from typing import TypedDict
class User(TypedDict):
    name: str
    age: int
    is_active: bool
# 創(chuàng)建符合TypedDict定義的字典（在注解中聲明）
user: User = {
    "name": "張三",
    "age": 30,
    "is_active": True
}

def get_user_info(user: User) -> str:
    return f"用戶名：{user['name']}，年齡：{user['age']}"
print(get_user_info(user))  # 輸出：用戶名：張三，年齡：30

class PartialUser(TypedDict, total=False):
    name: str
    age: int
    email: str
# 所有字段都是可選的
partial_user: PartialUser = {
    "name": "李四"
    # age和email可以不提供
}

from typing_extensions import NotRequired
class UserProfile(TypedDict):
    name: str                    # 必需字段
    age: int                     # 必需字段
    email: NotRequired[str]      # 可選字段
    phone: NotRequired[str]      # 可選字段
# 可以不包含可選字段
profile1: UserProfile = {
    "name": "王五",
    "age": 25
}
# 也可以包含可選字段
profile2: UserProfile = {
    "name": "趙六",
    "age": 28,
    "email": "zhaoliu@example.com"
}

# 下面這段代碼在運(yùn)行時(shí)不會報(bào)錯(cuò)
# 但mypy等靜態(tài)檢查器會發(fā)出警告
user: User = {
    "name": "錯(cuò)誤",  # 這是str，沒問題
    "age": "三十",    # 應(yīng)該是int，類型錯(cuò)誤！
    "is_active": True
}

# TypedDict只能定義鍵的類型，不能定義方法
class BadExample(TypedDict):
    name: str
    def greet(self) -> str:  # 錯(cuò)誤！不允許方法
        return "Hello"

class APIResponse(TypedDict):
    success: bool
    data: NotRequired[list[dict]]
    error: NotRequired[str]
def process_response(response: APIResponse) -> None:
    if response["success"]:
        if "data" in response:
            print(f"處理數(shù)據(jù)：{response['data']}")
    else:
        if "error" in response:
            print(f"錯(cuò)誤信息：{response['error']}")
# 使用示例
success_resp: APIResponse = {
    "success": True,
    "data": [{"id": 1, "name": "商品1"}]
}
error_resp: APIResponse = {
    "success": False,
    "error": "認(rèn)證失敗"
}

class AnalysisResult(TypedDict):
    status: str
    score: float
    details: dict
def analyze_text(text: str) -> AnalysisResult:
    # 模擬分析邏輯
    return {
        "status": "completed",
        "score": 0.85,
        "details": {"length": len(text), "words": len(text.split())}
    }
result = analyze_text("這是一段測試文本")
# result的類型被明確為AnalysisResult，IDE可以提供智能提示