[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0  # Use the ref you want to point at
+    rev: v6.0.0  # Use the ref you want to point at
     hooks:
     - id: trailing-whitespace
     - id: check-ast
@@ -17,7 +17,7 @@ repos:
     - id: check-toml
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: 'v0.9.10'
+    rev: 'v0.12.11'
     hooks:
     - id: ruff
       args: [--fix, --exit-non-zero-on-fix]
@@ -26,7 +26,7 @@ repos:
       types_or: [python, jupyter]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.15.0
+    rev: v1.17.1
     hooks:
     - id: mypy
       entry: python3 -m mypy --config-file pyproject.toml
@@ -35,7 +35,7 @@ repos:
       exclude: "tests"
 
   - repo: https://github.com/crate-ci/typos
-    rev: v1
+    rev: v1.35.6
     hooks:
       - id: typos
         args: [--force-exclude]

atomgen/models/configuration_atomformer.py

@@ -30,7 +30,7 @@ def __init__(
         cls_token_id: int = 122,
         **kwargs: Any,
     ) -> None:
-        super().__init__(**kwargs)  # type: ignore[no-untyped-call]
+        super().__init__(**kwargs)
         self.vocab_size = vocab_size
         self.dim = dim
         self.num_heads = num_heads

atomgen/models/modeling_atomformer.py

@@ -2547,10 +2547,10 @@ def forward(
         return input_embeds, pos_embeds
 
 
-class AtomformerPreTrainedModel(PreTrainedModel):
+class AtomformerPreTrainedModel(PreTrainedModel):  # type: ignore[no-untyped-call]
     """Base class for all transformer models."""
 
-    config_class = AtomformerConfig  # type: ignore[assignment]
+    config_class = AtomformerConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
     _no_split_modules = ["ParallelBlock"]
@@ -2562,7 +2562,7 @@ def _set_gradient_checkpointing(  # type: ignore[override]
             module.gradient_checkpointing = value
 
 
-class AtomformerModel(AtomformerPreTrainedModel):
+class AtomformerModel(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer model for atom modeling."""
 
     def __init__(self, config: AtomformerConfig):
@@ -2581,7 +2581,7 @@ def forward(
         return output
 
 
-class AtomformerForMaskedAM(AtomformerPreTrainedModel):
+class AtomformerForMaskedAM(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer with an atom modeling head on top for masked atom modeling."""
 
     def __init__(self, config: AtomformerConfig):
@@ -2611,7 +2611,7 @@ def forward(
         return loss, logits
 
 
-class AtomformerForCoordinateAM(AtomformerPreTrainedModel):
+class AtomformerForCoordinateAM(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer with an atom coordinate head on top for coordinate denoising."""
 
     def __init__(self, config: AtomformerConfig):
@@ -2641,7 +2641,7 @@ def forward(
         return loss, coords_pred
 
 
-class InitialStructure2RelaxedStructure(AtomformerPreTrainedModel):
+class InitialStructure2RelaxedStructure(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer with an coordinate head on top for relaxed structure prediction."""
 
     def __init__(self, config: AtomformerConfig):
@@ -2674,7 +2674,7 @@ def forward(
         return loss, coords_pred
 
 
-class InitialStructure2RelaxedEnergy(AtomformerPreTrainedModel):
+class InitialStructure2RelaxedEnergy(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer with an energy head on top for relaxed energy prediction."""
 
     def __init__(self, config: AtomformerConfig):
@@ -2704,7 +2704,7 @@ def forward(
         return loss, energy
 
 
-class InitialStructure2RelaxedStructureAndEnergy(AtomformerPreTrainedModel):
+class InitialStructure2RelaxedStructureAndEnergy(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer with an coordinate and energy head."""
 
     def __init__(self, config: AtomformerConfig):
@@ -2757,7 +2757,7 @@ def forward(
         return loss, (formation_energy_pred, coords_pred)
 
 
-class Structure2Energy(AtomformerPreTrainedModel):
+class Structure2Energy(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer with an atom modeling head on top for masked atom modeling."""
 
     def __init__(self, config: AtomformerConfig):
@@ -2799,7 +2799,7 @@ def forward(
         )
 
 
-class Structure2Forces(AtomformerPreTrainedModel):
+class Structure2Forces(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer with a forces head on top for forces prediction."""
 
     def __init__(self, config: AtomformerConfig):
@@ -2841,7 +2841,7 @@ def forward(
         )
 
 
-class Structure2EnergyAndForces(AtomformerPreTrainedModel):
+class Structure2EnergyAndForces(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer with an energy and forces head for energy and forces prediction."""
 
     def __init__(self, config: AtomformerConfig):
@@ -2892,7 +2892,7 @@ def forward(
         return loss, (formation_energy_pred, forces_pred, attention_mask)
 
 
-class Structure2TotalEnergyAndForces(AtomformerPreTrainedModel):
+class Structure2TotalEnergyAndForces(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer with an energy and forces head for energy and forces prediction."""
 
     def __init__(self, config: AtomformerConfig):
@@ -2949,7 +2949,7 @@ def forward(
         return loss, (total_energy_pred, forces_pred, attention_mask)
 
 
-class AtomFormerForSystemClassification(AtomformerPreTrainedModel):
+class AtomFormerForSystemClassification(AtomformerPreTrainedModel):  # type: ignore[no-untyped-call]
     """Atomformer with a classification head for system classification."""
 
     def __init__(self, config: AtomformerConfig):

atomgen/models/schnet.py

@@ -105,7 +105,7 @@ def __init__(
         cls_token_id: int = 122,
         **kwargs: Any,
     ):
-        super().__init__(**kwargs)  # type: ignore[no-untyped-call]
+        super().__init__(**kwargs)
         self.vocab_size = vocab_size
         self.hidden_channels = hidden_channels
         self.num_filters = num_filters
@@ -126,20 +126,20 @@ def __init__(
         self.cls_token_id = cls_token_id
 
 
-class SchNetPreTrainedModel(PreTrainedModel):
+class SchNetPreTrainedModel(PreTrainedModel):  # type: ignore[no-untyped-call]
     """
     A base class for all SchNet models.
 
     An abstract class to handle weights initialization and a
     simple interface for loading and exporting models.
     """
 
-    config_class = SchNetConfig  # type: ignore[assignment]
+    config_class = SchNetConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = False
 
 
-class SchNetModel(SchNetPreTrainedModel):
+class SchNetModel(SchNetPreTrainedModel):  # type: ignore[no-untyped-call]
     """
     SchNet model for energy prediction.
 

atomgen/models/tokengt.py

@@ -2351,7 +2351,7 @@ def __init__(
         gradient_checkpointing: bool = False,
         **kwargs: Any,
     ):
-        super().__init__(**kwargs)  # type: ignore[no-untyped-call]
+        super().__init__(**kwargs)
         self.vocab_size = vocab_size
         self.dim = dim
         self.num_heads = num_heads
@@ -2507,15 +2507,15 @@ def custom_forward(*inputs: Any) -> Any:
         return input_embeds
 
 
-class TransformerPreTrainedModel(PreTrainedModel):
+class TransformerPreTrainedModel(PreTrainedModel):  # type: ignore[no-untyped-call]
     """Base class for all transformer models."""
 
     config_class = TransformerConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
     _no_split_modules = ["ParallelBlock"]
 
-    def _set_gradient_checkpointing(
+    def _set_gradient_checkpointing(  # type: ignore[override]
         self, module: nn.Module, value: bool = False
     ) -> None:
         if isinstance(module, (TransformerEncoder)):

scripts/training/pretrain_s2ef.py

@@ -162,7 +162,7 @@ def train(args: argparse.Namespace) -> None:
         config.gradient_checkpointing = (
             args.gradient_checkpointing if args.gradient_checkpointing else False
         )
-        model = Structure2EnergyAndForces(config)  # type: ignore[arg-type]
+        model = Structure2EnergyAndForces(config)
 
     tokenizer = AtomTokenizer(vocab_file=args.tokenizer_json)
     data_collator = DataCollatorForAtomModeling(
@@ -173,7 +173,7 @@ def train(args: argparse.Namespace) -> None:
         return_edge_indices=False,
     )
 
-    local_rank = int(os.environ.get("LOCAL_RANK", 0))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
     if local_rank == 0:
         wandb.login(key=os.environ["WANDB_API_KEY"])
         wandb.init(project=args.project, config=vars(args), name=args.name)
@@ -207,14 +207,14 @@ def train(args: argparse.Namespace) -> None:
         weight_decay=args.weight_decay,
     )
 
-    trainer = Trainer(  # type: ignore[no-untyped-call]
+    trainer = Trainer(
         model=model,
         args=training_args,
         train_dataset=dataset,
         data_collator=data_collator,
     )
 
-    trainer.train(resume_from_checkpoint=args.checkpoint_exists)  # type: ignore[attr-defined]
+    trainer.train(resume_from_checkpoint=args.checkpoint_exists)
 
     model.save_pretrained(args.output_dir)
 

scripts/training/run_atom3d.py

@@ -121,7 +121,7 @@ def run_atom3d(args: argparse.Namespace) -> None:
             else False,
             problem_type=task_config["problem_type"],
         )
-        model = AtomFormerForSystemClassification(config)  # type: ignore[arg-type]
+        model = AtomFormerForSystemClassification(config)
     else:
         config = AtomformerConfig.from_pretrained(
             args.model,
@@ -157,7 +157,7 @@ def run_atom3d(args: argparse.Namespace) -> None:
         return_edge_indices=False,
     )
 
-    local_rank = int(os.environ.get("LOCAL_RANK", 0))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
     if local_rank == 0:
         wandb.login(key=os.environ.get("WANDB_API_KEY"))
         wandb.init(project=args.project, config=vars(args), name=args.name)
@@ -182,7 +182,7 @@ def run_atom3d(args: argparse.Namespace) -> None:
     )
 
     # Initialize trainer
-    trainer = Trainer(  # type: ignore[no-untyped-call]
+    trainer = Trainer(
         model=model,
         args=training_args,
         train_dataset=dataset["train"],
@@ -192,12 +192,12 @@ def run_atom3d(args: argparse.Namespace) -> None:
     )
 
     # Train the model
-    trainer.train()  # type: ignore[attr-defined]
+    trainer.train()
 
-    trainer.evaluate(dataset["test"])  # type: ignore[attr-defined]
+    trainer.evaluate(dataset["test"])
 
     # Save the model
-    trainer.save_model(args.output_dir)  # type: ignore[attr-defined]
+    trainer.save_model(args.output_dir)
 
 
 if __name__ == "__main__":