"""Eval MBPP pass@1 vs loop depth k with prompt-only ("latent planning") loops.

Usage:
  eval_loop_code.py --tag untrained
  eval_loop_code.py --adapter ../results-loop/adapter_code.pt --tag trained
"""

import argparse
import json
import os
import sys
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path

import torch

from loop_common import (AdaptiveMergeAdapter, BandLooper, MergeAdapter,
                         ParcaeAdapter, RecurrentAdapter)
from prep_mbpp import DIRECT_SUFFIX, extract_code, mbpp_prompt, run_tests

sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from jlens.core import load_model  # noqa: E402

OUT = Path(os.environ.get("LOOP_OUT",
    Path(__file__).resolve().parent.parent / "results-loop"))


@torch.no_grad()
def pass1_at_k(looper, adapter, tok, items, k, batch=8, max_new=220,
               feedforward=False, pause=0, halt=False):
    codes = []
    k_convs = []
    for i in range(0, len(items), batch):
        torch.cuda.empty_cache()
        chunk = items[i : i + batch]
        enc = tok([mbpp_prompt(tok, it, DIRECT_SUFFIX) for it in chunk],
                  return_tensors="pt", padding=True,
                  add_special_tokens=False).to("cuda")
        if pause:
            B = enc["input_ids"].shape[0]
            pcol = torch.full((B, pause), 6, dtype=torch.long, device="cuda")
            enc["input_ids"] = torch.cat([enc["input_ids"], pcol], 1)
            enc["attention_mask"] = torch.cat(
                [enc["attention_mask"], torch.ones_like(pcol)], 1)
        cv = {} if halt else None
        gen = looper.generate_frozen_prompt(adapter, tok, enc["input_ids"], k,
                                            max_new_tokens=max_new,
                                            attention_mask=enc["attention_mask"],
                                            feedforward=feedforward,
                                            conv_out=cv)
        if halt:
            k_convs.extend(cv.get("k_conv", []))
        for j in range(len(chunk)):
            txt = tok.decode(gen[j, enc["input_ids"].shape[1]:],
                             skip_special_tokens=True)
            codes.append(extract_code(txt))
    with ThreadPoolExecutor(8) as ex:
        oks = list(ex.map(lambda ci: run_tests(ci[0], ci[1]),
                          zip(codes, items)))
    hits, per_label = 0, {}
    for it, ok in zip(items, oks):
        hits += ok
        d = per_label.setdefault(it["label"], [0, 0])
        d[0] += ok
        d[1] += 1
    per_item = [{"task_id": it["task_id"], "ok": bool(ok)}
                for it, ok in zip(items, oks)]
    if halt and k_convs:
        for r, kc in zip(per_item, k_convs):
            r["k_conv"] = kc
    return (hits / len(items),
            {l: c / n for l, (c, n) in per_label.items()}, per_item)


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--adapter", default=None)
    ap.add_argument("--tag", default="untrained")
    ap.add_argument("--ks", default="0,1,2,4")
    ap.add_argument("--n", type=int, default=250)
    ap.add_argument("--feedforward", action="store_true",
                    help="no-recurrence control arm: adapter(e,e) once")
    ap.add_argument("--pause", type=int, default=0,
                    help="append p pause tokens to each prompt")
    ap.add_argument("--alpha", type=float, default=0.3)
    ap.add_argument("--adaptive", action="store_true")
    ap.add_argument("--rec", action="store_true",
                    help="RecurrentAdapter (noise h0, learned A/B)")
    ap.add_argument("--parcae", action="store_true",
                    help="ParcaeAdapter (rho(A)<1 by construction)")
    ap.add_argument("--perdepth", action="store_true",
                    help="PerDepthAdapter (Bae-style per-iteration merges)")
    ap.add_argument("--halt", action="store_true",
                    help="record per-item convergence depth (free-ACT probe)")
    args = ap.parse_args()
    ks = [int(x) for x in args.ks.split(",")]

    model, tok = load_model(dtype=torch.bfloat16)
    tok.padding_side = "left"
    looper = BandLooper(model)
    from loop_common import PerDepthAdapter
    cls = (PerDepthAdapter if args.perdepth
           else ParcaeAdapter if args.parcae
           else RecurrentAdapter if args.rec
           else AdaptiveMergeAdapter if args.adaptive else MergeAdapter)
    kw = ({"alpha0": args.alpha} if args.adaptive else {"alpha": args.alpha})
    adapter = cls(d=model.config.get_text_config().hidden_size, **kw).cuda()
    if args.adapter:
        adapter.load_state_dict(torch.load(args.adapter, map_location="cuda"))
        adapter.eval()

    items = [it for it in json.load(open(OUT / "mbpp_data.json"))
             if it["split"] == "test"][: args.n]
    print(f"[{args.tag}] MBPP eval on {len(items)} test items, ks={ks}",
          flush=True)

    res = {"tag": args.tag, "ks": {}, "n": len(items)}
    for k in ks:
        t0 = time.time()
        acc, by_label, per_item = pass1_at_k(looper, adapter, tok, items, k,
                                             feedforward=args.feedforward,
                                             pause=args.pause, halt=args.halt)
        if args.halt:
            by_lbl_k = {}
            for it, r in zip(items, per_item):
                by_lbl_k.setdefault(it["label"], []).append(r.get("k_conv", k))
            print("  mean k_conv:", {l: round(sum(v)/len(v), 2)
                                     for l, v in by_lbl_k.items()}, flush=True)
        res["ks"][k] = {"acc": acc, "by_label": by_label,
                        "per_item": per_item}
        print(f"k={k}: pass@1={acc:.3f}  "
              f"by_label={ {l: round(v,3) for l,v in by_label.items()} }"
              f"  ({time.time()-t0:.0f}s)", flush=True)

    with open(OUT / f"eval_code_{args.tag}.json", "w") as f:
        json.dump(res, f, indent=1)
    print("wrote", OUT / f"eval_code_{args.tag}.json")


if __name__ == "__main__":
    main()