"""Build diverse 4-section critique SFT data for validator.

Source: data/rollouts/scaleup_bird_train_2stage_K4.jsonl (3B planner K=4 rollouts).

For each (planner_sql, gold_sql, exec_result, is_planner_correct):
- Parse both SQLs with sqlglot.
- Diff structurally: SELECT columns, WHERE/HAVING conditions, JOIN tables/keys, ORDER BY / LIMIT.
- Build a 4-section critique localizing the specific error.

Output: data/multi-agents/fixed/sft-validator-diverse-v2/ (HF dataset on disk)
"""
import json
import os
import re
import sys
import random
from collections import Counter

import sqlglot
from sqlglot import exp
from datasets import Dataset, DatasetDict


# -------------------- SQL diff helpers --------------------

def normalize(s):
    if s is None:
        return ""
    return re.sub(r"\s+", " ", str(s).strip().lower())


def parse_safe(sql, dialect="sqlite"):
    if not sql:
        return None
    try:
        return sqlglot.parse_one(sql, read=dialect)
    except Exception:
        return None


def extract_select_items(tree):
    if tree is None:
        return []
    sel = tree.find(exp.Select)
    if sel is None:
        return []
    return [normalize(e.sql()) for e in sel.expressions]


def extract_where_text(tree):
    if tree is None:
        return ""
    w = tree.find(exp.Where)
    return normalize(w.sql()) if w else ""


def extract_having_text(tree):
    if tree is None:
        return ""
    h = tree.find(exp.Having)
    return normalize(h.sql()) if h else ""


def extract_join_keys(tree):
    if tree is None:
        return []
    keys = []
    for j in tree.find_all(exp.Join):
        keys.append(normalize(j.sql()))
    return keys


def extract_tables(tree):
    if tree is None:
        return set()
    return {normalize(t.name) for t in tree.find_all(exp.Table)}


def extract_order_text(tree):
    if tree is None:
        return ""
    o = tree.find(exp.Order)
    return normalize(o.sql()) if o else ""


def extract_limit_text(tree):
    if tree is None:
        return ""
    l = tree.find(exp.Limit)
    return normalize(l.sql()) if l else ""


def extract_group_text(tree):
    if tree is None:
        return ""
    g = tree.find(exp.Group)
    return normalize(g.sql()) if g else ""


# -------------------- critique builders --------------------

SELECT_OK = ["None", "Selected columns look correct.", "The projection matches the question.", "No issues with SELECT."]
COND_OK = ["None", "No issues with WHERE/HAVING.", "Filter conditions look correct.", "Conditions match the question intent."]
JOIN_OK = ["None", "No issues with JOIN.", "Tables and join keys look correct.", "All required tables are joined correctly."]
ORDER_OK = ["None", "No issues with ORDER BY / LIMIT.", "Sorting and limit are correct.", "Ordering matches the question."]

SELECT_TEMPLATES = [
    "The SELECT clause is incorrect. The query projects {planner_cols} but the question requires {gold_cols}.",
    "The SELECT clause selects the wrong columns. Expected {gold_cols}, got {planner_cols}.",
    "The projection list is wrong. The query should output {gold_cols} instead of {planner_cols}.",
    "Wrong columns are being returned. The question asks for {gold_cols}.",
    "The SELECT clause needs adjustment — the question requires {gold_cols} but the query returns {planner_cols}.",
    "Incorrect projection: replace {planner_cols} with {gold_cols}.",
    "The SELECT clause is missing required output. It should include {gold_cols}.",
    "The query selects {planner_cols}, but the expected output is {gold_cols}.",
]

COND_TEMPLATES = [
    "The WHERE/HAVING conditions are incorrect. The query should filter where {gold_cond} but it filters where {planner_cond}.",
    "Filter conditions need adjustment. Replace {planner_cond} with {gold_cond}.",
    "The WHERE clause is wrong. The question requires {gold_cond}.",
    "The filter conditions don't match the question. Use {gold_cond} instead of {planner_cond}.",
    "Incorrect conditions in WHERE. The proper filter is {gold_cond}.",
    "The query filters rows incorrectly. Expected: {gold_cond}.",
    "WHERE/HAVING needs fixing: the question implies {gold_cond}, but the query uses {planner_cond}.",
    "The conditions filter out valid rows or include invalid ones. Use {gold_cond}.",
]

COND_MISSING_TEMPLATES = [
    "The query is missing a WHERE filter. It should include {gold_cond}.",
    "Add a WHERE clause: {gold_cond}.",
    "No filter is applied, but the question requires {gold_cond}.",
]

COND_EXTRA_TEMPLATES = [
    "Extra WHERE conditions filter out valid rows. Remove {planner_cond}.",
    "The query has unnecessary WHERE conditions: {planner_cond}.",
    "Drop the extraneous filter — the question does not require {planner_cond}.",
]

JOIN_TEMPLATES = [
    "The JOIN structure is incorrect. The query should join {gold_tables} but joins {planner_tables}.",
    "Missing tables in JOIN. Add JOIN to {gold_only_tables}.",
    "Unnecessary tables joined. Remove JOIN to {extra_tables}.",
    "The JOIN keys are wrong. Use {gold_joins}.",
    "Incorrect JOIN: the proper join is {gold_joins}.",
    "Tables are joined incorrectly. The required join is {gold_joins}.",
]

ORDER_TEMPLATES = [
    "The ORDER BY / LIMIT is wrong. The query should order by {gold_order}.",
    "Missing ORDER BY. The question requires ordering by {gold_order}.",
    "Incorrect sort. Replace {planner_order} with {gold_order}.",
    "ORDER BY direction is wrong. Use {gold_order}.",
    "The LIMIT is incorrect. The question expects {gold_limit}.",
    "Missing LIMIT clause. The query should be limited to {gold_limit}.",
]


def _short(s, n=120):
    if s is None:
        return ""
    s = re.sub(r"\s+", " ", str(s).strip())
    return s if len(s) <= n else s[:n] + "..."


def build_select_critique(planner_items, gold_items, rng):
    if not planner_items and not gold_items:
        return rng.choice(SELECT_OK)
    if set(planner_items) == set(gold_items):
        return rng.choice(SELECT_OK)
    tmpl = rng.choice(SELECT_TEMPLATES)
    return tmpl.format(
        planner_cols=_short(", ".join(planner_items[:6]) or "(none)", 120),
        gold_cols=_short(", ".join(gold_items[:6]) or "(none)", 120),
    )


def build_cond_critique(planner_where, gold_where, planner_having, gold_having, rng):
    pw = (planner_where + " " + planner_having).strip()
    gw = (gold_where + " " + gold_having).strip()
    if not pw and not gw:
        return rng.choice(COND_OK)
    if normalize(pw) == normalize(gw):
        return rng.choice(COND_OK)
    if not pw and gw:
        return rng.choice(COND_MISSING_TEMPLATES).format(gold_cond=_short(gw, 200))
    if pw and not gw:
        return rng.choice(COND_EXTRA_TEMPLATES).format(planner_cond=_short(pw, 200))
    tmpl = rng.choice(COND_TEMPLATES)
    return tmpl.format(
        planner_cond=_short(pw, 200),
        gold_cond=_short(gw, 200),
    )


def build_join_critique(planner_tables, gold_tables, planner_joins, gold_joins, rng):
    if planner_tables == gold_tables and set(planner_joins) == set(gold_joins):
        return rng.choice(JOIN_OK)
    if planner_tables == gold_tables:
        return rng.choice(JOIN_OK)  # tables match; treat as OK at join level
    missing = gold_tables - planner_tables
    extra = planner_tables - gold_tables
    if missing and not extra:
        return rng.choice(JOIN_TEMPLATES[1:2]).format(gold_only_tables=_short(", ".join(sorted(missing)), 120))
    if extra and not missing:
        return rng.choice(JOIN_TEMPLATES[2:3]).format(extra_tables=_short(", ".join(sorted(extra)), 120))
    tmpl = rng.choice(JOIN_TEMPLATES[:1] + JOIN_TEMPLATES[3:])
    return tmpl.format(
        planner_tables=_short(", ".join(sorted(planner_tables)), 120),
        gold_tables=_short(", ".join(sorted(gold_tables)), 120),
        gold_joins=_short("; ".join(gold_joins[:3]), 200),
    )


def build_order_critique(planner_order, gold_order, planner_limit, gold_limit, rng):
    po = (planner_order + " " + planner_limit).strip()
    go = (gold_order + " " + gold_limit).strip()
    if normalize(po) == normalize(go):
        return rng.choice(ORDER_OK)
    if not po and go:
        if "limit" in go and "order" not in go:
            return rng.choice(ORDER_TEMPLATES[5:6]).format(gold_limit=_short(go, 200))
        return rng.choice(ORDER_TEMPLATES[1:2]).format(gold_order=_short(go, 200))
    if po and not go:
        return rng.choice(ORDER_OK)  # extra ordering rarely wrong
    tmpl = rng.choice(ORDER_TEMPLATES)
    return tmpl.format(
        planner_order=_short(po, 200),
        gold_order=_short(go, 200),
        gold_limit=_short(gold_limit or go, 100),
    )


def build_critique(planner_sql, gold_sql, is_correct, rng):
    if is_correct:
        # All correct → all-None template
        return (
            f"<select>\nSELECT.\n{rng.choice(SELECT_OK)}\n</select>\n\n"
            f"<condition>\nCONDITION.\n{rng.choice(COND_OK)}\n</condition>\n\n"
            f"<join>\nJOIN.\n{rng.choice(JOIN_OK)}\n</join>\n\n"
            f"<order>\nORDER BY.\n{rng.choice(ORDER_OK)}\n</order>"
        )

    p_tree = parse_safe(planner_sql)
    g_tree = parse_safe(gold_sql)

    p_items = extract_select_items(p_tree)
    g_items = extract_select_items(g_tree)
    p_where = extract_where_text(p_tree)
    g_where = extract_where_text(g_tree)
    p_having = extract_having_text(p_tree)
    g_having = extract_having_text(g_tree)
    p_tables = extract_tables(p_tree)
    g_tables = extract_tables(g_tree)
    p_joins = extract_join_keys(p_tree)
    g_joins = extract_join_keys(g_tree)
    p_order = extract_order_text(p_tree)
    g_order = extract_order_text(g_tree)
    p_limit = extract_limit_text(p_tree)
    g_limit = extract_limit_text(g_tree)

    sel_crit = build_select_critique(p_items, g_items, rng)
    cond_crit = build_cond_critique(p_where, g_where, p_having, g_having, rng)
    join_crit = build_join_critique(p_tables, g_tables, p_joins, g_joins, rng)
    order_crit = build_order_critique(p_order, g_order, p_limit, g_limit, rng)

    return (
        f"<select>\nSELECT.\n{sel_crit}\n</select>\n\n"
        f"<condition>\nCONDITION.\n{cond_crit}\n</condition>\n\n"
        f"<join>\nJOIN.\n{join_crit}\n</join>\n\n"
        f"<order>\nORDER BY.\n{order_crit}\n</order>"
    )


# -------------------- prompt builder --------------------

PROMPT_HEADER = (
    "You are a SQL critique agent. Output FOUR critique sections "
    "(<select>...</select>, <condition>...</condition>, <join>...</join>, <order>...</order>) "
    "analysing the SQL query below; do NOT output any SQL.\n\n"
)


def schema_to_string(schema):
    if not schema or not isinstance(schema, dict):
        return ""
    out = []
    for tbl in schema.get("schema_items", []):
        tname = tbl.get("table_name", "")
        cols = tbl.get("column_names", [])
        types = tbl.get("column_types", [])
        comments = tbl.get("column_comments", [])
        contents = tbl.get("column_contents", [])
        pks = tbl.get("pk_indicators", [])
        col_lines = []
        for i, c in enumerate(cols):
            t = types[i] if i < len(types) else ""
            cm = comments[i] if i < len(comments) else ""
            ex = ""
            if i < len(contents):
                vals = contents[i]
                if vals:
                    ex = f"Example Values: `{vals[0]}`"
            pk = "Primary Key" if i < len(pks) and pks[i] else ""
            extra = " | ".join(x for x in [ex, ("Column Description: " + cm) if cm else "", pk] if x)
            col_lines.append(f"    {c} {t},  -- {extra}".rstrip())
        out.append(f"CREATE TABLE {tname}\n(\n" + "\n".join(col_lines) + "\n);")
    fks = schema.get("foreign_keys", []) or []
    if fks:
        for src_t, src_c, dst_t, dst_c in fks:
            out.append(f"-- FK: {src_t}.{src_c} -> {dst_t}.{dst_c}")
    return "\n".join(out)


def build_prompt(question, evidence, schema, planner_sql):
    return (
        PROMPT_HEADER
        + "database schema:\n"
        + schema_to_string(schema)
        + "\n\n"
        + ("external knowledge:\n" + evidence + "\n\n" if evidence else "")
        + "question:\n" + (question or "") + "\n\n"
        + "SQL query to critique:\n" + (planner_sql or "") + "\n"
    )


# -------------------- main --------------------

def main():
    src = "data/rollouts/scaleup_bird_train_2stage_K4.jsonl"
    out_dir = "data/multi-agents/fixed/sft-validator-diverse-v2"

    rng = random.Random(42)

    prompts, completions = [], []
    n_correct = 0
    n_wrong = 0
    counter = Counter()

    with open(src) as f:
        for line in f:
            s = json.loads(line)
            schema = s.get("schema")
            question = s.get("question")
            evidence = s.get("evidence", "") or ""
            gold_sql = s.get("sql", "")
            for t in s.get("trajectories", []):
                planner_sql = t.get("planner_sql") or ""
                if not planner_sql.strip():
                    continue
                is_correct = bool(t.get("is_planner_correct"))
                if is_correct:
                    n_correct += 1
                else:
                    n_wrong += 1
                prompt = build_prompt(question, evidence, schema, planner_sql)
                completion = build_critique(planner_sql, gold_sql, is_correct, rng)
                prompts.append(prompt)
                completions.append(completion)
                # Track template diversity
                counter[completion[:200]] += 1

    print(f"Built {len(prompts)} examples. correct={n_correct}, wrong={n_wrong}")
    print(f"Unique critique prefixes (200 chars): {len(counter)}")
    print("Top 5:")
    for s, c in counter.most_common(5):
        print(f"  {c:5d}: {repr(s[:120])}")

    # Train/test split 95/5
    pairs = list(zip(prompts, completions))
    rng.shuffle(pairs)
    n_test = max(50, len(pairs) // 20)
    test = pairs[:n_test]
    train = pairs[n_test:]

    def make_ds(rows):
        return Dataset.from_list([
            {
                "prompt": p,
                "completion": c,
                "messages": {"prompt": p, "completion": c},
            }
            for p, c in rows
        ])

    dd = DatasetDict({"train": make_ds(train), "test": make_ds(test)})
    os.makedirs(out_dir, exist_ok=True)
    dd.save_to_disk(out_dir)
    print(f"Saved {len(train)} train / {len(test)} test → {out_dir}")


if __name__ == "__main__":
    main()