fix: remove doubled words in error/warning messages

nemo/collections/asr/modules/conformer_encoder.py

@@ -1301,7 +1301,7 @@ def change_attention_model(
                     )
                 else:
                     raise ValueError(
-                        f"'{self_attention_model}' is not not a valid value for 'self_attention_model', "
+                        f"'{self_attention_model}' is not a valid value for 'self_attention_model', "
                         f"valid values can be from ['rel_pos', 'rel_pos_local_attn', 'abs_pos', 'rope']"
                     )
                 if device is not None:

nemo/core/optim/lr_scheduler.py

@@ -925,7 +925,7 @@ def prepare_lr_scheduler(
         # Raise exception if neither `max_steps` nor `t_max_epochs` is provided
         if scheduler_config.get('t_max_epochs', None) is None:
             logging.warning(
-                "`t_max_epochs` cannot be None when `max_steps` is not not provided.\n"
+                "`t_max_epochs` cannot be None when `max_steps` is not provided.\n"
                 "This can occur when `train dataloader` is not available to correctly "
                 "prepare the scheduler.\n"
                 "Scheduler will not be instantiated !"

tests/core/test_optimizers_schedulers.py

@@ -292,6 +292,21 @@ def test_sched_config_parse_simple(self):
         scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
         assert isinstance(scheduler_setup['scheduler'], optim.lr_scheduler.CosineAnnealing)
 
+    @pytest.mark.unit
+    def test_sched_config_warns_when_t_max_epochs_is_none(self, caplog):
+        model = TempModel()
+        opt_cls = optim.get_optimizer('novograd')
+        opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+        train_dataloader = torch.utils.data.DataLoader(RandomDataset(8), batch_size=2)
+        sched_config = {'name': 'CosineAnnealing', 't_max_epochs': None}
+
+        with caplog.at_level(logging.WARNING):
+            scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, sched_config, train_dataloader)
+
+        assert scheduler_setup is None
+        assert "`t_max_epochs` cannot be None when `max_steps` is not provided" in caplog.text
+
     @pytest.mark.unit
     def test_sched_config_parse_from_cls(self):
         model = TempModel()