Ignore __setattr__ in FBT003

[alexfikl]

Summary

This is a fairly common pattern

object.__setattr__(self, "flag", True)

that's not covered by the current special case for setattr.

Test Plan

Added a little test for this in FBT.py.

[MichaReiser]

Python noob here. Is this a realistic example? Why not use setattr(object, "flag", true) or object.flag = True? If not, can we add a realistic example where the use of __setattr__ over setattr is recommended?

[alexfikl]

In that example, object would be the python base class for all classes, so basically

object.__setattr__(self, "flag", True)

will call the implementation of setattr of the object class, while

self.flag = True
setattr(self, "flag", True)
self.__setattr__(self, "flag", True)

all call the implementation of the self class (here that's Registry). This is sometimes used when you want to bypass any custom user implementation of __setattr__ and just call the default from object.

We can add another class with an overwritten __setattr__ to highlight the difference, but not sure it's worth it (?).

[alexfikl]

For my real-life example, I used this pattern to memoize some properties on a frozen dataclass and called object.__setattr__(self, "cache_dict", {}), which would be impossible with setattr. (maybe debatable how hacky that is?)

[MichaReiser]

Thx, that helped

[github-actions]

PR Check Results

Ecosystem

✅ ecosystem check detected no changes.

Benchmark

Linux

group                                      main                                   pr
-----                                      ----                                   --
linter/all-rules/large/dataset.py          1.00     15.1±0.06ms     2.7 MB/sec    1.01     15.3±0.07ms     2.7 MB/sec
linter/all-rules/numpy/ctypeslib.py        1.00      3.7±0.02ms     4.5 MB/sec    1.00      3.7±0.01ms     4.5 MB/sec
linter/all-rules/numpy/globals.py          1.00    375.3±1.46µs     7.9 MB/sec    1.00    374.9±2.47µs     7.9 MB/sec
linter/all-rules/pydantic/types.py         1.00      6.3±0.01ms     4.0 MB/sec    1.00      6.3±0.02ms     4.0 MB/sec
linter/default-rules/large/dataset.py      1.00      7.5±0.02ms     5.5 MB/sec    1.01      7.5±0.02ms     5.4 MB/sec
linter/default-rules/numpy/ctypeslib.py    1.00   1591.0±3.99µs    10.5 MB/sec    1.01   1600.6±4.78µs    10.4 MB/sec
linter/default-rules/numpy/globals.py      1.00    172.1±0.28µs    17.1 MB/sec    1.01    174.4±0.20µs    16.9 MB/sec
linter/default-rules/pydantic/types.py     1.00      3.4±0.01ms     7.5 MB/sec    1.00      3.4±0.01ms     7.5 MB/sec
parser/large/dataset.py                    1.01      5.7±0.00ms     7.1 MB/sec    1.00      5.7±0.01ms     7.1 MB/sec
parser/numpy/ctypeslib.py                  1.02   1139.0±1.11µs    14.6 MB/sec    1.00   1121.6±1.63µs    14.8 MB/sec
parser/numpy/globals.py                    1.01    115.8±0.38µs    25.5 MB/sec    1.00    114.6±0.37µs    25.7 MB/sec
parser/pydantic/types.py                   1.01      2.5±0.00ms    10.3 MB/sec    1.00      2.4±0.01ms    10.4 MB/sec

Windows

group                                      main                                   pr
-----                                      ----                                   --
linter/all-rules/large/dataset.py          1.01     17.0±0.20ms     2.4 MB/sec    1.00     16.7±0.17ms     2.4 MB/sec
linter/all-rules/numpy/ctypeslib.py        1.01      4.3±0.06ms     3.9 MB/sec    1.00      4.2±0.06ms     3.9 MB/sec
linter/all-rules/numpy/globals.py          1.00    502.7±7.11µs     5.9 MB/sec    1.01    505.2±6.74µs     5.8 MB/sec
linter/all-rules/pydantic/types.py         1.00      7.1±0.09ms     3.6 MB/sec    1.00      7.1±0.08ms     3.6 MB/sec
linter/default-rules/large/dataset.py      1.00      8.2±0.07ms     4.9 MB/sec    1.01      8.3±0.09ms     4.9 MB/sec
linter/default-rules/numpy/ctypeslib.py    1.00  1762.8±16.92µs     9.4 MB/sec    1.02  1789.5±21.65µs     9.3 MB/sec
linter/default-rules/numpy/globals.py      1.00    203.8±4.60µs    14.5 MB/sec    1.01    205.2±6.04µs    14.4 MB/sec
linter/default-rules/pydantic/types.py     1.00      3.7±0.04ms     6.8 MB/sec    1.01      3.8±0.05ms     6.7 MB/sec
parser/large/dataset.py                    1.00      6.4±0.05ms     6.3 MB/sec    1.01      6.5±0.08ms     6.3 MB/sec
parser/numpy/ctypeslib.py                  1.00  1215.2±18.33µs    13.7 MB/sec    1.01  1229.0±13.60µs    13.5 MB/sec
parser/numpy/globals.py                    1.01    126.4±2.75µs    23.3 MB/sec    1.00    125.5±2.40µs    23.5 MB/sec
parser/pydantic/types.py                   1.00      2.7±0.03ms     9.3 MB/sec    1.00      2.8±0.03ms     9.2 MB/sec