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chore(lint): update Ruff ignores for project conventions and maintainability (#1184)

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- Added new ignores from flake8-bugbear (`B007`, `B008`)
- Ignored `C901` (high function complexity) for now, pending review
- Maintained PyTorch conventions (`N812`, `N817`)
- Allowed `E731` (lambda assignments) for flexibility
- Consolidated existing ignores (`E402`, `E501`, `F405`, `C408`, `N812`)
- Documented rationale for each ignored rule

This keeps our linting aligned with project needs while tracking
potential fixes.

Signed-off-by: Sébastien Han <seb@redhat.com>

Signed-off-by: Sébastien Han <seb@redhat.com>
This commit is contained in:
Sébastien Han 2025-02-28 18:36:49 +01:00 • committed by GitHub
parent 3b57d8ee88
commit 6fa257b475
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33 changed files with 113 additions and 145 deletions
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9
llama_stack/providers/tests/eval/test_eval.py
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@ -39,12 +39,11 @@ class Testeval:
@pytest.mark.asyncio
async def test_eval_evaluate_rows(self, eval_stack, inference_model, judge_model):
eval_impl, benchmarks_impl, datasetio_impl, datasets_impl, models_impl = (
eval_impl, benchmarks_impl, datasetio_impl, datasets_impl = (
eval_stack[Api.eval],
eval_stack[Api.benchmarks],
eval_stack[Api.datasetio],
eval_stack[Api.datasets],
eval_stack[Api.models],
)
await register_dataset(datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval")
@ -92,11 +91,10 @@ class Testeval:
@pytest.mark.asyncio
async def test_eval_run_eval(self, eval_stack, inference_model, judge_model):
eval_impl, benchmarks_impl, datasets_impl, models_impl = (
eval_impl, benchmarks_impl, datasets_impl = (
eval_stack[Api.eval],
eval_stack[Api.benchmarks],
eval_stack[Api.datasets],
eval_stack[Api.models],
)
await register_dataset(datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval")
@ -131,11 +129,10 @@ class Testeval:
@pytest.mark.asyncio
async def test_eval_run_benchmark_eval(self, eval_stack, inference_model):
eval_impl, benchmarks_impl, datasets_impl, models_impl = (
eval_impl, benchmarks_impl, datasets_impl = (
eval_stack[Api.eval],
eval_stack[Api.benchmarks],
eval_stack[Api.datasets],
eval_stack[Api.models],
)
response = await datasets_impl.list_datasets()