try to fix typing

Author: voorhs

autointent/_dump_tools/unit_dumpers.py

@@ -215,13 +215,13 @@ def dump(obj: PeftModel, path: Path, exists_ok: bool) -> None:
             ptuning_path = path / "ptuning"
             ptuning_path.mkdir(parents=True, exist_ok=exists_ok)
             obj.save_pretrained(str(ptuning_path / "peft"))
-            obj.base_model.save_pretrained(ptuning_path / "base_model")  # type: ignore[attr-defined]
+            obj.base_model.save_pretrained(ptuning_path / "base_model")
         else:
             # strategy to save lora models: merge adapters and save as usual hugging face model
             lora_path = path / "lora"
             lora_path.mkdir(parents=True, exist_ok=exists_ok)
             merged_model: PreTrainedModel = obj.merge_and_unload()
-            merged_model.save_pretrained(lora_path)  # type: ignore[attr-defined]
+            merged_model.save_pretrained(lora_path)
 
     @staticmethod
     def load(path: Path, **kwargs: Any) -> PeftModel:  # noqa: ANN401, ARG004
@@ -248,7 +248,7 @@ class HFModelDumper(BaseObjectDumper[PreTrainedModel]):
     @staticmethod
     def dump(obj: PreTrainedModel, path: Path, exists_ok: bool) -> None:
         path.mkdir(parents=True, exist_ok=exists_ok)
-        obj.save_pretrained(path)  # type: ignore[attr-defined]
+        obj.save_pretrained(path)
 
     @staticmethod
     def load(path: Path, **kwargs: Any) -> PreTrainedModel:  # noqa: ANN401, ARG004
@@ -265,7 +265,7 @@ class HFTokenizerDumper(BaseObjectDumper[PreTrainedTokenizer | PreTrainedTokeniz
     @staticmethod
     def dump(obj: PreTrainedTokenizer | PreTrainedTokenizerFast, path: Path, exists_ok: bool) -> None:
         path.mkdir(parents=True, exist_ok=exists_ok)
-        obj.save_pretrained(path)  # type: ignore[union-attr]
+        obj.save_pretrained(path)
 
     @staticmethod
     def load(path: Path, **kwargs: Any) -> PreTrainedTokenizer | PreTrainedTokenizerFast:  # noqa: ANN401, ARG004

autointent/modules/scoring/_bert.py

@@ -181,7 +181,7 @@ def _train(self, tokenized_dataset: DatasetDict) -> None:
                 load_best_model_at_end=self.early_stopping_config.metric is not None,
             )
 
-            trainer = Trainer(  # type: ignore[no-untyped-call]
+            trainer = Trainer(
                 model=self._model,
                 args=training_args,
                 train_dataset=tokenized_dataset["train"],
@@ -192,10 +192,10 @@ def _train(self, tokenized_dataset: DatasetDict) -> None:
                 callbacks=self._get_trainer_callbacks(),
             )
             if not self.print_progress:
-                trainer.remove_callback(PrinterCallback)  # type: ignore[attr-defined]
-                trainer.remove_callback(ProgressCallback)  # type: ignore[attr-defined]
+                trainer.remove_callback(PrinterCallback)
+                trainer.remove_callback(ProgressCallback)
 
-            trainer.train()  # type: ignore[attr-defined]
+            trainer.train()
 
     def _get_trainer_callbacks(self) -> list[TrainerCallback]:
         res: list[TrainerCallback] = []

user_guides/advanced/03_automl.py

@@ -33,8 +33,7 @@
 """
 
 # %%
-from autointent.modules import (DECISION_MODULES, EMBEDDING_MODULES,
-                                REGEX_MODULES, SCORING_MODULES)
+from autointent.modules import DECISION_MODULES, EMBEDDING_MODULES, REGEX_MODULES, SCORING_MODULES
 
 print(list(SCORING_MODULES.keys()))
 print(list(DECISION_MODULES.keys()))

user_guides/basic_usage/03_automl.py

@@ -115,8 +115,7 @@
 """
 
 # %%
-from autointent.configs import (CrossEncoderConfig, EmbedderConfig,
-                                TokenizerConfig)
+from autointent.configs import CrossEncoderConfig, EmbedderConfig, TokenizerConfig
 
 # Configure embedding model (used for vector representations)
 custom_pipeline.set_config(EmbedderConfig(model_name="prajjwal1/bert-tiny"))