"""LLM 翻译 + 投资事件抽取数据模型 (M4)。

EnTranslatedArticle 是 M4 最终落盘格式，包含中英文双语内容和抽取的事件。
EventExtraction 是 LLM JSON 输出直接映射，经 Pydantic 强校验。
"""

import re
from datetime import datetime
from enum import StrEnum

from pydantic import BaseModel, Field, field_validator


class Sentiment(StrEnum):
    """事件情绪倾向。"""

    POSITIVE = "positive"  # 利好
    NEUTRAL = "neutral"    # 中性
    NEGATIVE = "negative"  # 利空


# 国际财经事件类型（LLM Prompt 中展示）
INTERNATIONAL_EVENT_TYPES: tuple[str, ...] = (
    "财报披露",
    "并购收购",
    "产品发布",
    "监管政策",
    "宏观经济",
    "央行决议",
    "行业动态",
    "技术突破",
    "高管变动",
    "诉讼法律",
    "市场异动",
    "地缘政治",
    "大宗商品",
    "外汇波动",
    "其他",
)

# 美股代码正则：1-5 个大写字母
_US_STOCK_RE = re.compile(r"^[A-Z]{1,5}$")

MIN_IMPORTANCE = 1
MAX_IMPORTANCE = 5


class EventExtraction(BaseModel):
    """LLM 输出的单个事件，直接映射 JSON。"""

    event_type: str = Field(..., description="事件类型，见 INTERNATIONAL_EVENT_TYPES")
    stock_codes: list[str] = Field(
        default_factory=list,
        description="涉及美股代码，如 AAPL、TSLA；无相关股票时为空",
    )
    sentiment: Sentiment = Field(..., description="positive/neutral/negative")
    importance: int = Field(
        ..., ge=MIN_IMPORTANCE, le=MAX_IMPORTANCE, description="1-5 重要程度"
    )
    summary_zh: str = Field(
        default="",
        max_length=200,
        description="一句话中文事件摘要",
    )

    @field_validator("stock_codes")
    @classmethod
    def _validate_stock_codes(cls, v: list[str]) -> list[str]:
        """剔除非美股代码格式、去重、统一大写。"""
        cleaned: list[str] = []
        seen: set[str] = set()
        for code in v:
            s = (code or "").strip().upper()
            if not s or not _US_STOCK_RE.match(s):
                continue
            if s not in seen:
                seen.add(s)
                cleaned.append(s)
        return cleaned

    @field_validator("event_type")
    @classmethod
    def _normalize_event_type(cls, v: str) -> str:
        s = (v or "").strip()
        return s if s else "其他"


class LLMTranslationOutput(BaseModel):
    """LLM 单次调用的完整输出 JSON 映射。"""

    title_zh: str = Field(..., description="中文翻译标题")
    content_zh: str = Field(..., description="中文翻译正文")
    events: list[EventExtraction] = Field(
        default_factory=list, description="提取的投资事件列表"
    )


class EnTranslatedArticle(BaseModel):
    """M4 最终落盘格式：双语文章 + 抽取事件 + 调用元信息。"""

    # ── 来源标识 ──
    source_id: str
    source_name: str
    url: str
    url_hash: str

    # ── 双语内容 ──
    title: str = ""              # 英文原标题
    title_zh: str = ""           # 中文翻译标题
    content_en: str = ""         # 英文原文
    content_zh: str = ""         # 中文翻译
    publish_time: str = ""       # ISO 8601
    word_count: int = 0          # 英文词数
    word_count_zh: int = 0       # 中文译文字数

    # ── 抽取事件 ──
    events: list[EventExtraction] = Field(default_factory=list)

    # ── 调用元信息 ──
    provider: str = ""           # deepseek / qwen
    model: str = ""
    translated_at: datetime = Field(default_factory=datetime.now)
    attempts: int = 1            # 实际调用次数（含重试）
    prompt_tokens: int | None = None
    completion_tokens: int | None = None

    def short_summary(self) -> str:
        codes = set()
        for ev in self.events:
            codes.update(ev.stock_codes)
        codes_str = ",".join(sorted(codes)[:5]) or "-"
        return (
            f"[{self.source_id}] {self.title[:30]}... "
            f"→ {len(self.events)}events, stocks: {codes_str}"
        )


class LLMCallError(Exception):
    """LLM 调用失败（网络 / 解析 / 校验）。"""

    def __init__(self, reason: str, *, attempts: int = 0) -> None:
        super().__init__(reason)
        self.reason = reason
        self.attempts = attempts