code/scripts/build_fixer_critique_conditional_v7.py

"""
Build CRITIQUE-CONDITIONAL fixer SFT data (v7).

Key change vs v6: completion depends on critique content.
- If critique (both fb_select and fb_condition) lenient-OK → completion = planner_sql VERBATIM
- Else → completion = gold_sql

This teaches the fixer to:
- KEEP planner_sql when the validator approves (no break)
- FIX to gold when the validator flags issues

With this fixer + iter2 validators:
- COLLAB validator should accurately identify when planner is correct/wrong
- Fixer's outcome depends on validator's verdict accuracy + critique content

Concurrent processing via ThreadPoolExecutor.
"""
import argparse
import json
import os
import re
import random
import sqlite3
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed

os.environ.setdefault("PYTHONNOUSERSITE", "1")
os.environ["NO_PROXY"] = "localhost,127.0.0.1"

import requests
from datasets import load_dataset, Dataset, DatasetDict

_db_lock = threading.Lock()


def safe_exec(db_path, sql, timeout=5):
    r = [None]; e = [None]
    def _run():
        try:
            c = sqlite3.connect(db_path); c.text_factory = lambda b: b.decode(errors="ignore")
            r[0] = c.execute(sql).fetchmany(100); c.close()
        except Exception as ex:
            e[0] = str(ex)
    t = threading.Thread(target=_run, daemon=True); t.start(); t.join(timeout)
    return (None, "TIMEOUT") if t.is_alive() else (r[0], e[0])


def results_match(g, p):
    if g is None or p is None: return False
    def n(rs): return sorted(tuple(str(v).strip().lower() if v is not None else "" for v in r) for r in rs)
    return n(g) == n(p)


def extract_sql(text):
    m = re.search(r"```(?:sql)?\s*(.*?)\s*```", text, re.DOTALL)
    if m:
        s = m.group(1).strip()
        return s[3:].strip() if s.upper().startswith("SQL") else s
    return ""


def qwen_chat(p):
    return f"<|im_start|>user\n{p}<|im_end|>\n<|im_start|>assistant\n"


def llama3_chat(p):
    return (f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n"
            f"{p}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n")


def vllm_complete(host, model, prompt, n, temperature, top_p, max_tokens, seed, stop=None):
    try:
        r = requests.post(f"{host}/v1/completions", json={
            "model": model, "prompt": prompt,
            "n": n, "temperature": temperature, "top_p": top_p,
            "max_tokens": max_tokens, "seed": seed,
            "stop": stop or ["<|eot_id|>", "<|im_end|>"],
        }, timeout=300)
        r.raise_for_status()
        return [c["text"].strip() for c in r.json()["choices"]]
    except Exception:
        return []


FIXER_PROMPT_HEADER = (
    "You are a SQL fixer. Given the question, schema, original SQL query, "
    "execution response, and the validator's critique below, output ONLY the corrected "
    "final SQL inside ```sql ... ``` markers.\n\n"
)


def build_fixer_prompt(schema_str, question, evidence, planner_sql, exec_response, critique):
    body = (
        f"database schema:\n{schema_str}\n\n"
        f"Question: {question}\n"
        f"External knowledge: {evidence or 'None'}\n\n"
        f"Generated SQL query: {planner_sql}\n\n"
        f"Execution response:\n{exec_response}\n\n"
    )
    return FIXER_PROMPT_HEADER + body + "\n\nValidator critique:\n" + critique + "\n\nFinal SQL:"


def build_validator_body(schema_str, question, evidence, planner_sql, exec_response):
    return (
        f"Generate feedbacks to fix the following SQL query:\n"
        f"Database Schema:\n{schema_str}\n\n"
        f"Question: {question}\n"
        f"External knowledge: {evidence or 'None'}\n\n"
        f"SQL query: {planner_sql}\n\n"
        f"Execution response:\n{exec_response}\n\n"
        f"Feedback:"
    )


def is_ok(s):
    """Lenient match: True if critique text contains 'correct' markers and not 'incorrect'."""
    s = (s or "").lower().strip()
    if "incorrect" in s:
        return False
    return (
        not s
        or "none" in s
        or "no issues" in s
        or "looks correct" in s
        or "is correct" in s
        or "correct." in s
        or "correctly" in s
        or "returns the expected" in s
    )


DEFAULT_SEL  = "SELECT.\nNo SELECT critique generated.\nConclude: correct."
DEFAULT_COND = "CONDITION.\nNo CONDITION critique generated.\nConclude: correct."


def process_one(args, q_lower, info, bird_train, seed_offset):
    bt = bird_train[info["sid"]]
    db_path = bt.get("db_path") or f"data/train_databases/{bt['db_id']}/{bt['db_id']}.sqlite"
    if not os.path.exists(db_path):
        return ("skip_no_db", [])
    question = bt["question"]
    evidence = bt.get("evidence", "") or ""
    gold_sql = bt["sql"]

    user_msg = info["user_msg"]
    if "Database Schema:" in user_msg:
        schema_str = user_msg.split("Database Schema:", 1)[1].split("Question:", 1)[0].rstrip()
    else:
        schema_str = user_msg

    planning_prompt = user_msg.rstrip() + "\n\nPlanning:"
    plans = vllm_complete(
        args.planner_host, "planner", qwen_chat(planning_prompt),
        n=1, temperature=0.0, top_p=1.0, max_tokens=1024, seed=args.seed + seed_offset,
    )
    if not plans:
        return ("no_planner", [])
    m = re.search(r"Final SQL query:\s*```(?:sql)?\s*(.+?)```", plans[0], re.DOTALL | re.IGNORECASE)
    planner_sql = m.group(1).strip() if m else extract_sql(plans[0])
    if not planner_sql:
        return ("no_planner", [])

    with _db_lock:
        gold_res, _ = safe_exec(db_path, gold_sql)
        pred_res, perr = safe_exec(db_path, planner_sql)
    if gold_res is None:
        return ("no_gold", [])
    planner_correct = (not perr) and results_match(gold_res, pred_res)
    exec_response = (f"Error: {perr[:200]}" if perr
                     else f"OK. Result rows (preview): {str(pred_res)[:300]}")

    val_body = build_validator_body(schema_str, question, evidence, planner_sql, exec_response)
    sel_seeded  = val_body + "\nSELECT.\n"
    cond_seeded = val_body + "\nCONDITION.\n"

    sel_outs = vllm_complete(
        args.val_sel_host, "validator", llama3_chat(sel_seeded),
        n=args.K, temperature=args.temperature, top_p=0.9,
        max_tokens=384, seed=args.seed + seed_offset,
    )
    cond_outs = vllm_complete(
        args.val_cond_host, "validator", llama3_chat(cond_seeded),
        n=args.K, temperature=args.temperature, top_p=0.9,
        max_tokens=384, seed=args.seed + seed_offset + 1,
    )
    if not sel_outs and not cond_outs:
        return ("no_val", [])
    sel_outs  = [f"SELECT.\n{c.lstrip()}"    if c else DEFAULT_SEL  for c in sel_outs]
    cond_outs = [f"CONDITION.\n{c.lstrip()}" if c else DEFAULT_COND for c in cond_outs]
    while len(sel_outs)  < args.K: sel_outs.append(DEFAULT_SEL)
    while len(cond_outs) < args.K: cond_outs.append(DEFAULT_COND)

    rows = []
    n_keep_planner = 0
    n_fix_to_gold = 0
    for j in range(args.K):
        s_out, c_out = sel_outs[j], cond_outs[j]
        combined = (
            f"<select>\n{s_out}\n</select>\n\n"
            f"<condition>\n{c_out}\n</condition>\n\n"
            "<join>\nJOIN.\nNone\n</join>\n\n"
            "<order>\nORDER BY.\nNone\n</order>"
        )
        prompt = build_fixer_prompt(schema_str, question, evidence, planner_sql, exec_response, combined)
        # CRITIQUE-CONDITIONAL completion
        sel_ok = is_ok(s_out)
        cond_ok = is_ok(c_out)
        val_approves = sel_ok and cond_ok
        if val_approves:
            # Validator approves -> output planner_sql verbatim
            completion = f"```sql\n{planner_sql}\n```"
            n_keep_planner += 1
        else:
            # Validator flags issue -> output gold_sql
            completion = f"```sql\n{gold_sql}\n```"
            n_fix_to_gold += 1
        rows.append({"prompt": prompt, "completion": completion})

    status = "planner_correct" if planner_correct else "planner_wrong"
    return (status, rows, n_keep_planner, n_fix_to_gold)


def main():
    p = argparse.ArgumentParser()
    p.add_argument("--planner_host",   default="http://localhost:8100")
    p.add_argument("--val_sel_host",   default="http://localhost:8101")
    p.add_argument("--val_cond_host",  default="http://localhost:8104")
    p.add_argument("--K", type=int, default=8)
    p.add_argument("--temperature", type=float, default=1.0)
    p.add_argument("--max_questions", type=int, default=-1, help="-1 = use full dataset (default)")
    p.add_argument("--threads", type=int, default=8)
    p.add_argument("--seed", type=int, default=42)
    p.add_argument("--out", required=True)
    args = p.parse_args()

    print("Loading BIRD-train + griffith prompts...", flush=True)
    with open("data/sft_bird_with_evidence_train_text2sql.json") as f:
        bird_train = json.load(f)
    ds_g = load_dataset("griffith-bigdata/sft_text2sql", split="train_sft",
                        cache_dir="/weka/s225250685/Huggingface/hub"
                       ).filter(lambda x: x["model_name"] == "deepseek-reasoner")
    griffith = {}
    for row in ds_g:
        sid = int(row["sample_id"])
        if not (0 <= sid < len(bird_train)): continue
        user_msg = row["messages"][1]["content"]
        q_m = re.search(r"Question:\s*(.+?)(?:\n|$)", user_msg)
        if not q_m: continue
        q = q_m.group(1).strip()
        if q.lower() == bird_train[sid]["question"].strip().lower():
            griffith[q.lower()] = {"user_msg": user_msg, "sid": sid}
    print(f"  griffith: {len(griffith)} questions", flush=True)

    random.seed(args.seed)
    items = list(griffith.items()); random.shuffle(items)
    chunk = items[:(args.max_questions if args.max_questions > 0 else len(items))]

    rows_all = []
    counters = {"planner_correct": 0, "planner_wrong": 0, "no_planner": 0,
                "skip_no_db": 0, "no_gold": 0, "no_val": 0}
    total_keep_planner = 0
    total_fix_gold = 0

    print(f"Processing {len(chunk)} questions with {args.threads} threads...", flush=True)
    with ThreadPoolExecutor(max_workers=args.threads) as ex:
        futures = []
        for idx, (q_lower, info) in enumerate(chunk):
            futures.append(ex.submit(process_one, args, q_lower, info, bird_train, idx))
        done = 0
        for fut in as_completed(futures):
            try:
                result = fut.result()
                if len(result) == 4:
                    status, rows, n_kp, n_fg = result
                    total_keep_planner += n_kp
                    total_fix_gold += n_fg
                else:
                    status, rows = result
            except Exception as e:
                print(f"  worker exception: {e}", flush=True)
                continue
            counters[status] = counters.get(status, 0) + 1
            rows_all.extend(rows)
            done += 1
            if done % 50 == 0:
                print(f"  [{done}/{len(chunk)}] rows={len(rows_all)} "
                      f"keep_planner={total_keep_planner} fix_gold={total_fix_gold} "
                      f"ok={counters['planner_correct']} wrong={counters['planner_wrong']} "
                      f"no_planner={counters['no_planner']} no_gold={counters['no_gold']} no_val={counters['no_val']}",
                      flush=True)

    print(f"\nGenerated {len(rows_all)} fixer SFT rows", flush=True)
    print(f"  {counters}", flush=True)
    print(f"  Keep planner: {total_keep_planner} ({100*total_keep_planner/max(len(rows_all),1):.1f}%)")
    print(f"  Fix to gold:  {total_fix_gold} ({100*total_fix_gold/max(len(rows_all),1):.1f}%)")
    if not rows_all:
        print("ERROR: no rows generated"); return

    random.seed(42); random.shuffle(rows_all)
    n_train = int(0.95 * len(rows_all))
    DatasetDict({
        "train": Dataset.from_list(rows_all[:n_train]),
        "test":  Dataset.from_list(rows_all[n_train:]),
    }).save_to_disk(args.out)
    print(f"Saved → {args.out}  train={n_train}  test={len(rows_all) - n_train}", flush=True)


if __name__ == "__main__":
    main()