"""Per-pair worker: pre-merge diagnostic -> recorded prediction -> merge (naive / aligned) ->
DOWNSTREAM ACCURACY for parent A, parent B, naive merge, aligned merge.

usage: run_pairs.py <pairs.json> <gpu> [<shard> <nshards>]
"""
from __future__ import annotations
import os, sys, json, time, gc, traceback
gpu = sys.argv[2]
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
import numpy as np, torch
import ma_common as C
import tasks as TK

PAIRS = json.load(open(sys.argv[1]))
SHARD, NSH = (int(sys.argv[3]), int(sys.argv[4])) if len(sys.argv) > 4 else (0, 1)
LEDGER = os.environ.get("MA_LEDGER", "/root/merge-accuracy/results/ledger.jsonl")
NDOC = int(os.environ.get("MA_NDOC", "500"))
CORE = os.environ.get("MA_TASKS", "sciq,piqa,arc_easy,lambada").split(",")
DEV = "cuda"

_docs = {}
def docs(t):
    if t not in _docs:
        _docs[t] = TK.TASKS[t](NDOC)
    return _docs[t]

done = C.jload(LEDGER)
def have(k): return k in done
def put(k, rec):
    rec["key"] = k; rec["t"] = time.time()
    C.jappend(LEDGER, rec); done[k] = rec
    print(f"[{time.strftime('%H:%M:%S')}] {k}  " +
          " ".join(f"{t}={rec['acc'][t]:.4f}" for t in rec.get("acc", {})), flush=True)

def eval_sd(model, tok, sd=None):
    if sd is not None:
        C.sd_load(model, sd)
    out, items = {}, {}
    for t in CORE:
        r = C.eval_task(model, tok, docs(t), DEV, bs=int(os.environ.get("MA_BS", "16")))
        out[t] = r["acc"]; out[t + "_norm"] = r["acc_norm"]; items[t] = r["items"]
    out["mean"] = float(np.mean([out[t] for t in CORE]))
    return out, items


for pi, P in enumerate(PAIRS):
    if pi % NSH != SHARD:
        continue
    name = P["name"]; rung = P["rung"]
    t_pair = time.time()
    try:
        # ---------------------------------------------------------- parents
        ma = C.load_model(P["a"]["repo"], P["a"].get("rev"))
        ta = C.load_tok(P["a"]["repo"], P["a"].get("rev"))
        sents = C.flores_lines("eng_Latn", 256)
        acts_a = C.capture_acts_sent(ma, ta, sents, DEV)
        sd_a = C.sd_np(ma)
        ka = f"{rung}|{name}|parentA"
        if not have(ka):
            acc, _ = eval_sd(ma, ta)
            put(ka, {"rung": rung, "pair": name, "arm": "parentA", "alpha": None,
                     "model": P["a"]["repo"], "rev": P["a"].get("rev"), "acc": acc})
        accA = done[ka]["acc"]

        mb = C.load_model(P["b"]["repo"], P["b"].get("rev"))
        tb = C.load_tok(P["b"]["repo"], P["b"].get("rev"))
        acts_b = C.capture_acts_sent(mb, tb, sents, DEV)
        sd_b = C.sd_np(mb)
        kb = f"{rung}|{name}|parentB"
        if not have(kb):
            acc, _ = eval_sd(mb, tb)
            put(kb, {"rung": rung, "pair": name, "arm": "parentB", "alpha": None,
                     "model": P["b"]["repo"], "rev": P["b"].get("rev"), "acc": acc})
        accB = done[kb]["acc"]
        del mb; gc.collect(); torch.cuda.empty_cache()

        cfg = ma.config
        hid = getattr(cfg, "hidden_size", None); nh = getattr(cfg, "num_attention_heads", None)

        # ---------------------------------------------------------- mergeable keys
        mk = C.body_keys(sd_a, sd_b)
        full = C.shared_keys(sd_a, sd_b)
        emb_ok = len(full) > len(mk) and P.get("same_tokenizer", True)
        keys = full if emb_ok else mk
        scope = "full" if emb_ok else "body_only"

        # ---------------------------------------------------------- ALIGN (B -> A's frame)
        t0 = time.time()
        sd_b_perm, info_p = C.align_pair(sd_a, sd_b, hid, nh, acts_a, acts_b, "permutation")
        sd_b_orth, info_o = C.align_pair(sd_a, sd_b, hid, nh, acts_a, acts_b, "orthogonal")
        t_align = time.time() - t0

        # ---------------------------------------------------------- PRE-MERGE DIAGNOSTIC
        kd = f"{rung}|{name}|diag"
        if not have(kd):
            d = C.diagnostics({k: sd_a[k] for k in keys}, {k: sd_b[k] for k in keys},
                              {k: sd_b_perm[k] for k in keys}, {k: sd_b_orth[k] for k in keys},
                              acts_a, acts_b)
            d["align_info_perm"] = info_p; d["align_info_orth"] = info_o
            d["align_seconds"] = t_align; d["merge_scope"] = scope; d["n_merge_keys"] = len(keys)
            # PREDICTION, recorded BEFORE any merged model is scored.
            d["predicted_align_helps"] = bool(d["coord_share"] >= 0.02)
            put(kd, {"rung": rung, "pair": name, "arm": "diag", "diag": d})
        diag = done[kd]["diag"]

        # pick the better of the two aligners by scale-free residual distance
        use_orth = diag.get("qmd_bn_orth", 9e9) < diag.get("qmd_bn_perm", 9e9)
        sd_b_al = sd_b_orth if use_orth else sd_b_perm
        aligner = "orthogonal" if use_orth else "permutation"

        # ---------------------------------------------------------- MERGES
        for alpha in P.get("alphas", [0.5]):
            for arm, sdb in (("naive", sd_b), ("aligned", sd_b_al)):
                k = f"{rung}|{name}|{arm}|a{alpha}"
                if have(k):
                    continue
                sd_m = dict(sd_a)
                for kk in keys:
                    sd_m[kk] = (1 - alpha) * sd_a[kk] + alpha * np.asarray(sdb[kk], float)
                acc, _ = eval_sd(ma, ta, sd_m)
                put(k, {"rung": rung, "pair": name, "arm": arm, "alpha": alpha,
                        "aligner": aligner if arm == "aligned" else None,
                        "scope": scope, "acc": acc,
                        "coord_share": diag["coord_share"],
                        "accA_mean": accA["mean"], "accB_mean": accB["mean"]})
                del sd_m; gc.collect()
                C.sd_load(ma, sd_a)   # restore A for the next merge
        # ---------------------------------------------------------- TIES (alpha-free)
        if P.get("ties", True):
            for arm, sdb in (("ties_naive", sd_b), ("ties_aligned", sd_b_al)):
                k = f"{rung}|{name}|{arm}"
                if have(k):
                    continue
                try:
                    base = {kk: np.zeros_like(sd_a[kk]) for kk in keys}
                    tv = C.MG.ties(base, [{kk: sd_a[kk] for kk in keys},
                                          {kk: np.asarray(sdb[kk], float) for kk in keys}],
                                   density=0.2)
                    sd_m = dict(sd_a); sd_m.update(tv)
                    acc, _ = eval_sd(ma, ta, sd_m)
                    put(k, {"rung": rung, "pair": name, "arm": arm, "alpha": None,
                            "scope": scope, "acc": acc, "coord_share": diag["coord_share"],
                            "accA_mean": accA["mean"], "accB_mean": accB["mean"]})
                    del sd_m, tv; gc.collect()
                    C.sd_load(ma, sd_a)
                except Exception as e:
                    print("TIES fail", name, arm, repr(e)[:200], flush=True)
        print(f"== pair {name} done in {time.time()-t_pair:.0f}s", flush=True)
    except Exception as e:
        print(f"!! PAIR FAIL {name}: {traceback.format_exc()[-1500:]}", flush=True)
    finally:
        for v in ("ma", "mb", "sd_a", "sd_b", "sd_b_perm", "sd_b_orth", "acts_a", "acts_b"):
            if v in dir(): pass
        try: del ma
        except Exception: pass
        gc.collect(); torch.cuda.empty_cache()
print("ALLDONE", flush=True)