from __future__ import annotations

import argparse
import json
import os
import pathlib as pl
import subprocess
import sys
import time
from datetime import datetime, timezone


def utc_now_iso() -> str:
    return datetime.now(timezone.utc).isoformat()


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description=(
            "Run ensemble training followed by bayesian training using existing scripts."
        )
    )
    parser.add_argument(
        "--python",
        default=sys.executable,
        help="Python executable to use for child scripts (default: current interpreter).",
    )
    parser.add_argument(
        "--workdir",
        default=str(pl.Path(__file__).resolve().parent),
        help="Working directory for training scripts (default: this script directory).",
    )
    parser.add_argument(
        "--log-dir",
        default=None,
        help="Directory for log and summary files (default: <workdir>/logs).",
    )
    parser.add_argument(
        "--continue-on-error",
        action="store_true",
        help="Continue to the next stage even if a stage fails.",
    )
    parser.add_argument(
        "--dry-run",
        action="store_true",
        help="Print resolved commands and paths without running training.",
    )
    return parser.parse_args()


def run_stage(
    stage_name: str,
    command: list[str],
    workdir: pl.Path,
    log_file: pl.Path,
) -> dict[str, object]:
    started = utc_now_iso()
    start_time = time.monotonic()

    log_file.parent.mkdir(parents=True, exist_ok=True)
    with open(log_file, "w", encoding="utf-8") as log:
        log.write(f"[{started}] Starting stage: {stage_name}\n")
        log.write(f"Command: {' '.join(command)}\n")
        log.write(f"Working directory: {workdir}\n\n")

        process = subprocess.Popen(
            command,
            cwd=str(workdir),
            stdout=subprocess.PIPE,
            stderr=subprocess.STDOUT,
            text=True,
            bufsize=1,
            universal_newlines=True,
            env=os.environ.copy(),
        )

        if process.stdout is not None:
            for line in process.stdout:
                print(line, end="")
                log.write(line)

        return_code = process.wait()

        finished = utc_now_iso()
        duration_seconds = time.monotonic() - start_time
        log.write(
            (
                "\n"
                f"[{finished}] Finished stage: {stage_name}\n"
                f"Exit code: {return_code}\n"
                f"Duration seconds: {duration_seconds:.2f}\n"
            )
        )

    return {
        "stage": stage_name,
        "command": command,
        "started_at_utc": started,
        "finished_at_utc": finished,
        "duration_seconds": duration_seconds,
        "exit_code": return_code,
        "status": "success" if return_code == 0 else "failed",
        "log_file": str(log_file),
    }


def main() -> int:
    args = parse_args()

    workdir = pl.Path(args.workdir).resolve()
    log_dir = (
        pl.Path(args.log_dir).resolve()
        if args.log_dir is not None
        else workdir / "logs"
    )
    run_stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    run_log_dir = log_dir / f"overnight_{run_stamp}"

    ensemble_script = workdir / "train_ensemble.py"
    bayesian_script = workdir / "train_bayesian.py"

    for script in (ensemble_script, bayesian_script):
        if not script.exists():
            print(f"Error: required script not found: {script}")
            return 2

    stages = [
        ("ensemble", [args.python, str(ensemble_script)]),
        ("bayesian", [args.python, str(bayesian_script)]),
    ]

    if args.dry_run:
        print("Dry run: no training scripts will be executed.")
        print(f"Working directory: {workdir}")
        print(f"Run log directory: {run_log_dir}")
        for stage_name, command in stages:
            print(f"Stage {stage_name}: {' '.join(command)}")
        return 0

    run_log_dir.mkdir(parents=True, exist_ok=True)
    summary_path = run_log_dir / "run_summary.json"

    run_started = utc_now_iso()
    run_start_time = time.monotonic()
    stage_results: list[dict[str, object]] = []

    final_exit_code = 0
    for stage_name, command in stages:
        print(f"\n=== Starting {stage_name} training ===")
        log_file = run_log_dir / f"{stage_name}.log"
        result = run_stage(stage_name, command, workdir, log_file)
        stage_results.append(result)

        if int(result["exit_code"]) != 0 and not args.continue_on_error:
            final_exit_code = int(result["exit_code"])
            print(
                (
                    f"Stage '{stage_name}' failed with exit code {result['exit_code']}. "
                    "Stopping because --continue-on-error was not set."
                )
            )
            break

    if final_exit_code == 0:
        failed = [r for r in stage_results if int(r["exit_code"]) != 0]
        if failed:
            final_exit_code = int(failed[-1]["exit_code"])

    run_finished = utc_now_iso()
    total_duration = time.monotonic() - run_start_time

    summary = {
        "run_started_at_utc": run_started,
        "run_finished_at_utc": run_finished,
        "total_duration_seconds": total_duration,
        "workdir": str(workdir),
        "python_executable": args.python,
        "continue_on_error": args.continue_on_error,
        "final_exit_code": final_exit_code,
        "overall_status": "success" if final_exit_code == 0 else "failed",
        "stages": stage_results,
    }

    with open(summary_path, "w", encoding="utf-8") as f:
        json.dump(summary, f, indent=2)

    print("\n=== Overnight run complete ===")
    print(f"Summary: {summary_path}")
    print(f"Logs directory: {run_log_dir}")

    return final_exit_code


if __name__ == "__main__":
    raise SystemExit(main())