roles == "my"]
        g_team = np.sort(adj_my, 1)[:, ::-1][:, :m].sum(1)      # best-m team words
    g = g_team - lam_a * tmax(is_as) - lam_opp * tmax(is_opp) - lam_neu * tmax(is_neu)
    if vocab_freq is not None and lam_f:
        g = g + lam_f * np.asarray(vocab_freq, dtype=np.float32)[keep]
    out = []
    for bi in np.argsort(-g)[:n]:
        if fixed:
            tg = my_words
        else:
            om = np.argsort(-adj_my[bi])
            tg = [my_words[j] for j in om if adj_my[bi, j] > 0.05][:3] or [my_words[int(om[0])]]
        out.append({"word": cand[int(bi)], "intended": tg, "count": len(tg), "score": float(g[bi])})
    return out


# --------------------------------------------------------------------------- #
# Hebrew LLM (DictaLM 3.0 via MLX)
# --------------------------------------------------------------------------- #

class HebrewLLM:
    def __init__(self, model_id: str = LLM_FAST):
        from mlx_lm import load
        self.model_id = model_id
        self.model, self.tok = load(model_id)

    def chat(self, system: str, user: str, max_tokens: int = 256) -> str:
        from mlx_lm import generate
        msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]
        prompt = self.tok.apply_chat_template(msgs, add_generation_prompt=True)
        try:
            return generate(self.model, self.tok, prompt=prompt, max_tokens=max_tokens, verbose=False)
        except TypeError:
            return generate(self.model, self.tok, prompt, max_tokens=max_tokens, verbose=False)


_SPY_SYS = (
    "אתה רב מרגלים במשחק 'שם קוד' בעברית. אתה רואה את מילות הצוות שלך, מילות היריב, "
    "מילים ניטרליות, ומילת המתנקש שאסור בשום אופן לרמוז עליה. תן רמז של מילה אחת "
    "(לא אחת מהמילים על הלוח) שמקשרת כמה שיותר ממילות הצוות שלך, ורחוקה מהשאר ובמיוחד מהמתנקש."
)
_SPY_FMT = (
    "ענה בדיוק בפורמט הזה ובלי שום טקסט נוסף:\n"
    "רמז: <מילה אחת>\n"
    "מספר: <כמה מילים>\n"
    "מילים: <המילים מהצוות שלך שהרמז מתאר, מופרדות בפסיק>"
)


def llm_spymaster(llm: HebrewLLM, board: Board) -> Clue | None:
    def block(label, ws): return f"{label}: " + ", ".join(ws)
    user = (
        block("הצוות שלי", board.my) + "\n" +
        block("היריב", board.of("opp")) + "\n" +
        block("ניטרלי", board.of("neutral")) + "\n" +
        f"המתנקש (אסור!): {board.assassin}\n\n" + _SPY_FMT
    )
    txt = llm.chat(_SPY_SYS, user, max_tokens=120)
    clue = _grab(r"רמז:\s*([^\n,]+)", txt)
    cnt = _grab(r"מספר:\s*(\d+)", txt)
    words_line = _grab(r"מילים:\s*(.+)", txt)
    if not clue:
        return None
    clue = clue.strip().split()[0]
    if shares_lemma(clue, board):            # illegal: clue is a board word or a form of one
        return None
    intended = []
    if words_line:
        for tok in re.split(r"[,־\-/]| ו", words_line):
            w = _match_board(tok, board.my)
            if w and w not in intended:
                intended.append(w)
    return Clue(word=clue, count=int(cnt) if cnt else len(intended) or 2,
                intended=intended, margin=float("nan"))


_PICK_SYS = (
    "אתה רב מרגלים במשחק 'שם קוד'. קיבלת רשימת רמזים מועמדים, כל אחד עם מילות הצוות שהוא מתאר. "
    "בחר את הרמז הטוב, הבטוח והטבעי ביותר — שמקשר כמה שיותר ממילות הצוות בלי לרמוז על המתנקש או על היריב. "
    "ענה בפורמט הזה בלבד:\nרמז: <המילה מהרשימה>\nמספר: <כמה מילים>\nסיבה: <משפט קצר אחד מדוע זה הרמז הטוב ביותר>"
)


def llm_pick_clue(llm: HebrewLLM, board: Board, candidates) -> Clue:
    """Hybrid spymaster: the LLM picks the best clue out of a geometry-vetted shortlist."""
    lines = "\n".join(f"{i+1}. {c['word']}  →  {', '.join(c['intended'])}" for i, c in enumerate(candidates))
    user = (f"הצוות שלי: {', '.join(board.my)}\nהמתנקש (אסור!): {board.assassin}\n\n"
            f"מועמדים:\n{lines}\n\nבחר רמז אחד מהרשימה.")
    txt = llm.chat(_PICK_SYS, user, max_tokens=120)
    word = _grab(r"רמז:\s*([^\n,]+)", txt)
    cnt = _grab(r"מספר:\s*(\d+)", txt)
    reason = _grab(r"סיבה:\s*(.+)", txt) or ""
    chosen = None
    if word:
        word = word.strip().split()[0]
        for c in candidates:
            if c["word"] == word or word in c["word"] or c["word"] in word:
                chosen = c; break
    chosen = chosen or candidates[0]
    return Clue(word=chosen["word"], count=int(cnt) if cnt else chosen["count"],
                intended=chosen["intended"], margin=chosen.get("score", float("nan")), reason=reason)


_GUESS_SYS = (
    "אתה שחקן במשחק 'שם קוד' בעברית. קיבלת רמז של מילה אחת ורשימת מילים על הלוח. "
    "דרג את כל מילות הלוח מהקשורה ביותר לרמז ועד הפחות קשורה."
)


def llm_guess_ranking(llm: HebrewLLM, board: Board, clue: str) -> list[str]:
    """Full ranking of the 25 board words by the LLM, given the clue."""
    user = (
        f"הרמז: {clue}\n"
        f"מילות הלוח: {', '.join(board.words)}\n\n"
        "החזר את כל מילות הלוח מסודרות מהקשורה ביותר לרמז עד הפחות קשורה, "
        "מופרדות בפסיק, בלי מספור ובלי טקסט נוסף."
    )
    txt = llm.chat(_GUESS_SYS, user, max_tokens=400)
    ranked, seen = [], set()
    for tok in re.split(r"[,\n־]| ו", txt):
        w = _match_board(tok, board.words)
        if w and w not in seen:
            ranked.append(w); seen.add(w)
    for w in board.words:                 # append any the model dropped
        if w not in seen:
            ranked.append(w)
    return ranked


# --------------------------------------------------------------------------- #
# Legality (Codenames clue rules)
# --------------------------------------------------------------------------- #
# A clue may not be a board word, an inflection/attached-particle form of one, or
# share its root. Lemma equality (DictaBERT, via morph.py) is the deterministic
# core — no letter lists. The stricter shoresh/derivative case that lemma equality
# cannot see (תוכנה / תוכנית: same root, different lemma) is judged by the LLM.


def forbidden_lemmas(board: "Board") -> set[str]:
    """The board words plus their lemmas — a clue equal to any of these is illegal."""
    return set(board.words) | set(morph.lemmas(board.words))


def board_root_sigs(board: "Board") -> set[str]:
    """Shoresh signatures of the board words — a clue sharing one is a same-root derivative
    (e.g. קסם next to קוסם) and therefore illegal. Length<2 sigs are dropped as too coarse."""
    return {s for s in (morph.root_sig(lem) for lem in morph.lemmas(board.words)) if len(s) >= 2}


def _root_conflict(sig: str, board_sigs) -> bool:
    """Whether a clue's shoresh signature collides with any board word's. Equal signatures
    always conflict; for roots of 3+ letters, containment in either direction also conflicts
    — this catches reduplication/diminutive and affix derivations that bare equality misses
    (כלב/כלבלב, חתול/חתלתול, ספר/ספרון). For 2-letter skeletons only exact equality counts,
    so short unrelated roots don't collide (אש vs ראש)."""
    if not sig:
        return False
    for bs in board_sigs:
        if sig == bs:
            return True
        if min(len(sig), len(bs)) >= 3 and (bs in sig or sig in bs):
            return True
    return False


def legal_vocab_mask(clue_vocab, vocab_lemmas, forbidden, forbidden_roots=None) -> list[bool]:
    """Per-word legality over a clue vocabulary: not a board word/lemma, and (when
    `forbidden_roots` is given) not a same-root derivative of one."""
    out = []
    for w, lem in zip(clue_vocab, vocab_lemmas):
        ok = w not in forbidden and lem not in forbidden
        if ok and forbidden_roots and _root_conflict(morph.root_sig(lem), forbidden_roots):
            ok = False
        out.append(ok)
    return out


def shares_lemma(clue: str, board: "Board") -> bool:
    """Single-clue legality: clue (or its lemma) coincides with a board word/lemma, or
    shares a shoresh with one (the קסם/קוסם case lemma equality cannot catch)."""
    forbidden = forbidden_lemmas(board)
    lem = morph.lemma(clue)
    if clue in forbidden or lem in forbidden:
        return True
    return _root_conflict(morph.root_sig(lem), board_root_sigs(board))


_ROOT_SYS = (
    "אתה מומחה למורפולוגיה של העברית. ההכרעה מורפולוגית בלבד — לפי שורש משותף או צורה "
    "נטויה/נגזרת — ולא לפי קשר במשמעות. רמז פסול רק אם יש לו אותו שורש כמו מילת לוח, או שהוא "
    "נטייה/נגזרת שלה. דוגמאות לפסול: 'תוכנית' ליד 'תוכנה', 'ספרייה' ליד 'ספר', 'רכב' ליד 'רכבת', "
    "'כלבה' ליד 'כלב'. "
    "דוגמאות לתקין (קשר משמעות בלבד, שורש שונה): 'אורות' ליד 'אש', 'עיתון' ליד 'ספר'. "
    "החזר אך ורק את מספרי המועמדים הפסולים מופרדים בפסיק, או את המילה 'אין' אם כולם תקינים."
)


def llm_root_conflicts(llm: "HebrewLLM", candidate_words, board_words) -> set[str]:
    """Shoresh/derivative gate: ask the Hebrew LLM which candidates share a root with a
    board word — real morphological knowledge for the case lemma equality cannot catch."""
    cw = list(candidate_words)
    if not cw:
        return set()
    lines = "\n".join(f"{i+1}. {w}" for i, w in enumerate(cw))
    user = f"מילות הלוח: {', '.join(board_words)}\n\nמועמדים:\n{lines}\n\nאילו מועמדים פסולים?"
    txt = llm.chat(_ROOT_SYS, user, max_tokens=80)
    bad = set()
    for m in re.findall(r"\d+", txt):
        i = int(m) - 1
        if 0 <= i < len(cw):
            bad.add(cw[i])
    return bad


# --------------------------------------------------------------------------- #
# Parsing helpers
# --------------------------------------------------------------------------- #

def _grab(pat: str, text: str):
    m = re.search(pat, text)
    return m.group(1).strip() if m else None


def _match_board(token: str, candidates: list[str]):
    """Map a noisy LLM token to a board word: exact, then substring either way."""
    t = re.sub(r"[^֐-׿]", "", token).strip()
    if not t:
        return None
    if t in candidates:
        return t
    for c in candidates:
        if t == c.replace(" ", ""):
            return c
    for c in candidates:
        if (t in c) or (c in t):
            return c
    return None


# --------------------------------------------------------------------------- #
# Metrics
# --------------------------------------------------------------------------- #

def spearman(order_a: list[str], order_b: list[str]) -> float:
    """Spearman rho between two orderings of the same item set."""
    from scipy.stats import spearmanr
    rank_a = {w: i for i, w in enumerate(order_a)}
    rank_b = {w: i for i, w in enumerate(order_b)}
    items = list(order_a)
    rho, _ = spearmanr([rank_a[w] for w in items], [rank_b[w] for w in items])
    return float(rho)


def recovery_at_k(order: list[str], intended: list[str], k: int) -> float:
    if not intended:
        return float("nan")
    return len(set(order[:k]) & set(intended)) / len(intended)