"""批量生成正演代理模型的原始数值试井数据集。

脚本读取数据生成配置，调用并行数据集生成器批量采样地层/井筒参数和流量制度，
运行底层数值求解器并把有效曲线写入 HDF5。它是训练前最上游的数据生产入口。
"""
# pylint: disable=import-error,wrong-import-position,broad-exception-caught

from __future__ import annotations
import argparse
import multiprocessing as mp
import sys
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
sys.path.append(str(ROOT))

from src.common.config import Config
from src.common.experiment_paths import config_for_stage
from src.data.dataset_generation import ParallelDatasetGenerator


def main() -> None:
    """按配置阶段启动并行数值试井样本生成，输出原始 HDF5 数据集路径。"""
    parser = argparse.ArgumentParser()
    parser.add_argument("--config", default=None)
    parser.add_argument(
        "--stage",
        choices=[
            "fixed_case",
            "case_neighborhood",
            "family_random",
            "family_random_hard",
            "family_random_v2_q",
        ],
        default=None,
    )
    parser.add_argument("--n-samples", type=int, default=None)
    parser.add_argument("--n-workers", type=int, default=None)
    parser.add_argument("--seed", type=int, default=None)
    parser.add_argument("--method", type=str, default=None)
    parser.add_argument(
        "--dataset-case",
        type=str,
        default=None,
        help="Generate only the named dataset_cases entry; omit to generate all cases",
    )
    parser.add_argument(
        "--dataset-tag",
        type=str,
        default=None,
        help="Optional tag injected into output dataset filename",
    )
    args = parser.parse_args()

    config_path = args.config
    if config_path is None:
        config_path = str(config_for_stage(args.stage) or Path("configs/data_gen.yaml"))

    # 没有dataset_cases时保持原来的单case行为；有case时按声明顺序依次生成。
    base_cfg = Config(config_path)
    case_names = [str(case.get("name", "")).strip() for case in base_cfg.dataset_cases]
    if any(not name for name in case_names) or len(case_names) != len(set(case_names)):
        raise ValueError("dataset_cases中的name必须非空且不能重复")

    if args.dataset_case is None:
        selected_cases = case_names or [None]
    elif args.dataset_case not in case_names:
        available = ", ".join(case_names) if case_names else "(none)"
        parser.error(f"unknown --dataset-case {args.dataset_case!r}; available: {available}")
    else:
        selected_cases = [args.dataset_case]

    for case_name in selected_cases:
        cfg = Config(config_path, dataset_case=case_name)
        cfg.ensure_dirs()
        if case_name is None:
            output_tag = args.dataset_tag
        else:
            output_tag = f"{args.dataset_tag}_{case_name}" if args.dataset_tag else case_name
            print(f"\n=== Generating dataset case: {case_name} ===")

        path = ParallelDatasetGenerator(
            cfg=cfg,
            n_workers=args.n_workers,
        ).generate(
            n_samples=args.n_samples,
            method=args.method,
            random_seed=args.seed,
            dataset_tag=output_tag,
        )
        print(path)


if __name__ == "__main__":
    mp.freeze_support()
    try:
        mp.set_start_method("spawn", force=True)
    except RuntimeError:
        pass
    main()