"""Item 20 phase 1: record the E1c halting head's probabilities per item.

One pass over the test set with the gatehead adapter: p0 (pre-loop, on
s_0) and p_1..p_kmax (after each iteration). With these, k*(threshold)
is computable offline for any threshold — no more GPU passes.
"""

import argparse
import json
import os
import sys
from pathlib import Path

import torch

from halting_common import HaltingMergeAdapter
from loop_common import BandLooper
from prep_mbpp import DIRECT_SUFFIX, mbpp_prompt

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"))


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--adapter", required=True)
    ap.add_argument("--kmax", type=int, default=4)
    ap.add_argument("--n", type=int, default=250)
    ap.add_argument("--batch", type=int, default=8)
    args = ap.parse_args()

    model, tok = load_model(dtype=torch.bfloat16)
    tok.padding_side = "left"
    looper = BandLooper(model)
    adapter = HaltingMergeAdapter(
        d=model.config.get_text_config().hidden_size).cuda()
    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]
    rows = []
    with torch.no_grad():
        for i in range(0, len(items), args.batch):
            chunk = items[i : i + args.batch]
            enc = tok([mbpp_prompt(tok, it, DIRECT_SUFFIX) for it in chunk],
                      return_tensors="pt", padding=True,
                      add_special_tokens=False).to("cuda")
            ids, msk = enc["input_ids"], enc["attention_mask"]
            calls, _ = looper.capture(ids, msk, logits_to_keep=1)
            e = looper._hin[looper.l0].detach()
            B = e.shape[0]
            bidx = torch.arange(B, device=e.device)
            pl = torch.full((B,), ids.shape[1] - 1, device=e.device,
                            dtype=torch.long)
            s = looper.band(e, calls)
            probs = [adapter.halt_prob(e[bidx, pl], s[bidx, pl])]
            for _ in range(args.kmax):
                x = adapter(e, s)
                s = looper.band(x, calls)
                probs.append(adapter.halt_prob(e[bidx, pl], s[bidx, pl]))
            P = torch.stack(probs, -1).float().cpu()  # (B, kmax+1)
            for j, it in enumerate(chunk):
                rows.append({"task_id": it["task_id"], "label": it["label"],
                             "p": [round(v, 5) for v in P[j].tolist()]})
            if i % 40 == 0:
                print(f"[{i+len(chunk)}/{len(items)}]", flush=True)
    json.dump(rows, open(OUT / "gate_probs.json", "w"), indent=1)
    print("wrote", OUT / "gate_probs.json")


if __name__ == "__main__":
    main()