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Comment by wrmsr | Hacker News Reader

type annotations are actual objects instantiated at runtime, usually only done at module import for non-local annotations but done each function invocation for locals. this is nice for extensibility as it makes interacting with and extending the type system as easy as writing any other normal python but in older versions of python it was extraordinarily slow, particularly with generics (as they were previously implemented by creating new actual typeobjects each time they had arguments applied (to be usable as baseclasses) - this has been corrected in 3.7 with the addition of __mro_entries__). 3.8 is however making annotations evaluated on demand not eagerly pretty much removing all runtime performance impact.

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Thank you, I've been eyeing typing in Python and appreciate your explanation.

In what sense does

    x: float = 1.0

instantiate?

List[Tuple[Tuple[str, int], Dict[str, str]]] creates a bunch of objects; if you're having a closure these are necessarily created each time the closure is invoked, by evaluating the entire "List[Tuple[Tuple[str, int], Dict[str, str]]]" expression.

Why can’t you close over that type instantied outside the closure?

Complicated closures are often more idiomatically as classes where the aggregate type would be a class member.

There are two cases to consider. One is where the closure is called multiple times. This is the "by necessity" case, because the enclosed definition is executable and Python must make believe that this definition is re-executed every time the function is called.

If you are just calling the enclosed function then of course this does not happen. Compare:

    def mytype():
        print("!")
        return dict()

    def closure():
        def enclosed(x: mytype()):
            pass
        return enclosed

    print("Calling the closure")
    for i in range(10):
        closure()
        
    print("Calling the enclosed")
    f = closure()
    for i in range(10):
        f(0)

Note that using closures is almost always faster in Python than classes, because accesses to enclosed variables are done through offsets instead of attribute lookup (this gap has been somewhat closed, but is still significant in some cases; arguably almost all of these cases indicate that you are using either the wrong language, or the wrong implementation). So adding overhead to closures can be a somewhat valid reason to avoid using these annotations.

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