📖 薪资谈判与职业发展¶

def calculate_total_compensation(
    base_monthly: float,
    months: int = 15,
    bonus_months: float = 3,
    sign_on: float = 0,
    sign_on_years: int = 1,
    rsu_total: float = 0,
    vest_years: int = 4,
    monthly_benefits: float = 3000
):
    """
    计算AI岗位总年包

    Args:
        base_monthly: 基本月薪（税前）
        months: 月薪数（含固定月度奖）
        bonus_months: 年终绩效奖金（月数）
        sign_on: 签字费总额
        sign_on_years: 签字费分摊年数
        rsu_total: RSU/股票总包
        vest_years: 股票归属年数
        monthly_benefits: 月度补贴
    """
    annual_base = base_monthly * months
    annual_bonus = base_monthly * bonus_months
    annual_sign_on = sign_on / sign_on_years
    annual_equity = rsu_total / vest_years
    annual_benefits = monthly_benefits * 12

    total = annual_base + annual_bonus + annual_sign_on + annual_equity + annual_benefits

    print(f"=== 年包拆解 ===")
    print(f"基本年薪:   {annual_base/10000:>8.1f}W ({annual_base/total*100:.0f}%)")
    print(f"年终奖金:   {annual_bonus/10000:>8.1f}W ({annual_bonus/total*100:.0f}%)")
    print(f"签字费/年:  {annual_sign_on/10000:>8.1f}W ({annual_sign_on/total*100:.0f}%)")
    print(f"股票/年:    {annual_equity/10000:>8.1f}W ({annual_equity/total*100:.0f}%)")
    print(f"补贴/年:    {annual_benefits/10000:>8.1f}W ({annual_benefits/total*100:.0f}%)")
    print(f"{'='*30}")
    print(f"总年包:     {total/10000:>8.1f}W")

    # 税后估算（简化，不含专项扣除）
    annual_tax = estimate_annual_tax(total)
    print(f"预估年税:   {annual_tax/10000:>8.1f}W")
    print(f"税后年收入: {(total - annual_tax)/10000:>8.1f}W")
    print(f"税后月收入: {(total - annual_tax)/12/10000:>8.2f}W")

    return total

def estimate_annual_tax(annual_income):
    """简化个税计算（不含专项扣除和社保）"""
    taxable = annual_income - 60000  # 起征点
    if taxable <= 0: return 0

    brackets = [
        (36000, 0.03), (144000, 0.10), (300000, 0.20),
        (420000, 0.25), (660000, 0.30), (960000, 0.35),
        (float('inf'), 0.45)
    ]
    deductions = [0, 2520, 16920, 31920, 52920, 85920, 181920]

    for i, (limit, rate) in enumerate(brackets):  # enumerate同时获取索引和值
        if taxable <= limit:
            return taxable * rate - deductions[i]
    return taxable * 0.45 - 181920

# 示例：某大厂P7 offer
print("【示例1：互联网大厂 P7级别】")
calculate_total_compensation(
    base_monthly=35000,
    months=16,
    bonus_months=3,
    sign_on=100000,
    sign_on_years=1,
    rsu_total=400000,
    vest_years=4
)

print("\n【示例2：大模型创业公司 高级算法】")
calculate_total_compensation(
    base_monthly=45000,
    months=14,
    bonus_months=2,
    sign_on=200000,
    sign_on_years=2,
    rsu_total=0,  # 期权不好估值
    vest_years=4
)

def evaluate_offer(
    company_name: str,
    total_comp: float,        # 总年包(万)
    growth_score: int,         # 成长空间(1-10)
    tech_depth: int,           # 技术深度(1-10)
    wlb: int,                  # Work-Life Balance(1-10)
    team_vibe: int,            # 团队氛围(1-10)
    stability: int,            # 岗位稳定性(1-10)
    location_score: int,       # 城市/通勤(1-10)
    brand: int,                # 品牌背书(1-10)
    weights: dict = None
):
    """
    Offer评估量化模型

    不同阶段权重建议:
    - 应届: 成长>品牌>技术>薪资
    - 3-5年: 技术>薪资>成长>WLB
    - 5-8年: 薪资>技术>WLB>稳定
    """
    if weights is None:
        weights = {
            'comp': 0.25,
            'growth': 0.20,
            'tech': 0.15,
            'wlb': 0.10,
            'team': 0.10,
            'stability': 0.08,
            'location': 0.05,
            'brand': 0.07
        }

    # 薪资归一化(假设range 20-200W)
    comp_score = min(10, max(1, (total_comp - 20) / 18))

    score = (
        weights['comp'] * comp_score +
        weights['growth'] * growth_score +
        weights['tech'] * tech_depth +
        weights['wlb'] * wlb +
        weights['team'] * team_vibe +
        weights['stability'] * stability +
        weights['location'] * location_score +
        weights['brand'] * brand
    )

    print(f"\n{'='*40}")
    print(f"  {company_name} Offer评估")
    print(f"{'='*40}")
    print(f"  总年包:     {total_comp}W (得分: {comp_score:.1f}/10)")
    print(f"  成长空间:   {growth_score}/10")
    print(f"  技术深度:   {tech_depth}/10")
    print(f"  WLB:        {wlb}/10")
    print(f"  团队氛围:   {team_vibe}/10")
    print(f"  稳定性:     {stability}/10")
    print(f"  城市/通勤:  {location_score}/10")
    print(f"  品牌背书:   {brand}/10")
    print(f"{'='*40}")
    print(f"  综合得分:   {score:.2f} / 10")

    return score

# 对比两个offer
score_a = evaluate_offer("字节跳动", 75, 8, 8, 5, 7, 7, 7, 9)
score_b = evaluate_offer("AI创业公司", 65, 9, 9, 7, 8, 4, 6, 3)

print(f"\n推荐: {'字节跳动' if score_a > score_b else 'AI创业公司'}")