Conditional overloading

Conditional overloading takes a set of functions and calls the first one that is callable. This is one of the ways to resolve ambiguity with overloads, but more importantly it allows an alternative function to be used when the first is not callable.

Stringify

Take a look at this example of defining a stringify function from stackoverflow here.

The user would like to write stringify to call to_string where applicable and fallback on using sstream to convert to a string. Most of the top answers usually involve some amount of metaprogramming using either void_t or is_detected(see n4502):

template<class T>
using to_string_t = decltype(std::to_string(std::declval<T>()));

template<class T>
using has_to_string = std::experimental::is_detected<to_string_t, T>;

template<typename T> 
typename std::enable_if<has_to_string<T>{}, std::string>::type 
stringify(T t)
{
    return std::to_string(t);
}
template<typename T> 
typename std::enable_if<!has_to_string<T>{}, std::string>::type 
stringify(T t)
{
    return static_cast<std::ostringstream&>(std::ostringstream() << t).str();
}

However, with the Fit library it can simply be written like this:

FIT_STATIC_LAMBDA_FUNCTION(stringify) = conditional(
    [](auto x) FIT_RETURNS(std::to_string(x)),
    [](auto x) FIT_RETURNS(static_cast<std::ostringstream&>(std::ostringstream() << x).str())
);

So, using FIT_RETURNS not only deduces the return type for the function, but it also constrains the function on whether the expression is valid or not either. So by writing FIT_RETURNS(std::to_string(x)) then the first function will try to call std::to_string function if possible. If not, then the second function will be called.

The second function still uses FIT_RETURNS, so the function will still be constrained by whether the << stream operator can be used. Although it may seem unnecessary because there is not another function, however, this makes the function composable. So we could use this to define a serialize function that tries to call stringify first, otherwise it looks for the member .serialize():

FIT_STATIC_LAMBDA_FUNCTION(serialize) = conditional(
    [](auto x) FIT_RETURNS(stringify(x)),
    [](auto x) FIT_RETURNS(x.serialize())
);

static_if

In addition, this can be used with the if decorator to create static_if-like constructs. For example, Baptiste Wicht discusses how one could write static_if in C++ here.

He wants to be able to write this:

template<typename T>
void decrement_kindof(T& value){
    static_if(std::is_same<std::string, T>::value){
        value.pop_back();
    } else {
        --value;
    }
}

However, that isn’t possible in C++. With the Fit library one can simply write this:

template<typename T>
void decrement_kindof(T& value)
{
    eval(conditional(
        if_(std::is_same<std::string, T>())([&](auto id){
            id(value).pop_back();
        }),
        [&](auto id){
            --id(value);
        }
    ));
}

The id parameter passed to the lambda is the identity function. As explained in the article, this is used to delay the lookup of types by making it a dependent type(i.e. the type depends on a template parameter), which is necessary to avoid compile errors. The eval function that is called will pass this identity function to the lambdas.

The advantage of using the Fit library instead of the solution in Baptiste Wicht’s blog, is that conditional allows more than just two conditions. So if there was another trait to be checked, such as is_stack, it could be written like this:

template<typename T>
void decrement_kindof(T& value)
{
    eval(conditional(
        if_(is_stack<T>())([&](auto id){
            id(value).pop();
        }),
        if_(std::is_same<std::string, T>())([&](auto id){
            id(value).pop_back();
        }),
        [&](auto id){
            --id(value);
        }
    ));
}

Type traits

Furthermore, this technique can be used to write type traits as well. Jens Weller was looking for a way to define a general purpose detection for pointer operands(such as * and ->). One way to accomplish this is using Fit like this:

// Check that T has member function for operator* and ope
template<class T>
auto has_pointer_member(const T&) -> decltype(
    &T::operator*,
    &T::operator->,
    std::true_type{}
);

FIT_STATIC_LAMBDA_FUNCTION(has_pointer_operators) = conditional(
    FIT_LIFT(has_pointer_member),
    [](auto* x) -> bool_constant<(!std::is_void<decltype(*x)>())> { return {}; },
    always(std::false_type{})
);

template<class T>
struct is_dereferenceable
: decltype(has_pointer_operators(std::declval<T>()))
{};

Which is much simpler than the other implementations that were written, which were about 3 times the amount of code(see here).

The has_pointer_operators function works by first trying to call has_pointer_member which returns true_type if the type has member functions operator* and operator->, otherwise the function is not callable. The next function is only callable on pointers, which returns true if it is not a void* pointer(because void* pointers are not dereferenceable). Finally, if none of those functions matched then the last function will always return false_type.