Effortless Async Methods: SmartAsync Integration For Switcher

Dive into Asynchronous Power: Why SmartAsync Integration for Switcher is a Game-Changer

Hey guys, let's talk about something super exciting that's going to make your life a whole lot easier when dealing with asynchronous Python. We're proposing a killer feature: native smartasync integration directly into Switcher. This isn't just a small tweak; it's a massive quality-of-life improvement that will automatically wrap your async methods, letting them work flawlessly in both synchronous and asynchronous contexts without you having to lift a finger for manual decoration. Imagine building robust APIs and command-line interfaces (CLIs) where your async functions just work, no matter how they're called. That's the dream, right? This integration is all about removing boilerplate, reducing cognitive load, and supercharging your development workflow, especially within genropy and smartswitch environments. We want to empower you to write elegant, performant, and flexible code that seamlessly bridges the sync and async worlds. This isn't just about adding a new button; it's about fundamentally enhancing how you interact with async patterns, making them accessible and manageable even for those complex mixed-mode applications. We're talking about a significant upgrade to how Switcher handles asynchronous operations, making it more intuitive and powerful than ever before. This will truly differentiate Switcher as a go-to tool for developers who demand both performance and ease of use in their Python projects. So, if you've ever wrestled with async def in a synchronous CLI or vice-versa, get ready for a solution that's as smart as it is simple. This proposed enhancement is poised to solve those common pain points and make developing with asynchronous Python a truly enjoyable experience.

Why We Need This: The Motivation Behind SmartAsync's New Role

Now, let's get down to the nitty-gritty of why this feature is so crucial. When you're building modern Python applications, especially those that interact with external services, databases, or network requests, you're inevitably going to run into asynchronous code. You'll have async def methods doing all the heavy lifting. The challenge arises when these async methods need to play nicely with different parts of your application, some of which might be synchronous (like a CLI tool) and others inherently asynchronous (like a FastAPI web server running on uvicorn). The current workaround involves manually decorating every single async method with @smartasync from the smartasync library. While smartasync itself is an incredible tool that allows you to call async functions from sync code (and vice versa) without explicit await or asyncio.run(), the manual application of @smartasync can quickly become tedious and lead to repetitive code. Think about it: every time you create an async method that you might want to call from a sync context, you have to remember to add that decorator. It clutters your code, adds visual noise, and frankly, it's just one more thing for developers to worry about. We believe that Switcher, being a central dispatch mechanism, is the perfect place to abstract this complexity away. By integrating smartasync directly, we can streamline the developer experience, making Switcher an even more powerful and elegant solution for managing method calls across different execution contexts. This isn't just about saving a few keystrokes; it's about making your codebase cleaner, more maintainable, and significantly less error-prone when navigating the complexities of async Python. It truly simplifies the mental model required to work with mixed sync/async code, allowing developers to focus on business logic rather than boilerplate. This is about making your async development feel truly seamless.

from smartasync import smartasync

class MyHandler:
    api = Switcher()

    @api
    @smartasync  # ← Manual decoration required, which we want to avoid!
    async def my_method(self):
        await asyncio.sleep(0.1)
        return "result"

The problem is crystal clear: every single async method requires this manual @smartasync decoration. This isn't scalable for larger projects or teams, and it can introduce inconsistencies if someone forgets to add the decorator. We can do better, and Switcher is ready to lead the charge in making async method handling incredibly intuitive.

The Game-Changer: Proposed Solution for Seamless Integration

Alright, let's talk solutions, guys! Our goal here is to make Switcher so intelligent that it handles smartasync for you, automatically wrapping async methods when needed. We're proposing two brilliant levels of control, giving you both convenience and ultimate flexibility. First up, we've got the global (Switcher-level) integration. This is for those of you who know most, if not all, of your Switcher-managed methods will be async and you want them always callable from a sync context. Imagine setting a single flag when you instantiate your Switcher, and boom! All subsequent async methods registered with that Switcher are automatically wrapped with smartasync. No more @smartasync above every async def! It's super clean, super efficient, and perfect for applications where your primary interaction mode (like a CLI) is synchronous but your underlying logic is heavily asynchronous. This approach reduces boilerplate code significantly, making your method definitions much cleaner and easier to read. It's truly a "set it and forget it" kind of magic, letting you focus on the what your methods do, not how they're called across sync/async boundaries. This global setting is perfect for streamlining development in environments where a consistent async-to-sync wrapping strategy is desired across a whole suite of published methods. It drastically cuts down on repetitive code and boosts developer productivity by simplifying the interface to async operations.

class MyHandler:
    # Auto-wrap all async methods published through this Switcher instance
    api = Switcher(smartasync=True)

    @api  # ← smartasync applied automatically here, no manual decorator needed!
    async def my_method(self):
        await asyncio.sleep(0.1)
        return "result"

handler = MyHandler()

# Works seamlessly in a sync context!
callable_sync = handler.api('my_method')
result_sync = callable_sync(handler)  # ✓ No await needed, smartasync handles it!

# And it still works great in an async context too!
# async def some_other_async_func():
#     callable_async = handler.api('my_method') # Still returns a coroutine here
#     result_async = await callable_async(handler)

But wait, there's more! What if you need more granular control? That's where our second level comes in: per-call (method-level) smartasync wrapping. This is awesome for situations where you might have a Switcher that exposes a mix of sync and async methods, or where you only want to apply smartasync wrapping for specific calls to an async method, perhaps for a particular context. By default, your Switcher could have smartasync=False, meaning it behaves exactly as it does today. But when you fetch a callable using handler.api('my_method'), you can explicitly request smartasync=True for that specific call. This gives you unparalleled control. You can have the same underlying async method, but retrieve it in a wrapped (sync-callable) form for your CLI, and in its pure async form (returning a coroutine) for your HTTP API. The flexibility here is just incredible, allowing you to optimize performance by only wrapping when strictly necessary, or to provide different interfaces to the same backend logic depending on the caller's context. This fine-grained control ensures that Switcher remains incredibly versatile, catering to the most demanding and varied application architectures. It's all about giving you the power to decide how and when your async methods are made accessible.

class MyHandler:
    api = Switcher()  # smartasync=False by default (current behavior)

    @api
    async def my_method(self):
        return "result"

handler = MyHandler()

# On-demand wrapping for a sync context
wrapped_for_sync = handler.api('my_method', smartasync=True)
result_from_sync = wrapped_for_sync(handler)  # ✓ Works directly in sync code

# Without wrapping, for direct async use (e.g., within an `async def` function)
pure_async_callable = handler.api('my_method')  # Returns a coroutine in a sync context, needs await in async context
# await pure_async_callable(handler) # This would be needed in an async function

These two levels of control mean you're covered no matter your project's complexity. You get to choose the level of automation that best fits your needs, making Switcher an even more indispensable tool for modern Python development. It's about empowering you to write cleaner, more adaptable, and more maintainable async code without the usual headaches.

Real-World Scenarios: SmartAsync in Action

Let's get practical and talk about some awesome real-world scenarios where this smartasync integration for Switcher is going to absolutely shine. This isn't just theoretical; these are the everyday problems it will solve, making your development experience so much smoother. Our first major use case is building CLI applications with async methods. Guys, how many times have you wanted to expose a powerful async function, like a database query or an API call, directly through a command-line interface? Traditionally, this means a lot of boilerplate to manage the async event loop in a sync CLI context. But with Switcher(smartasync=True), that pain is gone. Your DatabaseHandler can have all its methods defined as async def, interacting with aiohttp or httpx for non-blocking I/O, and yet your Publisher can expose them for CLI use without a second thought. The CLI, which runs in a synchronous context (no event loop), can just call smpub db get_user as if it were a regular sync function. This is pure magic, because smartasync is transparently managing the event loop for you in the background when it detects a sync caller. Your users get the responsiveness of async operations, and you get the simplicity of writing async code without worrying about how it's consumed by sync tools. It's a massive win for consistency and code reuse across your application stack. Imagine your users running smpub db get_user --user_id 123 and getting results from an async database query without any manual asyncio.run() in your CLI wrapper! This kind of seamless operation truly elevates the developer experience and makes genropy's smartswitch an even more versatile tool for building powerful, dual-mode applications. The smartasync=True flag at the Switcher level is a game-changer for CLI developers.

class AppPublisher(Publisher):
    def on_init(self):
        self.db = DatabaseHandler()
        # CLI needs sync calls, but the underlying methods are async
        # Switcher will auto-wrap them for sync compatibility
        self.publish('db', self.db)

class DatabaseHandler:
    api = Switcher(smartasync=True)  # ← Auto-wrap all async methods for CLI compatibility!

    @api
    async def get_user(self, user_id: int):
        """Async database query example using aiohttp"""
        print(f"Fetching user {user_id} asynchronously...")
        async with aiohttp.ClientSession() as session:
            # Simulate an async network call or DB query
            response = await session.get(f"https://api.example.com/users/{user_id}")
            return await response.json()

# CLI usage (which typically runs in a sync context)
# $ smpub db get_user --user_id 123  # ✓ This will now just work out-of-the-box!
# The 'smpub' command would execute the wrapped callable in a sync fashion.

Next up, consider Use Case 2: building a mixed sync/async API. Sometimes, not all your methods need to be async-wrapped. Maybe you have some quick, CPU-bound sync operations alongside your I/O-bound async ones. With Switcher's default smartasync=False and the per-call control, you get the best of both worlds. You can define a MixedHandler with both sync_method and async_method. When you fetch sync_call = handler.api('sync_method'), it's just a regular Python function. No overhead, no wrapping. But for async_method, if you know you're calling it from a sync context, you can explicitly grab async_call = handler.api('async_method', smartasync=True). This allows you to optimize for performance by only applying the smartasync overhead when it's truly necessary. It's about surgical precision in your application of async compatibility, giving you the power to mix and match as your architecture demands. This level of control is incredibly valuable for complex applications that need to balance different types of operations efficiently. It means you don't have to compromise on either sync or async performance; you can truly have it all with intelligent, context-aware method retrieval.

class MixedHandler:
    api = Switcher() # Default smartasync=False

    @api
    def sync_method(self):
        print("Executing a synchronous operation.")
        return "sync_result"

    @api
    async def async_method(self):
        print("Executing an asynchronous operation...")
        await asyncio.sleep(0.1)
        return "async_result"

handler = MixedHandler()

# Calling the sync method - no wrapping needed, direct call
sync_call = handler.api('sync_method')
print(f"Sync method output: {sync_call(handler)}")

# Calling the async method for a sync context - request smartasync wrapping
async_call_for_sync_context = handler.api('async_method', smartasync=True)
print(f"Async method output (via sync wrapper): {async_call_for_sync_context(handler)}")

# Calling the async method for an async context - no smartasync wrapping
# This would return a coroutine that you'd await in an async function.
# async def another_async_caller():
#     pure_async_callable = handler.api('async_method')
#     print(f"Async method output (pure async): {await pure_async_callable(handler)}")

Finally, let's talk about Use Case 3: the ultimate HTTP + CLI Dual Mode. This is where Switcher(smartasync=True) truly shines, making your application inherently flexible across multiple serving modes. Imagine an async def fetch_data method that uses httpx.AsyncClient to grab data from an external API. This method is perfectly suited for a FastAPI endpoint, where FastAPI and uvicorn are already managing the event loop and await calls. But what if you want to run the exact same method from your CLI? With smartasync=True configured on your Switcher, your CLI can call $ smpub app fetch_data --url "https://api.example.com" and it just works. The smartasync wrapper intelligently handles the transition between sync and async contexts. Conversely, when FastAPI calls fetch_data via Switcher, it receives the original coroutine function, allowing await to proceed normally, without unnecessary wrapping overhead. This dual-mode capability drastically simplifies your architecture, allowing you to define your core business logic once, as async functions, and deploy it everywhere. No need for separate sync/async versions of the same logic, no complex adapters. It's elegant, efficient, and embodies the genropy spirit of making powerful tools easy to use. This is the holy grail for developers building versatile applications, saving countless hours of refactoring and maintenance. The ability to use the same codebase for both interactive command-line operations and high-performance web services is a testament to the power of this integration, making your projects incredibly adaptable and future-proof. It truly unlocks the full potential of asynchronous Python for smartswitch-powered applications, paving the way for more robust and maintainable codebases. This integration means less code, fewer bugs, and more flexible deployment options for everyone.

Under the Hood: How SmartAsync Integration Works

Alright, for those of you who love to peek behind the curtain, let's break down how we're going to make this magic happen inside Switcher. This isn't just a simple patch; it involves thoughtful modifications to Switcher's core to gracefully handle smartasync integration. First up, we'll be making some key changes to the Switcher.__init__() method. We're adding a brand new parameter, smartasync: bool = False. This small but mighty addition will allow you to control the default smartasync behavior for all methods published through that Switcher instance. Setting it to True means every async method you register will automatically get the smartasync treatment, making it callable from sync contexts by default. We'll store this flag internally as self._smartasync, giving us a persistent setting for the Switcher's lifecycle. This is the foundation for the global control, enabling that "set it and forget it" capability we talked about earlier. It's a clean, non-intrusive addition that respects Switcher's existing API while dramatically extending its capabilities. This small change in the constructor is what unlocks the global automation, streamlining async method handling across your entire Switcher instance. It’s a crucial step in simplifying your genropy development workflow by reducing the need for repetitive decorators across numerous async methods.

class Switcher:
    def __init__(
        self,
        name: str | None = None,
        prefix: str | None = None,
        parent: "Switcher | None" = None,
        wrap_methods: bool = True,
        smartasync: bool = False,  # ← NEW: This flag controls auto-wrapping of async methods
    ):
        self._name = name
        self._prefix = prefix
        self._parent = parent
        self._wrap_methods = wrap_methods
        self._smartasync = smartasync  # ← Store this new flag internally
        # ... rest of Switcher's initialization logic continues here
        self._methods = {}

Next, the real brains of the operation lies within Switcher's __call__() method. This is the method responsible for getting a callable for the given method name. We're enhancing it to intelligently decide whether to apply smartasync wrapping. We'll introduce a new smartasync: bool | None = None parameter to __call__() itself. This allows for our per-call control, letting you override the Switcher-level setting if you need to. The logic will be simple yet powerful: first, it determines should_wrap_async. If smartasync is explicitly passed to __call__ (not None), that takes precedence. Otherwise, it falls back to the self._smartasync value set in the constructor. If should_wrap_async is True, it will then call a new helper method, _create_smartasync_wrapper, to do the actual wrapping. If should_wrap_async is False, or if the method isn't even async, it proceeds with the normal _SwitchCall behavior. This intelligent routing ensures that smartasync wrapping is only applied when explicitly requested or globally configured, avoiding unnecessary overhead. This is where the magic of conditional, context-aware wrapping happens, ensuring Switcher remains performant and flexible for all scenarios. This logic is key to providing both global convenience and fine-grained control, making Switcher an adaptable tool for any async challenge you throw at it. It's carefully designed to preserve the existing behavior by default, ensuring full backward compatibility while introducing powerful new capabilities.

def __call__(self, key: str, smartasync: bool | None = None) -> Callable:
    """
    Get a callable for the given method, intelligently applying smartasync wrapping.

    Args:
        key: The name of the method to retrieve.
        smartasync: Override the Switcher-level smartasync setting for this specific call.
                   - True: Force wrapping with smartasync (if method is async).
                   - False: Explicitly prevent wrapping with smartasync.
                   - None: Use the Switcher's default self._smartasync setting (this is the default).
    """
    entry = self._methods[key]

    # Determine if we should wrap with smartasync, respecting the call-level override
    should_wrap_async = smartasync if smartasync is not None else self._smartasync

    if should_wrap_async:
        # If wrapping is desired, defer to the helper method
        return self._create_smartasync_wrapper(entry)
    else:
        # Otherwise, proceed with the normal Switcher call behavior
        return _SwitchCall(self, entry)

Finally, we'll introduce a helper method: _create_smartasync_wrapper. This private method is responsible for the actual smartasync application. It first checks for the smartasync library, raising a clear ImportError if it's not installed, guiding the user on how to fix it. This is important because smartasync will be an optional dependency. Then, it creates a normal _SwitchCall callable (which encapsulates the method and Switcher context). The crucial step is checking if entry.func (the underlying method) is an asyncio.iscoroutinefunction. If it is, we wrap this _SwitchCall in a small wrapper function, which smartasync can then decorate. This intermediate wrapper ensures that smartasync is applied correctly to the callable returned by _SwitchCall, not directly to the original method, maintaining the Switcher's internal dispatch mechanism. If the function isn't async, smartasync would be a no-op anyway, so we just return the _SwitchCall as-is. This careful implementation ensures we only apply smartasync where it's truly needed, optimizing performance and maintaining Switcher's architectural integrity. This granular control and intelligent wrapping make Switcher incredibly robust and adaptable to any async scenario. We're not just tacking on a feature; we're integrating smartasync in a deep, thoughtful way that enhances Switcher's core capabilities. This ensures efficiency and avoids unnecessary overhead for synchronous methods, making the entire system incredibly performant and flexible. It's a testament to the power of thoughtful design in modern Python tooling.

def _create_smartasync_wrapper(self, entry):
    """Create a smartasync-wrapped callable for the given method entry."""
    try:
        from smartasync import smartasync # Attempt to import smartasync
    except ImportError:
        raise ImportError(
            "smartasync is required for smartasync=True. "
            "Please install it with: pip install smartasync"
        ) from None # Suppress original exception chain for clarity

    # First, get the standard Switcher callable that wraps the method
    switch_call = _SwitchCall(self, entry)

    # Only apply smartasync if the original function is truly asynchronous
    if asyncio.iscoroutinefunction(entry.func):
        # Create an intermediate wrapper function that smartasync can then decorate.
        # This wrapper ensures that the smartasync decorator operates on the
        # _SwitchCall's ability to dispatch, rather than directly on the raw function.
        def wrapper(*args, **kwargs):
            # When this wrapper is called, it in turn calls the actual switch_call
            # This ensures Switcher's context and argument binding are maintained.
            return switch_call(*args, **kwargs)

        # Apply the smartasync decorator to our intermediate wrapper
        # smartasync will now handle the sync/async context transition for 'wrapper'
        wrapped_callable = smartasync(wrapper)
        return wrapped_callable
    else:
        # If the original method is not asynchronous, smartasync would be a no-op anyway.
        # So, we just return the standard Switcher callable without any extra wrapping.
        return switch_call

Why This Matters: The Big Benefits for Your Code

Alright, guys, let's talk about the massive payoffs this smartasync integration will bring to your genropy and smartswitch projects. These aren't just minor improvements; these are fundamental shifts that will make your development process smoother, your code cleaner, and your applications more robust. First and foremost, we're talking about significantly cleaner, more readable code. Remember that nagging feeling of seeing @smartasync duplicated over and over again above every async def method? Well, consider that a thing of the past! With Switcher(smartasync=True), you can simply define your async def methods, decorate them with @api, and let Switcher handle the rest. No more visual clutter, no more repetitive boilerplate. Your code will become leaner, easier to scan, and much more focused on the actual business logic. This isn't just an aesthetic improvement; cleaner code is inherently more maintainable, less prone to errors, and much more welcoming for new team members. It allows you to express your intentions more clearly, without the distraction of infrastructural concerns. Imagine your code reviews becoming simpler because you're discussing the what instead of the how of async wrapping. This translates directly into increased developer velocity and reduced friction, which is a huge win for any project. This benefit alone makes the integration worthwhile, elevating the readability and maintainability of async-heavy Switcher implementations to a new level. It's all about making your genropy projects a joy to work on, ensuring that the focus remains on innovation rather than boilerplate management.

# Before (manual decoration on every async method)
@api
@smartasync
async def method_one(self): 
    await asyncio.sleep(0.01)
    return "result_one"

@api
@smartasync
async def method_two(self): 
    await asyncio.sleep(0.02)
    return "result_two"

# After (automatic with Switcher(smartasync=True))
api = Switcher(smartasync=True)

@api
async def method_one(self): 
    await asyncio.sleep(0.01)
    return "result_one"

@api
async def method_two(self): 
    await asyncio.sleep(0.02)
    return "result_two"

Secondly, this integration brings unmatched flexibility and control to your application architecture. You're not stuck with a one-size-fits-all solution. You can define different Switcher instances with different smartasync behaviors. For example, public_api = Switcher(smartasync=True) could expose methods that are always safely callable from sync contexts, perfect for a public-facing Publisher that might be consumed by a wide variety of callers. On the other hand, internal = Switcher(smartasync=False) might be used for highly performance-critical internal services where you explicitly want raw async coroutines for direct await calls, avoiding any smartasync overhead. This intelligent partitioning empowers you to design your application with precision, tailoring the Switcher's behavior to the specific needs and performance characteristics of different parts of your system. This level of adaptability ensures that Switcher remains a powerful and versatile tool for managing method dispatch in the most complex and demanding Python applications. It's about giving you the architectural levers to build systems that are both robust and highly optimized, ensuring that smartswitch remains at the forefront of flexible application design. This granular control over wrapping ensures that you can optimize for both developer convenience and execution performance depending on the specific use case.

# Different strategies for different Switcher instances
public_api = Switcher(smartasync=True)   # Always sync-callable, great for public endpoints or CLIs
internal = Switcher(smartasync=False)     # Returns raw async coroutines, perfect for internal async services where performance is paramount

# You can then mix and match these in your application design.
class MyService:
    @public_api
    async def get_public_data(self):
        return "Public Data"

    @internal
    async def _process_sensitive_data(self):
        return "Processed Data"

# Now, `service.public_api('get_public_data')` is sync-callable.
# But `service.internal('_process_sensitive_data')` returns a coroutine that must be awaited.

Finally, and this is a huge one, you gain on-demand, context-aware method calls. This means the same underlying async method can behave differently based on how you request it from Switcher. Need to call an async method from your CLI, which is typically synchronous? Just fetch it with cli_call = api('method', smartasync=True). This automatically wraps it, making it callable like a regular function. But what if that same method is being called from your FastAPI HTTP endpoint, which already runs in an async event loop? You'd fetch it with http_call = api('method', smartasync=False), receiving the pure coroutine function, allowing FastAPI to await it directly without any additional wrapping overhead. This dual capability is incredibly powerful, eliminating the need to write separate sync and async adapters for the same piece of business logic. It simplifies your codebase, reduces potential bugs from synchronization issues, and makes your methods truly versatile. You define your core logic once, and Switcher adapts it to the calling context. This is true flexibility in action, ensuring that your genropy applications can gracefully handle various execution environments without code duplication or complex workarounds. It's about making your methods smarter, more adaptable, and ultimately, more valuable across your entire application ecosystem, making smartswitch an even more intelligent dispatcher. This capability empowers developers to build highly flexible and efficient applications that adapt dynamically to their execution environment.

Important Considerations: Dependencies and Edge Cases

Alright, squad, before we wrap this up, let's briefly touch on some important considerations and edge cases that we've thoroughly thought through for this smartasync integration. It's all about making sure this feature is robust, reliable, and user-friendly. First up, the elephant in the room: handling dependencies gracefully. The smartasync package is absolutely essential for this feature to work its magic. However, we understand that not every Switcher user will need or want async capabilities. So, we're making smartasync an optional dependency. What does this mean for you? It means you won't be forced to install it if you're only working with synchronous Switcher methods. It keeps your dependency footprint light and clean. But, if you do decide to leverage the power of smartasync=True (either globally or per-call) without smartasync installed, Switcher will respond with a clear and helpful ImportError. This isn't a cryptic traceback; it's a polite nudge, telling you exactly what to do: pip install smartasync. This approach ensures that users who need the feature get clear guidance, while those who don't are unaffected. It's a best practice for library design, promoting flexibility and a great developer experience. We're committed to making genropy tooling as frictionless as possible, and managing optional dependencies in this way is a key part of that commitment. This strategy ensures that your project only includes the dependencies it truly needs, optimizing your environment and avoiding unnecessary bloat.

# In your pyproject.toml or setup.py, smartasync would be an optional dependency:
[project.optional-dependencies]
async = ["smartasync>=0.5.0"] # Users can install with `pip install your-package[async]`

Next, let's address what about sync methods with smartasync=True? This is a common question, and we've got you covered. If you happen to set Switcher(smartasync=True) or explicitly request handler.api('sync_method', smartasync=True) for a method that is not an async def function, smartasync is inherently designed to be a no-op for synchronous functions. This means it simply returns the original function without any wrapping or overhead. There's no harm, no foul, no unexpected behavior. Your synchronous methods will continue to work exactly as they always have, performing efficiently without any unnecessary async machinery being invoked. This robustness ensures that developers don't have to constantly check the async nature of every method before deciding on the smartasync flag; Switcher and smartasync will handle it intelligently. It simplifies your mental model, letting you focus on the logic rather than the low-level details of sync/async transitions. This particular edge case is handled gracefully, ensuring that the feature integrates smoothly without causing new issues or requiring complex workarounds. It's a subtle but important detail that contributes to the overall stability and ease of use of this new Switcher capability, further cementing its value in smartswitch environments.

class MySyncHandler:
    api = Switcher(smartasync=True) # Let's try global smartasync

    @api
    def my_sync_method(self):
        return "This is a sync result."

handler = MySyncHandler()

# Even though smartasync=True is set, the method is sync. smartasync is a no-op.
callable_sync_method = handler.api('my_sync_method', smartasync=True)
result = callable_sync_method(handler) # Works perfectly fine, no async wrapping occurs.
print(f"Result from sync method: {result}")

Finally, we've carefully considered already-wrapped methods. What if you, for some specific reason, already manually decorated an async method with @smartasync before Switcher gets its hands on it? Our proposed implementation will be smart enough to detect this. While smartasync itself is fairly resilient to double-wrapping, our goal is to avoid any redundant operations. The _create_smartasync_wrapper helper method will be designed to check if the underlying callable is already an instance of a smartasync-wrapped function before attempting to apply it again. If it detects existing wrapping, it will simply return the already-wrapped callable. This prevents unnecessary overhead and potential, albeit unlikely, issues from multiple layers of smartasync decorators. It's about ensuring efficiency and preventing unexpected behavior in more complex or legacy codebases. This attention to detail demonstrates our commitment to a robust and fault-tolerant implementation, making sure that smartswitch operates predictably even in mixed scenarios. This careful handling of existing decorations ensures maximum compatibility and prevents performance degradation from redundant wrapping.

Ensuring a Smooth Rollout: Compatibility, Testing, and Documentation

Okay, team, let's talk about how we're going to make sure this awesome new feature lands smoothly and flawlessly. When we introduce powerful new capabilities like smartasync integration, backward compatibility is paramount. And here's the great news: this feature is fully backward compatible! How, you ask? Because the default value for smartasync in the Switcher constructor will be False. This means if you don't explicitly enable it, your existing Switcher code will behave exactly as it does today. No changes required, no broken applications, no nasty surprises. You can opt into this new behavior at your leisure, when you're ready to leverage its benefits. This opt-in approach is crucial for minimizing disruption and ensuring a seamless upgrade path for all genropy users. It allows everyone to benefit from smartswitch improvements without being forced into new patterns. This commitment to backward compatibility is a core principle of our development, ensuring that your current investments in genropy are protected while we continue to innovate. You can confidently update your Switcher version, knowing that your existing codebase will continue to function as expected.

To guarantee the reliability of this feature, we'll implement a robust testing strategy. We're not just throwing this out there and hoping for the best; we're going to put it through its paces with a comprehensive suite of tests. We'll have dedicated tests for: test_smartasync_switcher_level() to confirm that the global auto-wrapping works as expected for async methods. We'll then test test_smartasync_call_level() to ensure that per-call wrapping correctly applies when explicitly requested. test_smartasync_override() will verify that a call-level smartasync setting correctly overrides the Switcher-level default. It’s crucial to confirm test_smartasync_sync_method() to make sure smartasync is a no-op for sync functions, preventing any unintended side effects. An important test will be test_smartasync_missing_dependency(), which will confirm that the ImportError is correctly raised if smartasync isn't installed when needed. And critically, we'll have tests for both test_smartasync_async_context() and test_smartasync_sync_context() to ensure that wrapped methods function correctly in both environments, whether called from an async def function or a regular synchronous one. These tests will cover all possible scenarios, ensuring that the feature is rock-solid and behaves predictably under various conditions. This thorough approach to testing is our promise to you for a stable and dependable smartswitch integration. Rigorous testing is non-negotiable for a feature of this impact, guaranteeing that it meets the highest standards of quality and reliability for genropy users.

Finally, comprehensive documentation is absolutely essential for user adoption and understanding. A powerful feature is only as good as its explanation! We'll be updating key sections of our documentation to clearly outline how to use this new smartasync integration. This includes enhancements to the README.md to highlight async handling, a brand-new guide titled docs/guide/async.md that dives deep into async patterns with Switcher, and updates to docs/guide/plugins.md to explain how smartasync fits into the broader plugin ecosystem. We'll also make sure the API reference for Switcher clearly documents the new smartasync parameter in both __init__ and __call__. Clear, concise, and complete documentation will empower you guys to quickly understand and effectively utilize this feature, unlocking its full potential in your genropy projects. Good documentation is often underestimated, but it's vital for a smooth developer experience. Our goal is to make this feature as accessible as possible, ensuring that all smartswitch users can easily integrate and benefit from it in their applications. This complete documentation strategy will ensure that you have all the information you need to leverage smartasync integration effectively.

Wrapping Up: Our Vision for Async Excellence

So there you have it, folks! This smartasync integration for Switcher is more than just a feature; it's a significant leap forward in making asynchronous Python development within genropy and smartswitch more intuitive, cleaner, and ultimately, more enjoyable. We're talking about taking the friction out of mixed sync/async environments, giving you the power to write elegant code that just works across all your application contexts. Our priority for this feature is Medium-High because we genuinely believe it addresses a crucial pain point for developers building dual sync/async applications (like CLIs and HTTP APIs). The implementation effort is estimated as Medium, requiring careful attention to smartasync integration, handling of async/sync contexts, and thorough testing, but the payoff in terms of developer experience and code quality will be immense. This proposed enhancement is a testament to our commitment to continually improving genropy as a framework that empowers developers to build sophisticated applications with ease and confidence. We're excited about the possibilities this opens up for cleaner, more flexible, and more robust asynchronous applications powered by smartswitch. Let's make async development truly effortless together!