functools.wraps在Python裝飾器中不僅保留函數(shù)名稱和文檔字符串，還支持保留函數(shù)簽名以提升工具支持、正確追蹤多層裝飾器中的原始函數(shù)、簡(jiǎn)化調(diào)試流程，並允許自定義屬性複制。 1. 使用wraps可保留完整函數(shù)簽名，使IDE、linters和文檔生成工具更準(zhǔn)確識(shí)別裝飾後的函數(shù)；2. 在堆疊多個(gè)裝飾器時(shí)，仍能通過(guò)__name__正確識(shí)別原始函數(shù)，方便日誌記錄與調(diào)試；3. 調(diào)試時(shí)顯示正確的函數(shù)名和模塊信息，避免錯(cuò)誤追蹤困難；4. 可通過(guò)assigned參數(shù)自定義需複制的屬性，如僅複製__name__和__doc__，適用於特殊場(chǎng)景?？傊?，wraps在復(fù)雜場(chǎng)景中確保代碼正常運(yùn)行並提高可維護(hù)性。

When you're working with decorators in Python, functools.wraps is more than just a helper to preserve a function's name and docstring. While that's its most obvious benefit, there are some more advanced ways to put functools.wraps to work — especially when writing complex or reusable decorator patterns.

Here's how you can go beyond the basics.

1. Preserving Function Signatures for Better Tooling Support

One of the less-talked-about but highly practical uses of functools.wraps is preserving the full function signature. This helps tools like IDEs, linters, and documentation generators understand your decorated functions better.

For example, if you write a decorator that wraps a function without using wraps , the wrapper will show up as taking *args, **kwargs , which makes autocomplete and tooltips less useful.

 from functools import wraps

def my_decorator(f):
    @wraps(f)
    def wrapper(*args, **kwargs):
        return f(*args, **kwargs)
    return wrapper

By using wraps , the original function's signature (including parameter names, defaults, and annotations) gets copied over to the wrapper. This makes debugging and tool support much smoother, especially in larger codebases or libraries.

2. Chaining Decorators Without Losing Track of the Original Function

When you stack multiple decorators on a function, it can get tricky to know what the "real" function is underneath all the layers. functools.wraps helps keep track of the original function by updating each wrapper's attributes along the chain.

Let's say you have two decorators:

 from functools import wraps

def debug(f):
    @wraps(f)
    def wrapper(*args, **kwargs):
        print(f"Calling {f.__name__}")
        return f(*args, **kwargs)
    return wrapper

def timer(f):
    @wraps(f)
    def wrapper(*args, **kwargs):
        start = time.time()
        result = f(*args, **kwargs)
        print(f"Time taken: {time.time() - start:.4f}s")
        return result
    return wrapper

Now, if you apply both:

 @debug
@timer
def my_function(x, y):
    return xy

Even after stacking, my_function.__name__ still shows 'my_function' , not 'wrapper' . That makes logging, introspection, and testing easier — especially when debugging why something isn't behaving as expected.

3. Using Wraps to Make Debugging Easier

Without wraps , every decorated function looks like a wrapper, which makes debugging harder. For instance, if you hit an error deep inside a decorated function and look at the traceback, you might see something like this:

 File "example.py", line 10, in wrapper

But if you use wraps , the traceback will correctly show the original function name and module, making it easier to locate the issue.

Also, if you're inspecting objects at runtime (eg, for serialization, API generation, or dynamic routing), having the correct __name__ , __module__ , and __doc__ makes a big difference.

A few key things wraps updates:

• __name__ : So the function appears with its real name.
• __doc__ : Keeps the original docstring visible.
• __module__ : Helps with locating where the function was defined.
• Other special attributes used by introspection tools.

4. Customizing What Gets Wrapped

While wraps copies a standard set of attributes, you can also customize which attributes you want to update by using the optional assigned and updated parameters.

 from functools import WRAPPER_ASSIGNMENTS, wraps

# Only copy name and docstring
wraps(f, assigned=('__name__', '__doc__'))

This can be handy in edge cases where you want to avoid copying certain attributes (like __annotations__ ) or need to handle them differently.

Just note: Most of the time, sticking with the default behavior is best unless you have a specific reason to change it.

So while functools.wraps seems simple on the surface, it quietly enables a lot of powerful behavior behind the scenes. It's not just about making your code look right — it helps make your code work right in more complex scenarios.

And honestly, once you start building reusable decorators or working in a team setting, skipping wraps becomes a pain point pretty fast. So yeah, just always use it — it's worth it.

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