Test speedups

[akx]

On my machine, this speeds up the entire test suite by ~8% on average, when running on all cores. (hyperfine)

~/b/Pillow (main) $ uv pip install -e . && hyperfine 'uv run -m pytest -nlogical --dist=worksteal'
Benchmark 1: uv run -m pytest -nlogical --dist=worksteal
  Time (mean ± σ):     11.237 s ±  0.323 s    [User: 61.079 s, System: 10.519 s]
  Range (min … max):   10.719 s … 11.919 s    10 runs

~/b/Pillow (test-speedups) $ uv pip install -e . && hyperfine 'uv run -m pytest -nlogical --dist=worksteal'
Benchmark 1: uv run -m pytest -nlogical --dist=worksteal
  Time (mean ± σ):     10.419 s ±  0.424 s    [User: 58.852 s, System: 10.216 s]
  Range (min … max):   10.048 s … 11.393 s    10 runs

• test_apng_save_split_fdat was spending time compressing data, but the premise of the test is to see whether large data is split across chunks correctly. The test now uses smaller data and a small MAXCHUNK, and tests the premise.
• Not a speedup, but 29ff5fc had replaced some patching with proper pytest monkeypatching, but left some stray globals in the files. Those get cleaned up.
• test_parser() is split up with parametrization (or in the explicitly failing case of PDFs, into a separate test...), so the different formats run in parallel (Run tests in parallel with pytest-xdist #9945 and all that).
• convert_to_comparable used get_flattened_data to convert a palette image to a list of numbers, just so it could be again interpreted as L bytes. Can just as well go with the bytes themselves (and Improve memory usage and performance for tobytes() #9938 will speed that up too).
• assert_image_similar now uses ImageChops.difference() to compute the difference map between 8bpp images in one multiband sweep. (This got faster in Speed up ImageChops operations #9738.) (ImageChops functions don't do non-8bpp at all; I might take a look at that.) Since many of the tests end up doing assert_image_similar, that's a nice win. (Improve memory usage and performance for tobytes() #9938 will speed up assert_image_equal.) This is probably the source of most of the speedup here. :)

[akx]

The reason I started looking at assert_image_similar was this tracy-via-tracypy fragment:

[radarhere]

I know we have a difference of opinion on this, but I still don't see minor performance improvements in the test suite as a reason to start monkeypatching. I would rather test Pillow as it is with a real image.

[akx]

Pillow is not being monkeypatched. MAXBLOCK is a documented public API, this just sets it temporarily.

This is the same pattern as used in e.g. test_padded_idat in test_file_png.