fix: satisfy linter by using optional types

src/gt4sd/algorithms/conditional_generation/regression_transformer/implementation.py

@@ -1013,7 +1013,7 @@ def validate_output(self, sequences: List[Any]) -> Tuple[List[Any], List[int]]:
             if isinstance(self.tokenizer.text_tokenizer, PolymerGraphTokenizer):
                 # Copolymer models require specific validation
                 return validate_molecules(
-                    pattern_list=list(zip(*sequences))[0],
+                    pattern_list=list(zip(*sequences))[0], # type: ignore
                     input_type=MoleculeFormat.copolymer,
                 )
 

src/gt4sd/algorithms/controlled_sampling/advanced_manufacturing/implementation/core.py

@@ -154,7 +154,7 @@ def __init__(
         targets: Dict[str, float],
         property_predictors: Dict[str, PropertyPredictor],
         representations: RepresentationsDict,
-        representation_order: List[str] = None,
+        representation_order: List[str] | None = None,
         scalers: Optional[Dict[str, Scaler]] = None,
         weights: Optional[Dict[str, float]] = None,
         custom_score_function: Optional[

src/gt4sd/algorithms/generation/diffusion/geodiff/model/core.py

@@ -127,13 +127,13 @@ def _forward(
         bond_index: torch.Tensor,
         bond_type: torch.Tensor,
         batch: torch.Tensor,
-        edge_index: torch.Tensor = None,
-        edge_type: torch.Tensor = None,
-        edge_length: int = None,
+        edge_index: torch.Tensor | None = None,
+        edge_type: torch.Tensor | None = None,
+        edge_length: int | None = None,
         return_edges: bool = False,
         extend_order: bool = True,
         extend_radius: bool = True,
-        is_sidechain: bool = None,
+        is_sidechain: bool | None = None,
     ) -> Tuple[Any, ...]:
         """Forward pass for edges features.
 

src/gt4sd/algorithms/generation/diffusion/implementation.py

@@ -63,7 +63,7 @@ def set_seed(seed: int = 42) -> None:
     """
     np.random.seed(seed)
     torch.manual_seed(seed)
-    if torch.cuda.is_available:
+    if torch.cuda.is_available():
         torch.cuda.manual_seed_all(seed)  # type:ignore
 
 

src/gt4sd/algorithms/generation/hugging_face/implementation.py

@@ -79,7 +79,7 @@ def set_seed(seed: int = 42) -> None:
     """
     np.random.seed(seed)
     torch.manual_seed(seed)
-    if torch.cuda.is_available:
+    if torch.cuda.is_available():
         torch.cuda.manual_seed_all(seed)  # type:ignore
 
 

src/gt4sd/frameworks/cgcnn/data.py

@@ -212,7 +212,7 @@ class GaussianDistance:
     Unit: angstrom
     """
 
-    def __init__(self, dmin: float, dmax: float, step: float, var: float = None):
+    def __init__(self, dmin: float, dmax: float, step: float, var: float | None = None):
         """
         Args:
             dmin: float
@@ -331,7 +331,7 @@ def __init__(
         dmin: int = 0,
         step: float = 0.2,
         random_seed: int = 123,
-        atom_initialization: AtomCustomJSONInitializer = None,
+        atom_initialization: AtomCustomJSONInitializer | None = None,
     ):
         """
         Args:

src/gt4sd/frameworks/gflownet/dataloader/dataset.py

@@ -50,7 +50,7 @@ class GFlowNetDataset(Dataset):
     """A dataset for gflownet."""
 
     def __init__(
-        self, h5_file: str = None, target: str = "gap", properties: List[str] = []
+        self, h5_file: str | None = None, target: str = "gap", properties: List[str] = []
     ) -> None:
 
         """Initialize a gflownet dataset.
@@ -239,8 +239,8 @@ def __init__(
         self,
         configuration: Dict[str, Any],
         dataset: GFlowNetDataset,
-        reward_model: nn.Module = None,
-        wrap_model: Callable[[nn.Module], nn.Module] = None,
+        reward_model: nn.Module | None = None,
+        wrap_model: Callable[[nn.Module], nn.Module] | None = None,
     ) -> None:
 
         """Initialize a generic gflownet task.
@@ -282,7 +282,7 @@ def load_task_models(self) -> Dict[str, nn.Module]:
         Returns:
             model: a dictionary with the task models.
         """
-        pass
+        raise NotImplementedError()
 
     def sample_conditional_information(self, n: int) -> Dict[str, Any]:
         """Samples conditional information for a minibatch.
@@ -293,7 +293,7 @@ def sample_conditional_information(self, n: int) -> Dict[str, Any]:
         Returns:
             cond_info: a dictionary with the sampled conditional information.
         """
-        pass
+        raise NotImplementedError()
 
     def cond_info_to_reward(
         self, cond_info: Dict[str, Any], flat_reward: FlatRewards

src/gt4sd/frameworks/gflownet/envs/graph_building_env.py

@@ -131,11 +131,11 @@ class GraphAction:
     def __init__(
         self,
         action: GraphActionType,
-        source: int = None,
-        target: int = None,
+        source: int | None = None,
+        target: int | None = None,
         value: Any = None,
-        attr: str = None,
-        relabel: int = None,
+        attr: str | None = None,
+        relabel: int | None = None,
     ):
         """Initialize a single graph-building action.
 
@@ -287,7 +287,7 @@ def count_backward_transitions(self, g: Graph) -> int:
 
 
 def generate_forward_trajectory(
-    g: Graph, max_nodes: int = None
+    g: Graph, max_nodes: int | None = None
 ) -> List[Tuple[Graph, GraphAction]]:
     """Sample (uniformly) a trajectory that generates g.
 

src/gt4sd/frameworks/gflownet/loss/trajectory_balance.py

@@ -77,7 +77,7 @@ def __init__(
         configuration: Dict[str, Any],
         environment: GraphBuildingEnv,
         context: GraphBuildingEnvContext,
-        max_len: int = None,
+        max_len: int | None = None,
     ):
         """Initialize trajectory balance algorithm.
 

src/gt4sd/frameworks/gflownet/tests/qm9.py

@@ -113,8 +113,8 @@ def __init__(
         self,
         configuration: Dict[str, Any],
         dataset: GFlowNetDataset,
-        reward_model: nn.Module = None,
-        wrap_model: Callable[[nn.Module], nn.Module] = None,
+        reward_model: nn.Module | None = None,
+        wrap_model: Callable[[nn.Module], nn.Module] | None = None,
     ):
         """Initialize QM9 task.
 

src/gt4sd/properties/scores/core.py

@@ -151,7 +151,7 @@ class CombinedScorer:
     def __init__(
         self,
         scorer_list: List[Type[Any]],
-        weights: List[float] = None,
+        weights: List[float] | None = None,
     ) -> None:
         """Scoring function which generates a combined score for a SMILES as per the given scoring functions.