Improving representative benchmarks for typing ecosystem

Due to a current lack of representative macrobenchmarks, it is very difficult to decide on whether complex accelerators for some parts of typing are worth implementing in the future. Hence, I'm trying to upstream some benchmarks into pyperformance.

IMO, there are three main areas:

1. Performance of static type checkers implemented in Python (e.g. mypy). (Fixed by #102)
2. Performance of programs using types at runtime (e.g. pydantic, attrs, etc.).
3. Runtime overhead of typed code vs fully untyped code.

For case 2, I plan to use one of pydantic's benchmarks here https://github.com/samuelcolvin/pydantic/tree/master/benchmarks, installed without compiled binaries.

Case 3 is very tricky because there are so many ways to use typing. I don't know how often people use certain features, whether they type-hint inside tight loops, etc. So I'm struggling to find a good benchmark. An idea: grabbing one of the existing pyperformance benchmarks, fully type-hinting it, then comparing the performance delta may work.

CC @JelleZijlstra, I would greatly appreciate hearing your opinion on this (especially for case 3). Maybe I can post this on typing-sig too if I need more help.

Afterword:
All 3 cases benefit from general CPython optimizations. But usually only 3. benefits greatly from typing module-only optimizations (with 1. maybe not improving much if at all, depending on implementation).

Start by reviewing the three benchmark areas described in the issue and the existing pydantic benchmarks linked there. Compare those options with the current pyperformance benchmark suite, then clarify which cases and benchmark programs should be included. Done means a representative benchmark scope is agreed and the selected benchmarks are upstreamed.