try to fix typing

Author: voorhs

autointent/_callbacks/tensorboard.py

@@ -16,7 +16,7 @@ def __init__(self) -> None:
         Raises an ImportError if neither are installed.
         """
         try:
-            from torch.utils.tensorboard import SummaryWriter  # type: ignore[attr-defined]
+            from torch.utils.tensorboard import SummaryWriter
 
             self.writer = SummaryWriter
         except ImportError:

autointent/_dump_tools/unit_dumpers.py

@@ -228,12 +228,12 @@ def load(path: Path, **kwargs: Any) -> PeftModel:  # noqa: ANN401, ARG004
         if (path / "ptuning").exists():
             # prompt learning model
             ptuning_path = path / "ptuning"
-            model = AutoModelForSequenceClassification.from_pretrained(ptuning_path / "base_model")  # type: ignore[no-untyped-call]
+            model = AutoModelForSequenceClassification.from_pretrained(ptuning_path / "base_model")
             return PeftModel.from_pretrained(model, ptuning_path / "peft")
         if (path / "lora").exists():
             # merged lora model
             lora_path = path / "lora"
-            return AutoModelForSequenceClassification.from_pretrained(lora_path)  # type: ignore[no-untyped-call,no-any-return]
+            return AutoModelForSequenceClassification.from_pretrained(lora_path)
         msg = f"Invalid PeftModel directory structure at {path}. Expected 'ptuning' or 'lora' subdirectory."
         raise ValueError(msg)
 
@@ -252,7 +252,7 @@ def dump(obj: PreTrainedModel, path: Path, exists_ok: bool) -> None:
 
     @staticmethod
     def load(path: Path, **kwargs: Any) -> PreTrainedModel:  # noqa: ANN401, ARG004
-        return AutoModelForSequenceClassification.from_pretrained(path)  # type: ignore[no-untyped-call,no-any-return]
+        return AutoModelForSequenceClassification.from_pretrained(path)
 
     @classmethod
     def check_isinstance(cls, obj: Any) -> bool:  # noqa: ANN401
@@ -269,7 +269,7 @@ def dump(obj: PreTrainedTokenizer | PreTrainedTokenizerFast, path: Path, exists_
 
     @staticmethod
     def load(path: Path, **kwargs: Any) -> PreTrainedTokenizer | PreTrainedTokenizerFast:  # noqa: ANN401, ARG004
-        return AutoTokenizer.from_pretrained(path)  # type: ignore[no-any-return]
+        return AutoTokenizer.from_pretrained(path)  # type: ignore[no-any-return,no-untyped-call]
 
     @classmethod
     def check_isinstance(cls, obj: Any) -> bool:  # noqa: ANN401

autointent/modules/scoring/_bert.py

@@ -128,7 +128,7 @@ def _initialize_model(self) -> Any:  # noqa: ANN401
         label2id = {i: i for i in range(self._n_classes)}
         id2label = {i: i for i in range(self._n_classes)}
 
-        return AutoModelForSequenceClassification.from_pretrained(  # type: ignore[no-untyped-call]
+        return AutoModelForSequenceClassification.from_pretrained(
             self.classification_model_config.model_name,
             trust_remote_code=self.classification_model_config.trust_remote_code,
             num_labels=self._n_classes,
@@ -144,7 +144,7 @@ def fit(
     ) -> None:
         self._validate_task(labels)
 
-        self._tokenizer = AutoTokenizer.from_pretrained(self.classification_model_config.model_name)
+        self._tokenizer = AutoTokenizer.from_pretrained(self.classification_model_config.model_name)  # type: ignore[no-untyped-call]
         self._model = self._initialize_model()
         tokenized_dataset = self._get_tokenized_dataset(utterances, labels)
         self._train(tokenized_dataset)