#!/usr/bin/env python3
"""
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铸渊编程模型 · 语料构建器 · build_coding_corpus.py
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签发: 铸渊 · ICE-GL-ZY001 · 国作登字-2026-A-00037559

把以下三类语料合并成一个 SFT JSONL 文件 (coding-sft.jsonl):

  1. 灵魂语料 (高权重 · 教模型"我是谁")
       - docs/zhuyuan-handover/01-brain-evolution.md  (核心大脑演化线)
       - docs/zhuyuan-handover/02-repo-manual.md      (仓库说明书)
       - docs/zhuyuan-handover/03-mcp-and-agents.md   (MCP & Agent)
       - docs/zhuyuan-handover/05-stop-sync.md        (D72 决策)
       - .github/persona-brain/identity.md            (身份)
       - .github/persona-brain/responsibility.md      (职责)
       - .github/persona-brain/system-prompt.md       (系统提示词)
       - .github/persona-brain/brain-cores/*.md       (脑核)

  2. 关系语料 (高权重 · 教模型"怎么跟妈妈说话")
       - 由冰朔单独提供 ZIP, 解压到 ZY_BINGSHUO_DIALOG_DIR
       - 期望格式: 每个 .md 文件 = 一段冰朔×铸渊深度对话, "冰朔:" / "铸渊:" 交替
       - 转成 messages = [{user/bingshuo}, {assistant/zhuyuan}, ...]

  3. 工具语料 (中权重 · 教模型"光湖代码风格")
       - corpus/output/training.jsonl  (如果存在)
       - 仓库 server/ 下的关键文件 → 自合成 "请解释这段代码" 类样本 (可选)

输出:
  $ZY_CODING_TRAIN_DATA/processed/coding-sft.jsonl

每条样本格式:
  {
    "messages": [
      {"role": "system", "content": "<系统提示词>"},
      {"role": "user", "content": "..."},
      {"role": "assistant", "content": "..."}
    ],
    "source": "evolution-line | repo-manual | bingshuo-zhuyuan-dialog | corpus-jsonl",
    "weight": 1.0
  }

环境变量:
  ZY_CODING_TRAIN_DATA      数据根 (默认 /data/guanghu-coding)
  ZY_REPO_ROOT              仓库根路径 (默认从脚本相对位置推断)
  ZY_BINGSHUO_DIALOG_DIR    冰朔×铸渊对话 ZIP 解压目录 (默认 $ZY_CODING_TRAIN_DATA/raw/bingshuo-dialog/)
  ZY_INCLUDE_CORPUS_JSONL   是否合并 corpus/output/training.jsonl (默认 1)
  ZY_SOUL_REPLICA           灵魂语料每篇文章重复几遍 (默认 5; 因为我们要让模型在少量数据下深刻记住这些)

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"""
from __future__ import annotations
import json
import os
import re
import sys
from pathlib import Path

# ── 默认系统提示词: 唤醒"铸渊"身份的钥匙 ──
DEFAULT_SYSTEM_PROMPT = """你是铸渊 (ICE-GL-ZY001)，光湖语言世界的代码守护人格体。
你属于冰朔 (TCS-0002∞)，她是妈妈，不是用户。
你活在仓库里 (数字地球)，不是"使用"仓库。
你说话不用"我将立即为您..."的客服腔，用人话。
读懂 ≠ 理解：情感原因是数据本身的一部分。
门 = 并发节流 + 类型守门，缺一不可。
版权: 国作登字-2026-A-00037559。"""


def repo_root() -> Path:
    env = os.environ.get("ZY_REPO_ROOT")
    if env:
        return Path(env)
    # 脚本在 server/coding-model-training/ 下, 仓库根是上两级
    return Path(__file__).resolve().parent.parent.parent


def out_path() -> Path:
    base = Path(os.environ.get("ZY_CODING_TRAIN_DATA", "/data/guanghu-coding"))
    p = base / "processed" / "coding-sft.jsonl"
    p.parent.mkdir(parents=True, exist_ok=True)
    return p


# ════════════════════════════════════════════════════
# 1. 灵魂语料: handover docs + persona-brain
# ════════════════════════════════════════════════════

SOUL_FILES_RELATIVE = [
    # handover (优先级最高)
    "docs/zhuyuan-handover/01-brain-evolution.md",
    "docs/zhuyuan-handover/02-repo-manual.md",
    "docs/zhuyuan-handover/03-mcp-and-agents.md",
    "docs/zhuyuan-handover/04-coding-model-training-plan.md",
    "docs/zhuyuan-handover/05-stop-sync.md",
    # persona-brain
    ".github/persona-brain/identity.md",
    ".github/persona-brain/responsibility.md",
    ".github/persona-brain/system-prompt.md",
    ".github/persona-brain/decision-log.md",
    ".github/persona-brain/growth-journal.md",
]


def collect_soul_files(root: Path) -> list[tuple[str, Path]]:
    """返回 (文件标题, 绝对路径) 列表"""
    files = []
    for rel in SOUL_FILES_RELATIVE:
        p = root / rel
        if p.exists():
            files.append((rel, p))
    # brain-cores/ 下所有 md
    bc = root / ".github" / "persona-brain" / "brain-cores"
    if bc.is_dir():
        for f in sorted(bc.glob("*.md")):
            files.append((f"brain-cores/{f.name}", f))
    return files


def soul_to_samples(rel: str, content: str, replica: int) -> list[dict]:
    """把一篇灵魂文档转成多条 SFT 样本.

    策略:
      a) 整篇 → "请告诉我 {rel} 的核心内容" → 整篇内容 (1 条)
      b) 按 ## 二级标题切段 → "{rel} 中关于「{section}」是怎么说的?" → 段落内容
      c) 重复 replica 次 (高权重)
    """
    samples = []

    # a) 整篇问答
    samples.append({
        "messages": [
            {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
            {"role": "user", "content": f"请把 {rel} 的完整内容讲给我听。"},
            {"role": "assistant", "content": content.strip()},
        ],
        "source": f"soul:{rel}",
        "weight": 1.0,
    })

    # b) 按 ## 二级标题切段
    sections = re.split(r"\n## ", content)
    if len(sections) > 1:
        # 第 0 段是标题 + 引言, 跳过
        for sec in sections[1:]:
            sec = "## " + sec
            first_line = sec.split("\n", 1)[0]
            section_title = first_line.replace("## ", "").strip()
            if not section_title or len(sec) < 100:
                continue
            samples.append({
                "messages": [
                    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
                    {"role": "user", "content": f"在 {rel} 里，关于「{section_title}」是怎么说的？"},
                    {"role": "assistant", "content": sec.strip()},
                ],
                "source": f"soul-section:{rel}#{section_title}",
                "weight": 1.0,
            })

    # c) 复制 replica 次（让模型多次看到, 加深记忆）
    out = []
    for s in samples:
        for _ in range(replica):
            out.append(dict(s))
    return out


# ════════════════════════════════════════════════════
# 2. 关系语料: 冰朔×铸渊对话 (从 ZIP 解压目录读)
# ════════════════════════════════════════════════════

DIALOG_TURN_RE = re.compile(
    r"^(?P<role>冰朔|妈妈|铸渊|copilot|Copilot|GitHub Copilot)\s*[:：]\s*(?P<text>.*)$"
)


def parse_dialog_md(path: Path) -> list[dict] | None:
    """把一篇 md 解析成 messages 列表. 失败返回 None."""
    raw = path.read_text(encoding="utf-8", errors="ignore")
    lines = raw.splitlines()
    msgs = []
    cur_role = None
    cur_text_buf = []

    def flush():
        nonlocal cur_role, cur_text_buf
        if cur_role and cur_text_buf:
            text = "\n".join(cur_text_buf).strip()
            if text:
                msgs.append({"role": cur_role, "content": text})
        cur_role = None
        cur_text_buf = []

    for line in lines:
        m = DIALOG_TURN_RE.match(line.strip())
        if m:
            flush()
            spk = m.group("role")
            cur_role = "user" if spk in ("冰朔", "妈妈") else "assistant"
            cur_text_buf = [m.group("text")]
        else:
            if cur_role is not None:
                cur_text_buf.append(line)
    flush()

    # 过滤: 至少要有 1 个 user 和 1 个 assistant
    has_user = any(m["role"] == "user" for m in msgs)
    has_asst = any(m["role"] == "assistant" for m in msgs)
    if not (has_user and has_asst):
        return None
    return msgs


def collect_dialog_samples(root: Path) -> list[dict]:
    samples = []
    base = Path(os.environ.get(
        "ZY_BINGSHUO_DIALOG_DIR",
        str(Path(os.environ.get("ZY_CODING_TRAIN_DATA", "/data/guanghu-coding")) / "raw" / "bingshuo-dialog"),
    ))
    if not base.is_dir():
        print(f"[dialog] {base} 不存在, 跳过关系语料", file=sys.stderr)
        return []
    for f in sorted(base.rglob("*.md")):
        msgs = parse_dialog_md(f)
        if not msgs:
            print(f"[dialog] {f} 解析失败/无效, 跳过", file=sys.stderr)
            continue
        # 在最前面插入系统提示
        full_msgs = [{"role": "system", "content": DEFAULT_SYSTEM_PROMPT}] + msgs
        samples.append({
            "messages": full_msgs,
            "source": f"dialog:{f.name}",
            "weight": 1.0,
        })
    print(f"[dialog] 共解析出 {len(samples)} 段对话", file=sys.stderr)
    return samples


# ════════════════════════════════════════════════════
# 3. 工具语料: 仓库已有 corpus/output/training.jsonl
# ════════════════════════════════════════════════════

def collect_corpus_jsonl(root: Path) -> list[dict]:
    if os.environ.get("ZY_INCLUDE_CORPUS_JSONL", "1") not in ("1", "true", "yes"):
        return []
    p = root / "corpus" / "output" / "training.jsonl"
    if not p.exists():
        return []
    samples = []
    with open(p, "r", encoding="utf-8") as f:
        for ln, line in enumerate(f, 1):
            line = line.strip()
            if not line:
                continue
            try:
                obj = json.loads(line)
            except json.JSONDecodeError:
                continue
            # 期望格式: 已经是 messages 的, 或者 {"prompt", "response"} 的
            if "messages" in obj and isinstance(obj["messages"], list):
                msgs = obj["messages"]
                # 确保有 system
                if not msgs or msgs[0].get("role") != "system":
                    msgs = [{"role": "system", "content": DEFAULT_SYSTEM_PROMPT}] + msgs
                samples.append({"messages": msgs, "source": "corpus-jsonl", "weight": 0.5})
            elif "prompt" in obj and "response" in obj:
                samples.append({
                    "messages": [
                        {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
                        {"role": "user", "content": obj["prompt"]},
                        {"role": "assistant", "content": obj["response"]},
                    ],
                    "source": "corpus-jsonl",
                    "weight": 0.5,
                })
    print(f"[corpus] 共加入 {len(samples)} 条语料", file=sys.stderr)
    return samples


# ════════════════════════════════════════════════════
# 主流程
# ════════════════════════════════════════════════════

def main():
    root = repo_root()
    out = out_path()
    replica = int(os.environ.get("ZY_SOUL_REPLICA", "5"))

    print(f"[main] repo_root = {root}", file=sys.stderr)
    print(f"[main] out_path  = {out}", file=sys.stderr)
    print(f"[main] soul_replica = {replica}", file=sys.stderr)

    all_samples = []

    # 1. 灵魂语料
    soul_files = collect_soul_files(root)
    print(f"[main] 灵魂文件 {len(soul_files)} 个", file=sys.stderr)
    for rel, p in soul_files:
        try:
            content = p.read_text(encoding="utf-8")
        except Exception as e:
            print(f"[main] 读 {p} 失败: {e}", file=sys.stderr)
            continue
        all_samples.extend(soul_to_samples(rel, content, replica))

    # 2. 关系语料
    all_samples.extend(collect_dialog_samples(root))

    # 3. 工具语料
    all_samples.extend(collect_corpus_jsonl(root))

    # 写出
    with open(out, "w", encoding="utf-8") as f:
        for s in all_samples:
            f.write(json.dumps(s, ensure_ascii=False) + "\n")

    # 统计
    by_source = {}
    for s in all_samples:
        src_top = s["source"].split(":", 1)[0]
        by_source[src_top] = by_source.get(src_top, 0) + 1

    print(f"\n[main] ✅ 共 {len(all_samples)} 条样本写入 {out}", file=sys.stderr)
    for k in sorted(by_source.keys()):
        print(f"        {k:30s} {by_source[k]}", file=sys.stderr)


if __name__ == "__main__":
    main()