"""traj-normalize CLI: Nebius OpenHands parquet -> SFT-ready jsonl.

Usage:
    traj-normalize --input nebius/SWE-rebench-openhands-trajectories \
        --out-dir out --max-success 5000 --seed 0
    traj-normalize --input /path/to/trajectories.parquet --out-dir out
"""

from __future__ import annotations

import argparse
import json
import random
import sys
import time
from pathlib import Path

import pyarrow.parquet as pq

from .core import Stats, load_tools_schema, normalize_row, row_to_record

META_COLS = ["trajectory_id", "instance_id", "repo", "resolved"]
DEFAULT_REPO = "nebius/SWE-rebench-openhands-trajectories"
DEFAULT_DATA_FILE = "trajectories.parquet"


def resolve_input(inp: str) -> tuple[Path, str]:
    """Return (parquet_path, source_desc). Downloads from the Hub if needed."""
    p = Path(inp)
    if p.is_file():
        return p, str(p.resolve())
    from huggingface_hub import hf_hub_download

    path = hf_hub_download(repo_id=inp, repo_type="dataset", filename=DEFAULT_DATA_FILE)
    return Path(path), inp


def resolve_tools_json(tools_arg: str | None, source: str) -> str | None:
    if tools_arg:
        return tools_arg
    # If input came from the Hub, try to fetch tools.json from the same repo.
    if not Path(source).is_file():
        try:
            from huggingface_hub import hf_hub_download

            return hf_hub_download(repo_id=source, repo_type="dataset", filename="tools.json")
        except Exception:
            return None
    # Local file: look for tools.json next to the parquet.
    cand = Path(source).parent / "tools.json"
    return str(cand) if cand.exists() else None


def pass1_select_indices(pf: pq.ParquetFile, args) -> tuple[set[int], Stats]:
    """Scan light columns; return selected row indices + populated counters."""
    stats = Stats()
    resolved_idx, fail_idx = [], []
    offset = 0
    for batch in pf.iter_batches(batch_size=args.batch_size, columns=META_COLS):
        for row in batch.to_pylist():
            stats.input_rows += 1
            if row.get("resolved") == 1:
                stats.resolved_rows += 1
                resolved_idx.append(offset)
            else:
                fail_idx.append(offset)
            offset += 1
            if args.limit and stats.input_rows >= args.limit:
                break
        if args.limit and stats.input_rows >= args.limit:
            break

    rng = random.Random(args.seed)
    if args.max_success and len(resolved_idx) > args.max_success:
        resolved_idx = sorted(rng.sample(resolved_idx, args.max_success))
    if args.include_failures:
        if args.max_fail and len(fail_idx) > args.max_fail:
            fail_idx = sorted(rng.sample(fail_idx, args.max_fail))
    else:
        fail_idx = []

    selected = set(resolved_idx) | set(fail_idx)
    return selected, stats


def run(args) -> int:
    parquet_path, source = resolve_input(args.input)
    tools_path = resolve_tools_json(args.tools_json, source)
    tools_schema = load_tools_schema(tools_path)
    if args.tools_json and tools_schema is None:
        print(f"warning: could not load tools schema from {args.tools_json}", file=sys.stderr)

    out_dir = Path(args.out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)
    success_path = out_dir / "sft_success.jsonl"
    fail_path = out_dir / "sft_fail.jsonl"
    meta_path = out_dir / "meta.json"

    pf = pq.ParquetFile(parquet_path)
    t0 = time.time()

    selected, stats = pass1_select_indices(pf, args)
    print(f"scanned {stats.input_rows} rows ({stats.resolved_rows} resolved); "
          f"selected {len(selected)} for extraction", file=sys.stderr)

    # Pass 2: full rows for selected indices.
    records = [] if args.shuffle else None
    f_succ = open(success_path, "w")
    f_fail = open(fail_path, "w") if args.include_failures else None
    offset = 0
    try:
        for batch in pf.iter_batches(batch_size=args.batch_size):
            for row in batch.to_pylist():
                idx = offset
                offset += 1
                if args.limit and idx >= args.limit:
                    break
                if idx not in selected:
                    continue
                messages, err = normalize_row(row, stats, tools_schema, args.strict_tools)
                if err:
                    stats.drop(err)
                    continue
                rec = row_to_record(row, messages, emit_tools=not args.no_tools)
                if records is not None:
                    records.append(rec)
                else:
                    (f_succ if rec["resolved"] == 1 else f_fail).write(
                        json.dumps(rec, ensure_ascii=False) + "\n")
                if rec["resolved"] == 1:
                    stats.written_success += 1
                else:
                    stats.written_fail += 1
            if args.limit and offset >= args.limit:
                break

        if records is not None:
            rng = random.Random(args.seed)
            rng.shuffle(records)
            stats.written_success = stats.written_fail = 0
            for rec in records:
                (f_succ if rec["resolved"] == 1 else f_fail).write(
                    json.dumps(rec, ensure_ascii=False) + "\n")
                if rec["resolved"] == 1:
                    stats.written_success += 1
                else:
                    stats.written_fail += 1
    finally:
        f_succ.close()
        if f_fail:
            f_fail.close()

    if not args.include_failures and fail_path.exists():
        fail_path.unlink()

    stats.skipped_unresolved = stats.input_rows - stats.resolved_rows

    meta = {
        "tool": "traj-normalize",
        "source": source,
        "tools_schema": tools_path if tools_schema else None,
        "params": {
            "limit": args.limit,
            "max_success": args.max_success,
            "max_fail": args.max_fail if args.include_failures else None,
            "include_failures": args.include_failures,
            "seed": args.seed,
            "shuffle": args.shuffle,
            "strict_tools": args.strict_tools,
        },
        "counts": {
            "input_rows": stats.input_rows,
            "resolved_rows": stats.resolved_rows,
            "written_success": stats.written_success,
            "written_fail": stats.written_fail,
            "skipped_unresolved": stats.skipped_unresolved,
            "dropped": sum(stats.drop_reasons.values()),
        },
        "drop_reasons": stats.drop_reasons,
        "fixes": stats.fixes,
        "unknown_tools": stats.unknown_tools,
        "missing_required_params": stats.missing_required_params,
        "unparseable_examples": stats.unparseable_examples,
        "outputs": {
            p.name: p.stat().st_size for p in (success_path, fail_path) if p.exists()
        },
        "elapsed_sec": round(time.time() - t0, 1),
    }
    meta_path.write_text(json.dumps(meta, indent=2, ensure_ascii=False))
    print(json.dumps(meta["counts"], indent=2), file=sys.stderr)
    print(f"wrote {success_path} (+meta {meta_path})", file=sys.stderr)
    return 0


def build_parser() -> argparse.ArgumentParser:
    ap = argparse.ArgumentParser(
        prog="traj-normalize",
        description="Deserialize Nebius OpenHands trajectories into SFT-ready message lists.",
    )
    ap.add_argument("--input", default=DEFAULT_REPO,
                    help="Local parquet path or HF dataset repo id (default: %(default)s)")
    ap.add_argument("--out-dir", default="out")
    ap.add_argument("--tools-json", default=None,
                    help="Path to upstream tools.json for soft validation (auto-fetched for Hub input)")
    ap.add_argument("--limit", type=int, default=0, help="Max input rows to scan (0 = all)")
    ap.add_argument("--max-success", type=int, default=0,
                    help="Cap on resolved trajectories written (0 = all). Random sample with --seed.")
    ap.add_argument("--include-failures", action="store_true",
                    help="Also write unresolved trajectories to sft_fail.jsonl")
    ap.add_argument("--max-fail", type=int, default=0, help="Cap on failure trajectories (0 = all)")
    ap.add_argument("--seed", type=int, default=0)
    ap.add_argument("--shuffle", action="store_true",
                    help="Shuffle output records (buffers selected rows in memory)")
    ap.add_argument("--strict-tools", action="store_true",
                    help="Drop trajectories calling tools absent from tools.json")
    ap.add_argument("--no-tools", action="store_true",
                    help="Do not embed the per-row tool list in each record")
    ap.add_argument("--batch-size", type=int, default=32)
    return ap


def main(argv=None) -> int:
    args = build_parser().parse_args(argv)
    return run(args)


if __name__ == "__main__":
    sys.exit(main())