"""Build v3 validator SFT data with balanced all-OK + critique rows.

v2 had 8.1% all-OK rows → validator hallucinates critiques at inference.
v3 supplements v2 with ~5000 all-OK rows mined from real planner_correct
trajectories on BIRD-TRAIN, so the validator learns to stay silent when
the planner SQL is already correct.

Output: data/multi-agents/fixed/sft-validator-diverse-v3
"""
import json
import random
from datasets import load_from_disk, Dataset, DatasetDict

OK_TEMPLATES = [
    """<select>
SELECT.
No issues with SELECT.
</select>

<condition>
CONDITION.
No issues with WHERE/HAVING.
</condition>

<join>
JOIN.
Tables and join keys look correct.
</join>

<order>
ORDER BY.
None
</order>""",
    """<select>
SELECT.
The SELECT clause is correct.
</select>

<condition>
CONDITION.
Filter conditions look correct.
</condition>

<join>
JOIN.
No issues with JOIN.
</join>

<order>
ORDER BY.
None
</order>""",
    """<select>
SELECT.
None
</select>

<condition>
CONDITION.
None
</condition>

<join>
JOIN.
None
</join>

<order>
ORDER BY.
None
</order>""",
    """<select>
SELECT.
The projection list matches the question.
</select>

<condition>
CONDITION.
WHERE/HAVING clauses are correct.
</condition>

<join>
JOIN.
Tables and join keys are correct.
</join>

<order>
ORDER BY.
The ordering is correct.
</order>""",
]


def main():
    rng = random.Random(42)

    # Load existing v2 (force plain-dict copy; drop "messages" because v2 stores it as a non-list dict that breaks arrow)
    v2 = load_from_disk("/home/datht/mats-sql-tist/data/multi-agents/fixed/sft-validator-diverse-v2")
    v2_train = [{"prompt": r["prompt"], "completion": r["completion"]} for r in v2["train"]]
    v2_test = [{"prompt": r["prompt"], "completion": r["completion"]} for r in v2["test"]]

    # Mine all-OK rows from K=4 train rollouts (planner_correct trajectories)
    src = "/home/datht/mats-sql-tist/data/rollouts/scaleup_bird_train_2stage_K4.jsonl"
    ok_rows = []
    seen_prompts = set()
    with open(src) as f:
        for line in f:
            s = json.loads(line)
            for t in s.get("trajectories", []):
                if not t.get("is_planner_correct"):
                    continue
                vp = (t.get("validator_prompt") or "").strip()
                if not vp:
                    # rebuild from planner_prompt
                    pp = (t.get("planner_prompt") or "").strip()
                    psql = (t.get("planner_sql") or "").strip()
                    if not pp or not psql:
                        continue
                    vp = pp + "\n\nSQL query:\n" + psql
                # dedup on full vp
                if vp in seen_prompts:
                    continue
                seen_prompts.add(vp)
                ok_rows.append(vp)

    rng.shuffle(ok_rows)

    # Aim: balance such that all-OK ≈ critique. v2 has ~5208 critique rows.
    target_ok = 5200
    ok_rows = ok_rows[:target_ok]

    # Add additional sft-style critique training: use v2 + new all-OK
    new_rows = []
    for vp in ok_rows:
        completion = rng.choice(OK_TEMPLATES)
        new_rows.append({"prompt": vp, "completion": completion})

    # Test split: keep v2 test + small mined sample
    test_ok = ok_rows[target_ok:target_ok + 100] if len(ok_rows) > target_ok else []
    new_test_rows = []
    for vp in test_ok:
        completion = rng.choice(OK_TEMPLATES)
        new_test_rows.append({"prompt": vp, "completion": completion})

    # Combine
    train_combined = v2_train + new_rows
    test_combined = v2_test + new_test_rows
    rng.shuffle(train_combined)

    dd = DatasetDict({
        "train": Dataset.from_list(train_combined),
        "test": Dataset.from_list(test_combined),
    })

    out_dir = "/home/datht/mats-sql-tist/data/multi-agents/fixed/sft-validator-diverse-v3"
    dd.save_to_disk(out_dir)

    # Stats
    n_train = len(train_combined)
    n_train_ok = sum(1 for r in train_combined if "No issues" in r["completion"] or r["completion"].count("None") >= 3)
    print(f"v3 built:")
    print(f"  train: {n_train} ({n_train_ok} all-OK, {n_train - n_train_ok} critique)")
    print(f"  test:  {len(test_combined)}")
    print(f"  Saved to {out_dir}")


if __name__ == "__main__":
    main()