from __future__ import annotations

from collections import Counter
from typing import Any

from .models import NewsItem


SECTION_ORDER = [
    "模型与能力",
    "产品与应用",
    "开发与基础设施",
    "公司与资本",
    "政策与安全",
    "论文与研究",
    "观点与教程",
    "人物与动态",
]

SECTION_ALIASES = {
    "模型发布/更新": "模型与能力",
    "产品发布/更新": "产品与应用",
    "产品与工具": "产品与应用",
    "开发与工程": "开发与基础设施",
    "行业动态": "公司与资本",
    "行业与公司": "公司与资本",
    "论文研究": "论文与研究",
    "论文与研究": "论文与研究",
    "技巧与观点": "观点与教程",
    "观点与教程": "观点与教程",
    "人物与花絮": "人物与动态",
}


RULES = [
    ("政策与安全", ("监管", "政策", "安全", "风险", "滥用", "攻击", "合规", "版权")),
    ("论文与研究", ("论文", "研究", "arxiv", "cvpr", "benchmark", "评测", "实验")),
    ("开发与基础设施", ("sdk", "api", "mcp", "kubernetes", "框架", "开源", "github", "部署", "基础设施")),
    ("公司与资本", ("融资", "ipo", "上市", "招股书", "合作", "估值", "收购", "资本")),
    ("模型与能力", ("模型", "gpt", "claude", "gemini", "grok", "token", "参数", "多模态", "语音", "推理")),
    ("产品与应用", ("agent", "应用", "产品", "平台", "上线", "工具", "智能体")),
    ("观点与教程", ("教程", "观点", "方法论", "guide", "实践", "技巧")),
    ("人物与动态", ("黄仁勋", "纳德拉", "访谈", "演讲", "人物")),
]


def normalize_section_hint(section_hint: str) -> str:
    hint = (section_hint or "").strip()
    if hint in SECTION_ORDER:
        return hint
    return SECTION_ALIASES.get(hint, "")


def rule_classify(item: NewsItem) -> str:
    text = f"{item.title or item.title_raw} {item.summary or item.summary_raw}".lower()
    for section, keywords in RULES:
        if any(keyword.lower() in text for keyword in keywords):
            return section
    return "公司与资本"


def rank_score(item: NewsItem) -> int:
    text = f"{item.title or item.title_raw} {item.summary or item.summary_raw}"
    score = max(0, 200 - item.source_priority)
    if item.source_role == "primary":
        score += 10
    if item.canonical_url:
        score += 10
    if any(ch.isdigit() for ch in text):
        score += 10
    if item.duplicate_sources:
        score += min(20, len(item.duplicate_sources) * 5)
    score -= len(item.quality_flags) * 10
    return score


def classify_and_order_items(items: list[NewsItem]) -> tuple[list[NewsItem], dict[str, Any]]:
    llm_classified = 0
    hint_classified = 0
    rule_classified = 0
    invalid_llm_section_count = 0

    for item in items:
        if item.section:
            if item.section in SECTION_ORDER:
                llm_classified += 1
                continue
            invalid_llm_section_count += 1

        mapped = normalize_section_hint(item.section_hint)
        if mapped:
            item.section = mapped
            hint_classified += 1
        else:
            item.section = rule_classify(item)
            rule_classified += 1

    section_index = {section: index for index, section in enumerate(SECTION_ORDER)}
    ordered = sorted(
        items,
        key=lambda item: (
            section_index.get(item.section or "", len(SECTION_ORDER)),
            -rank_score(item),
            item.title or item.title_raw,
        ),
    )
    section_counts = Counter(item.section for item in ordered if item.section)
    report = {
        "input_count": len(items),
        "section_counts": dict(section_counts),
        "hint_classified": hint_classified,
        "rule_classified": rule_classified,
        "llm_classified": llm_classified,
        "fallback_classified": hint_classified + rule_classified,
        "invalid_llm_section_count": invalid_llm_section_count,
        "invalid_section_count": sum(1 for item in ordered if item.section not in SECTION_ORDER),
    }
    return ordered, report