"""
Build v7 pointwise SFT data from BIRD-TRAIN paper-format K=8 rollouts.
Adds validator critique fields (fb_*) to the prompt.

Reads:  eval_results/paper_SFT_VF_passAt8_bird_TRAIN.jsonl (from pipeline regen)
Writes: data/sft_selector_v7_pointwise_fb/{train,test}
"""
import argparse, json, os, re, sys, random
os.environ.setdefault("PYTHONNOUSERSITE", "1")
os.environ.setdefault("DB_EXEC_API_DISABLE", "1")
ROOT = "/weka/s225250685/mats-tist"
os.chdir(ROOT); sys.path.insert(0, ROOT)
from validator_data.validator import _execute_sql
from datasets import Dataset, DatasetDict
from scripts.rich_schema import render_rich_schema

POINTWISE_PROMPT = (
    "You are a SQL correctness judge for the BIRD benchmark.\n"
    "Database Schema (with column meanings, value descriptions, and example values):\n"
    "{schema}\n\n"
    "Question: {question}\n"
    "External knowledge: {evidence}\n\n"
    "Candidate SQL:\n{sql}\n\n"
    "Execution result of the candidate:\n{exec_result}\n\n"
    "Validator critique of the planner draft (for context):\n"
    "  - select:    {fb_select}\n"
    "  - condition: {fb_condition}\n"
    "  - join:      {fb_join}\n"
    "  - order:     {fb_order}\n\n"
    "Does this SQL correctly answer the question, given the schema, the column "
    "descriptions, the external knowledge, the execution result, and the validator's critique? "
    "Answer YES or NO."
)
MAX_SCHEMA_CHARS = 3000


def safe_truncate(s, n):
    s = str(s) if s is not None else ""
    return s if len(s) <= n else s[:n] + "..."


def exec_str(db_path, sql, timeout=8):
    if not sql or not sql.strip(): return "Error: empty SQL"
    try:
        r, err = _execute_sql("./" + db_path if not db_path.startswith("./") else db_path, sql, timeout=timeout)
    except Exception as e:
        return f"Error: {str(e)[:160]}"
    if err: return f"Error: {str(r)[:160]}"
    rows = str(r)[:260]
    return f"OK. Rows preview: {rows}" if rows.strip() and rows.strip() != "[]" else "OK. (no rows returned)"


def render(sample, t, schema_text):
    sql_fixed = (t.get("fixed_sql") or "").strip()
    sql = sql_fixed or (t.get("planner_sql") or "").strip()
    if not sql: return None
    is_correct = bool(t.get("is_fixed_correct") if sql_fixed else t.get("is_planner_correct"))
    ex = exec_str(sample["db_path"], sql)
    label = "YES" if is_correct else "NO"
    prompt = POINTWISE_PROMPT.format(
        schema=schema_text,
        question=sample.get("question", ""),
        evidence=sample.get("evidence", "") or "None",
        sql=safe_truncate(sql, 800),
        exec_result=safe_truncate(ex, 300),
        fb_select=safe_truncate(t.get("fb_select") or "None", 200),
        fb_condition=safe_truncate(t.get("fb_condition") or "None", 200),
        fb_join=safe_truncate(t.get("fb_join") or "None", 200),
        fb_order=safe_truncate(t.get("fb_order") or "None", 200),
    )
    return {
        "prompt": prompt,
        "completion": label,
        "messages": [
            {"role": "user", "content": prompt},
            {"role": "assistant", "content": label},
        ],
        "question": sample.get("question", ""),
        "db_id": sample.get("db_id", ""),
        "is_yes": int(label == "YES"),
    }


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--input", default="eval_results/paper_SFT_VF_passAt8_bird_TRAIN.jsonl")
    ap.add_argument("--out", default="data/sft_selector_v7_pointwise_fb")
    args = ap.parse_args()

    rng = random.Random(42)
    records = []
    n_yes = n_no = 0
    schema_cache = {}
    n_rows = 0

    with open(args.input) as f:
        for line in f:
            line = line.strip()
            if not line: continue
            s = json.loads(line)
            n_rows += 1
            key = s["db_id"]
            if key not in schema_cache:
                schema_cache[key] = safe_truncate(render_rich_schema(s, split="train"), MAX_SCHEMA_CHARS)
            schema_text = schema_cache[key]
            seen = set()
            for t in s.get("trajectories", []):
                sql_fixed = (t.get("fixed_sql") or "").strip()
                sql = sql_fixed or (t.get("planner_sql") or "").strip()
                if not sql: continue
                norm = re.sub(r"\s+", " ", sql.lower())
                if norm in seen: continue
                seen.add(norm)
                rec = render(s, t, schema_text)
                if rec:
                    records.append(rec)
                    if rec["is_yes"]: n_yes += 1
                    else: n_no += 1
            if n_rows % 500 == 0:
                print(f"  read {n_rows} qs, records={len(records)} (YES={n_yes}, NO={n_no})", flush=True)

    print(f"\nTotal records: {len(records)} (YES={n_yes}, NO={n_no})", flush=True)

    # Balance: downsample NO to ~equal YES
    yes_rec = [r for r in records if r["is_yes"]]
    no_rec = [r for r in records if not r["is_yes"]]
    rng.shuffle(no_rec)
    keep_no = no_rec[: min(len(no_rec), int(1.2 * len(yes_rec)))]
    final = yes_rec + keep_no
    rng.shuffle(final)
    print(f"After balance: {len(final)} (YES={len(yes_rec)}, NO={len(keep_no)})")

    # 96/4 split by Q
    by_q = {}
    for r in final:
        by_q.setdefault(r["question"], []).append(r)
    qs = list(by_q.keys())
    rng.shuffle(qs)
    n_test_q = max(40, len(qs) // 25)
    test_qs = set(qs[:n_test_q])
    train, test = [], []
    for q, recs in by_q.items():
        (test if q in test_qs else train).extend(recs)
    rng.shuffle(train); rng.shuffle(test)
    print(f"train: {len(train)}  test: {len(test)}")

    DatasetDict({
        "train": Dataset.from_list(train),
        "test": Dataset.from_list(test),
    }).save_to_disk(args.out)
    print(f"SAVED: {args.out}")


if __name__ == "__main__":
    main()