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. So not compressing the data means it's faster and the data is larger, which is what that test cares about.
• 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:

[akx]

FAILED Tests/test_file_apng.py::test_apng_save_split_fdat - PIL.UnidentifiedImageError: cannot identify image file '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_apng_save_split_fdat0/temp.png'

... huh, why would that fail on pypy only? 🤔