fixed ruff misbehaving

pyproject.toml

@@ -3,7 +3,7 @@ target-version = "py311"
 line-length = 119
 fix = true
 lint.select = ["E","F","W","B"]
-lint.ignore = ["E501", "F841", "F811"]
+lint.ignore = ["E501", "F841", "F811", "F401"]
 
 [tool.pytest.ini_options]
 testpaths = ["smtb/tests"]

requirements.txt

@@ -1,5 +1,5 @@
 torch
-pytorch-lightning
+lightning
 torchmetrics
 fair-esm
 jsonargparse

smtb/__init__.py

@@ -1,3 +1,3 @@
-# from beartype.claw import beartype_this_package
+from beartype.claw import beartype_this_package
 
-# beartype_this_package()
+beartype_this_package()

smtb/tests/test_data.py

@@ -2,6 +2,7 @@
 import torch
 
 from ..data import DownstreamDataModule, DownstreamDataset
+from .fixtures import mock_data_dir
 
 
 def test_downstream_dataset(mock_data_dir):

smtb/tests/test_finetune.py

@@ -19,6 +19,7 @@
 import pytest
 
 from ..train import train
+from .fixtures import mock_data_dir
 
 
 @pytest.fixture

smtb/tokenization.py

@@ -3,21 +3,21 @@
 
 import transformers
 from tokenizers import Tokenizer
-from tokenizers.models import BPE, Model, Unigram, WordPiece
+from tokenizers.models import BPE, Unigram, WordPiece
 from tokenizers.pre_tokenizers import Whitespace
-from tokenizers.trainers import BpeTrainer, Trainer, UnigramTrainer, WordPieceTrainer
+from tokenizers.trainers import BpeTrainer, UnigramTrainer, WordPieceTrainer
 from transformers import PreTrainedTokenizerFast
 
 TOKENIZATION_TYPES = Literal["bpe", "wordpiece", "unigram", "char"]
 
 
 def _get_tokenizer(
-    model: Model,
-    trainer: Trainer,
+    model,
+    trainer,
     vocab_size: int,
     model_kwargs: dict | None = None,
     trainer_kwargs: dict | None = None,
-) -> Tokenizer:
+):
     if model_kwargs is None:
         model_kwargs = dict(unk_token="[UNK]")
     if trainer_kwargs is None: