Apply ruff flake8-comprehensions (#21694)
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@@ -155,21 +155,21 @@ class TestTrainerExt(TestCasePlus):
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@require_torch_multi_gpu
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def test_trainer_log_level_replica(self, experiment_id):
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# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
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experiments = dict(
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experiments = {
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# test with the default log_level - should be info and thus log info once
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base=dict(extra_args_str="", n_matches=1),
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"base": {"extra_args_str": "", "n_matches": 1},
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# test with low log_level and log_level_replica - should be noisy on all processes
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# now the info string should appear twice on 2 processes
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low=dict(extra_args_str="--log_level debug --log_level_replica debug", n_matches=2),
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"low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
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# test with high log_level and low log_level_replica
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# now the info string should appear once only on the replica
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high=dict(extra_args_str="--log_level error --log_level_replica debug", n_matches=1),
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"high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
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# test with high log_level and log_level_replica - should be quiet on all processes
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mixed=dict(extra_args_str="--log_level error --log_level_replica error", n_matches=0),
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)
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"mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
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}
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data = experiments[experiment_id]
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kwargs = dict(distributed=True, predict_with_generate=False, do_eval=False, do_predict=False)
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kwargs = {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
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log_info_string = "Running training"
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with CaptureStderr() as cl:
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self.run_seq2seq_quick(**kwargs, extra_args_str=data["extra_args_str"])
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