"""动态编译唯一命令行入口。"""

from __future__ import annotations

import argparse
import os
import sys
from pathlib import Path

from scripts.provider_router import parse_model_reference

from .pipeline import run_pipeline
from .scoring.agentrm import (
    DEFAULT_BATCH_SIZE,
    DEFAULT_CONCURRENCY,
    DEFAULT_MAX_LENGTH,
    DEFAULT_RM_API_URL,
    DEFAULT_TIMEOUT,
)


def _provider_model(value: str) -> str:
    try:
        return parse_model_reference(value).value
    except ValueError as exc:
        raise argparse.ArgumentTypeError(str(exc)) from exc


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(
        prog="python -m scripts.dynamic_compile.fast",
        description="从一组 BenchFlow 历史轨迹生成一个动态编译候选 Skill。",
    )
    parser.add_argument("--traces", required=True, type=Path, help="BenchFlow 轨迹目录")
    parser.add_argument("--skill", required=True, type=Path, help="含根 SKILL.md 的 Skill 包")
    parser.add_argument("--score-output", type=Path, help="评分阶段产物目录")
    parser.add_argument("--output", type=Path, help="分析与候选 Skill 产物目录")
    parser.add_argument(
        "--model",
        required=True,
        type=_provider_model,
        help="所有外部模型调用使用的 provider/model",
    )
    parser.add_argument("--max-parallel", type=int, default=3)
    parser.add_argument(
        "--rm-api-url", default=os.environ.get("RM_API_URL", DEFAULT_RM_API_URL)
    )
    parser.add_argument(
        "--rm-max-length",
        type=int,
        default=os.environ.get("RM_MAX_LENGTH", str(DEFAULT_MAX_LENGTH)),
    )
    parser.add_argument(
        "--rm-timeout",
        type=float,
        default=os.environ.get("RM_TIMEOUT", str(DEFAULT_TIMEOUT)),
    )
    parser.add_argument(
        "--rm-concurrency",
        type=int,
        default=os.environ.get("RM_CONCURRENCY", str(DEFAULT_CONCURRENCY)),
    )
    parser.add_argument(
        "--rm-batch-size",
        type=int,
        default=os.environ.get("RM_BATCH_SIZE", str(DEFAULT_BATCH_SIZE)),
    )
    parser.add_argument(
        "--force",
        action="store_true",
        help="复用有效评分/Map 缓存，强制重建 Reduce、Patch 和候选 Skill",
    )
    return parser


def main(argv: list[str] | None = None) -> int:
    args = build_parser().parse_args(argv)
    try:
        result = run_pipeline(
            args.traces,
            args.skill,
            score_output=args.score_output,
            output=args.output,
            model=args.model,
            max_parallel=args.max_parallel,
            rm_api_url=args.rm_api_url,
            rm_max_length=args.rm_max_length,
            rm_timeout=args.rm_timeout,
            rm_concurrency=args.rm_concurrency,
            rm_batch_size=args.rm_batch_size,
            force=args.force,
        )
    except (OSError, RuntimeError, ValueError) as exc:
        print(f"error: {exc}", file=sys.stderr)
        return 2
    print(result)
    return 0