import argparse, json, random, time, threading
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from buddhagpt.llm import openrouter_client, chat
from buddhagpt.datagen import build_messages, parse_pairs, dedupe, interleaved_variant

# NOTE: OpenRouter lists this model under the tilde-prefixed "latest" alias id.
MODEL = "~deepseek/deepseek-v4-flash-latest"


def load_qualifying() -> list[dict]:
    suttas = [json.loads(l) for l in Path("corpus/suttas.jsonl").read_text().splitlines()]
    return [s for s in suttas if len(s["text"]) > 800]  # only 2304 of 3920 suttas qualify


def build_full_sample(qualifying: list[dict], target_calls: int, seed: int) -> list[tuple[dict, int]]:
    """Cycle through the qualifying corpus (reshuffled each pass) until target_calls is
    reached, assigning template variant by running call index so every full run covers
    all templates in TEMPLATES rather than one variant per pass."""
    random.seed(seed)
    sample = []
    while len(sample) < target_calls:
        order = qualifying[:]
        random.shuffle(order)
        for s in order:
            if len(sample) >= target_calls:
                break
            sample.append((s, interleaved_variant(len(sample))))
    return sample


def build_topup_sample(qualifying: list[dict], variants: list[int], per_variant: int, seed: int) -> list[tuple[dict, int]]:
    """Sample `per_variant` distinct suttas (seeded, no repeats within a variant) for each
    variant in `variants`, for topping up underrepresented templates."""
    sample = []
    for variant in variants:
        random.seed(seed + variant)
        chosen = random.sample(qualifying, min(per_variant, len(qualifying)))
        sample += [(s, variant) for s in chosen]
    random.seed(seed)
    random.shuffle(sample)
    return sample


def run(sample: list[tuple[dict, int]], raw_path: Path) -> tuple[dict, int, int]:
    client = openrouter_client()
    totals = {"input": 0, "output": 0}
    fail_count = 0
    done_count = 0
    raw_count = 0
    start = time.time()
    lock = threading.Lock()

    Path("data").mkdir(parents=True, exist_ok=True)
    raw_f = raw_path.open("a")  # append: incremental persistence, survives interruption

    def gen_one(args):
        nonlocal fail_count, done_count, raw_count
        s, variant = args
        try:
            text, usage = chat(client, MODEL, build_messages(s, variant), max_tokens=2000)
        except Exception as e:
            with lock:
                fail_count += 1
            print(f"skip {s['uid']}: {e}", flush=True)
            return
        pairs = parse_pairs(text, uid=s["uid"])
        for p in pairs:
            p["variant"] = variant
        with lock:
            totals["input"] += usage["input"]; totals["output"] += usage["output"]
            done_count += 1
            raw_count += len(pairs)
            for p in pairs:
                raw_f.write(json.dumps(p) + "\n")
            raw_f.flush()
            if done_count % 100 == 0:
                elapsed = time.time() - start
                print(f"progress {done_count}/{len(sample)} | fails {fail_count} | "
                      f"raw_pairs {raw_count} | {elapsed:.0f}s elapsed", flush=True)

    with ThreadPoolExecutor(max_workers=8) as pool:
        list(pool.map(gen_one, sample))

    raw_f.close()

    fail_rate = fail_count / len(sample) if sample else 0.0
    print(f"calls: {len(sample)} | failed: {fail_count} ({fail_rate:.1%}) | raw pairs parsed: {raw_count}", flush=True)
    return totals, fail_count, raw_count


def rebuild_instructions(raw_path: Path, out_path: Path) -> list[dict]:
    """Rebuild data/instructions.jsonl from the FULL raw file (word filter + dedupe)."""
    pairs = [json.loads(l) for l in raw_path.read_text().splitlines() if l.strip()]
    pairs = [p for p in pairs if 60 <= len(p["answer"].split()) <= 400]
    print(f"pairs after word-count filter: {len(pairs)}", flush=True)
    pairs = dedupe(pairs)
    with out_path.open("w") as f:
        for p in pairs:
            f.write(json.dumps({"messages": [
                {"role": "user", "content": p["question"]},
                {"role": "assistant", "content": p["answer"]},
            ]}) + "\n")
    return pairs


def report_variant_counts(pairs: list[dict], label: str) -> None:
    counts: dict = {}
    for p in pairs:
        v = p.get("variant", "legacy")
        counts[v] = counts.get(v, 0) + 1
    print(f"{label} per-variant counts: {dict(sorted(counts.items(), key=lambda kv: str(kv[0])))}", flush=True)


if __name__ == "__main__":
    ap = argparse.ArgumentParser()
    ap.add_argument("--mode", choices=["full", "topup"], default="full")
    ap.add_argument("--target-calls", type=int, default=3200)
    ap.add_argument("--seed", type=int, default=7)
    ap.add_argument("--topup-variants", type=int, nargs="+", default=[2, 3, 4, 5])
    ap.add_argument("--topup-per-variant", type=int, default=575)
    args = ap.parse_args()

    qualifying = load_qualifying()
    raw_path = Path("data/instructions_raw.jsonl")

    if args.mode == "full":
        sample = build_full_sample(qualifying, args.target_calls, args.seed)
    else:
        sample = build_topup_sample(qualifying, args.topup_variants, args.topup_per_variant, args.seed)

    totals, fail_count, raw_count = run(sample, raw_path)

    pairs = rebuild_instructions(raw_path, Path("data/instructions.jsonl"))
    report_variant_counts(pairs, "final (post-filter, post-dedupe)")
    # deepseek-v4-flash list price ~$0.08/M in, $0.16/M out
    print(len(pairs), "pairs | tokens", totals, "| est cost $%.2f" % (totals["input"]/1e6*0.08 + totals["output"]/1e6*0.16), flush=True)