"""
Generate paper-format validator SFT data using Qwen-2.5-72B-Instruct-AWQ as the teacher,
with few-shot prompting (paper's validator_data/few_shot_prompt_*.txt examples).

Inputs:  data/planner_3B_greedy_bird_train.jsonl  (predictions to critique)
Outputs: data/hf_val_sel_paper_v1   {train, test}
         data/hf_val_cond_paper_v1  {train, test}

The TEACHER sees few-shot examples (5 examples / clause) → it generates feedback in
the paper's "5-step Feedback + Conclude: correct/incorrect" style.
The SAVED prompt is ZERO-SHOT (just the test instance) so the trained validator
generalizes at inference without needing the few-shot examples.

Saved prompt format (from data_processing/generate_sft_data_for_validator.py):
  Generate feedbacks to fix the following SQL query:
  {griffith rich-NL schema}

  Question: {Q}
  External knowledge: {E}

  SQL query: {SQL}

  Execution response:
  {response}

  Feedback:

Saved completion (val-sel): paper-format SELECT block starting with "SELECT.\n..."
Saved completion (val-cond): paper-format CONDITION block starting with "CONDITION.\n..."

Correctness label is OVERRIDDEN by execution match: if planner_correct=True in input
JSONL, force conclude=correct; else force conclude=incorrect. The teacher's NL
reasoning is preserved but its conclusion is patched (so the data is exec-grounded).
"""
import argparse, json, os, re, random, time
os.environ.setdefault("PYTHONNOUSERSITE", "1")
os.environ["NO_PROXY"] = "localhost,127.0.0.1"
import requests
from datasets import Dataset, DatasetDict


FEWSHOT_SEL_PATH = "validator_data/few_shot_prompt_select.txt"
FEWSHOT_COND_PATH = "validator_data/few_shot_prompt_condition.txt"


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


def vllm_complete(host, model, prompts_batch, temperature, top_p, max_tokens, seed, stop=None):
    """Batch completion via vLLM /v1/completions."""
    try:
        r = requests.post(f"{host}/v1/completions", json={
            "model": model, "prompt": prompts_batch,
            "n": 1, "temperature": temperature, "top_p": top_p,
            "max_tokens": max_tokens, "seed": seed,
            "stop": stop or ["=========", "<|im_end|>", "<|endoftext|>"],
        }, timeout=600)
        r.raise_for_status()
        return [c["text"] for c in r.json()["choices"]]
    except Exception as e:
        print(f"  vLLM error: {e}", flush=True)
        return [""] * len(prompts_batch)


def extract_schema_section(user_msg):
    """Extract griffith rich-NL schema portion from user_msg."""
    if "Database Schema:" in user_msg:
        s = user_msg.split("Database Schema:", 1)[1]
        if "Question:" in s:
            s = s.split("Question:", 1)[0]
        return "Database Schema:" + s.rstrip()
    return user_msg.rstrip()


def build_saved_prompt(user_msg, question, evidence, sql_query, exec_response):
    """Zero-shot prompt that gets SAVED as SFT data (no few-shot examples)."""
    schema = extract_schema_section(user_msg)
    return (f"Generate feedbacks to fix the following SQL query:\n"
            f"{schema}\n\n"
            f"Question: {question}\n"
            f"External knowledge: {evidence}\n\n"
            f"SQL query: {sql_query}\n\n"
            f"Execution response:\n"
            f"{exec_response}\n\n"
            f"Feedback:")


def build_teacher_prompt(fewshot_text, user_msg, question, evidence, sql_query, exec_response):
    """Few-shot prompt fed to Qwen-72B teacher (NOT saved)."""
    schema = extract_schema_section(user_msg)
    test = (f"=========\n"
            f"{schema}\n\n"
            f"Question: {question}\n\n"
            f"SQL query: {sql_query}\n\n"
            f"Execution response [written in pandas format]:\n{exec_response}\n\n"
            f"Feedback:")
    return fewshot_text + "\n" + test


def patch_conclusion(completion, planner_correct):
    """Replace teacher's conclusion with exec-grounded truth."""
    target = "Conclude: correct." if planner_correct else "Conclude: incorrect."
    if "Conclude: correct" in completion:
        return re.sub(r"Conclude:\s*correct\.?", target, completion, count=1)
    if "Conclude: incorrect" in completion:
        return re.sub(r"Conclude:\s*incorrect\.?", target, completion, count=1)
    # No conclusion found: append one
    return completion.rstrip() + f"\n- {target}"


def parse_feedback_block(completion, clause_token):
    """Extract just the SELECT./CONDITION. block from completion."""
    completion = completion.strip()
    # Try to find first occurrence of clause_token
    idx = completion.find(clause_token)
    if idx < 0:
        # Teacher might have omitted the token (rare). Prepend.
        completion = f"{clause_token}\n{completion}"
        idx = 0
    block = completion[idx:]
    # Cut at next "=========" or next clause token (if multi-clause output)
    for sep in ["=========", "\nQuestion:", "\nDatabase Schema:"]:
        if sep in block:
            block = block.split(sep, 1)[0]
    return block.rstrip()


def process_clause(args, fewshot_text, clause_token, rows, batch_size=16):
    """Generate paper-format SFT data for one clause (sel or cond)."""
    sft_rows = []
    n_done = 0
    n_correct = 0; n_incorrect = 0; n_empty = 0
    t0 = time.time()

    # Group rows by validity, process in batches
    teacher_prompts = []
    saved_prompts = []
    pcs = []
    for r in rows:
        if not r.get("pred_sql"):
            # Skip empty preds — can't critique
            continue
        sp = build_saved_prompt(r["user_msg"], r["question"], r.get("evidence", ""),
                                r["pred_sql"], r["pred_exec"])
        tp = build_teacher_prompt(fewshot_text, r["user_msg"], r["question"],
                                  r.get("evidence", ""), r["pred_sql"], r["pred_exec"])
        teacher_prompts.append(tp)
        saved_prompts.append(sp)
        pcs.append(r.get("planner_correct", False))

    for i in range(0, len(teacher_prompts), batch_size):
        batch_tp = teacher_prompts[i:i+batch_size]
        batch_sp = saved_prompts[i:i+batch_size]
        batch_pc = pcs[i:i+batch_size]
        # Format as Qwen chat
        chat_batch = [qwen_chat(p) for p in batch_tp]
        outs = vllm_complete(args.teacher_host, "teacher", chat_batch,
                             temperature=args.temperature, top_p=0.95,
                             max_tokens=512, seed=args.seed + i)
        for j, out in enumerate(outs):
            if not out.strip():
                n_empty += 1
                continue
            # Inject the SELECT./CONDITION. prefix if teacher omitted it (since few-shot
            # examples end with "Feedback:" → teacher continues directly into the clause)
            if not out.lstrip().startswith(clause_token):
                out = f"{clause_token}\n" + out.lstrip()
            block = parse_feedback_block(out, clause_token)
            patched = patch_conclusion(block, batch_pc[j])
            if "Conclude: correct" in patched: n_correct += 1
            else: n_incorrect += 1
            sft_rows.append({"prompt": batch_sp[j], "completion": patched})
        n_done = i + len(batch_tp)
        if n_done % 200 == 0 or n_done >= len(teacher_prompts):
            elapsed = time.time() - t0
            print(f"  [{clause_token[:-1]}] {n_done}/{len(teacher_prompts)} "
                  f"correct={n_correct} incorrect={n_incorrect} empty={n_empty} "
                  f"elapsed={elapsed:.0f}s", flush=True)
    return sft_rows


def main():
    p = argparse.ArgumentParser()
    p.add_argument("--input", default="data/planner_3B_greedy_bird_train.jsonl")
    p.add_argument("--out_sel", default="data/hf_val_sel_paper_v1")
    p.add_argument("--out_cond", default="data/hf_val_cond_paper_v1")
    p.add_argument("--teacher_host", default="http://localhost:8200")
    p.add_argument("--max_questions", type=int, default=-1)
    p.add_argument("--temperature", type=float, default=0.3)  # low T for stable teacher
    p.add_argument("--batch_size", type=int, default=16)
    p.add_argument("--seed", type=int, default=42)
    args = p.parse_args()

    # Load few-shot prompts
    with open(FEWSHOT_SEL_PATH) as f: fewshot_sel = f.read().rstrip()
    with open(FEWSHOT_COND_PATH) as f: fewshot_cond = f.read().rstrip()
    print(f"Few-shot prompts loaded: select={len(fewshot_sel)}b, condition={len(fewshot_cond)}b", flush=True)

    # Load predictions
    with open(args.input) as f:
        rows = [json.loads(line) for line in f]
    print(f"Loaded {len(rows)} planner predictions from {args.input}", flush=True)
    if args.max_questions > 0: rows = rows[:args.max_questions]

    # Wait for teacher to be ready
    for _ in range(60):
        try:
            r = requests.get(f"{args.teacher_host}/v1/models", timeout=5)
            if r.ok: break
        except Exception: pass
        time.sleep(5)
    print(f"Teacher host {args.teacher_host} ready", flush=True)

    def save_split(name, data, out_path):
        random.seed(args.seed)
        random.shuffle(data)
        n_train = int(0.95 * len(data))
        train = data[:n_train]; test = data[n_train:]
        n_corr = sum(1 for r in train if "Conclude: correct" in r["completion"])
        print(f"  {name}: train={len(train)} test={len(test)}  "
              f"correct={n_corr} ({100*n_corr/max(1,len(train)):.1f}%)")
        DatasetDict({
            "train": Dataset.from_list(train),
            "test": Dataset.from_list(test),
        }).save_to_disk(out_path)
        print(f"   saved → {out_path}", flush=True)

    # Process SELECT (save immediately so a later crash in val-cond doesn't lose this)
    print("\n=== Generating val-sel SFT (paper format) ===", flush=True)
    sel_rows = process_clause(args, fewshot_sel, "SELECT.", rows, args.batch_size)
    print(f"  generated {len(sel_rows)} val-sel rows")
    save_split("val-sel", sel_rows, args.out_sel)

    # Process CONDITION
    print("\n=== Generating val-cond SFT (paper format) ===", flush=True)
    cond_rows = process_clause(args, fewshot_cond, "CONDITION.", rows, args.batch_size)
    print(f"  generated {len(cond_rows)} val-cond rows")
    save_split("val-cond", cond_rows, args.out_cond)


if __name__ == "__main__":
    main()