"""E2 stage A prep (item 21): harvest TERSE verified CoTs for GSM8K train.

For each non-drop train item, sample a compressed scratchpad ("at most 3
short steps"), keep it only if the final number matches gold (STaR
filter). Output: results-loop/gsm_cot_data.json rows
{idx, label, cot} — the dense supervision the latent-mode arms never had.
"""

import json
import os
import sys
import time
from pathlib import Path

import torch

from loop_common import chat_prompt, last_number, num_eq

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"))
TERSE_SUFFIX = ("\nSolve in at most 3 short steps, one line each, digits "
                "only (like '4*6=24'). Then give the last line exactly as "
                "'Answer: N'.")
BATCH = 16
MAX_NEW = 120


@torch.no_grad()
def main():
    model, tok = load_model(dtype=torch.bfloat16)
    tok.padding_side = "left"
    items = [it for it in json.load(open(OUT / "star_data.json"))
             if it["split"] == "train" and it["label"] != "drop"]
    print(f"harvesting terse CoTs for {len(items)} train items", flush=True)
    rows, kept = [], 0
    t0 = time.time()
    for i in range(0, len(items), BATCH):
        chunk = items[i : i + BATCH]
        enc = tok([chat_prompt(tok, it["question"], TERSE_SUFFIX)
                   for it in chunk], return_tensors="pt", padding=True,
                  add_special_tokens=False).to("cuda")
        gen = model.generate(**enc, max_new_tokens=MAX_NEW, do_sample=False)
        for j, it in enumerate(chunk):
            txt = tok.decode(gen[j, enc["input_ids"].shape[1]:],
                             skip_special_tokens=True).strip()
            ok = num_eq(last_number(txt), it["gold"])
            if ok:
                kept += 1
                rows.append({"idx": it["idx"], "label": it["label"],
                             "cot": txt})
        if i % 80 == 0:
            print(f"[{i+len(chunk)}/{len(items)}] kept={kept} "
                  f"({time.time()-t0:.0f}s)", flush=True)
    json.dump(rows, open(OUT / "gsm_cot_data.json", "w"), indent=1)
    by = {}
    for r in rows:
        by[r["label"]] = by.get(r["label"], 0) + 1
    print(f"wrote {len(rows)} verified terse CoTs {by} -> gsm_cot_data.json",
          flush=True)


if __name__ == "__main__":
    main()