diff --git a/atomgen/models/configuration_atomformer.py b/atomgen/models/configuration_atomformer.py index 4608610..9ecf480 100644 --- a/atomgen/models/configuration_atomformer.py +++ b/atomgen/models/configuration_atomformer.py @@ -5,7 +5,7 @@ from transformers.configuration_utils import PretrainedConfig -class AtomformerConfig(PretrainedConfig): +class AtomformerConfig(PretrainedConfig): # type: ignore[no-untyped-call] r""" Configuration of a :class:`~transform:class:`~transformers.AtomformerModel`. diff --git a/atomgen/models/modeling_atomformer.py b/atomgen/models/modeling_atomformer.py index 104edc6..c07c02c 100644 --- a/atomgen/models/modeling_atomformer.py +++ b/atomgen/models/modeling_atomformer.py @@ -2550,7 +2550,7 @@ def forward( 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"] @@ -2968,9 +2968,9 @@ def __init__(self, config: AtomformerConfig): if self.problem_type == "regression": self.loss_fct = nn.L1Loss() - elif self.problem_type == "classification": + elif self.problem_type == "classification": # type: ignore[comparison-overlap] self.loss_fct = nn.BCEWithLogitsLoss() - elif self.problem_type == "multiclass_classification": + elif self.problem_type == "multiclass_classification": # type: ignore[comparison-overlap] self.loss_fct = nn.CrossEntropyLoss() def forward( @@ -2989,9 +2989,9 @@ def forward( loss = None if labels is not None: - if self.problem_type == "multiclass_classification": + if self.problem_type == "multiclass_classification": # type: ignore[comparison-overlap] labels = labels.long() - elif self.problem_type == "classification": + elif self.problem_type == "classification": # type: ignore[comparison-overlap] labels = labels.float() loss = self.loss_fct(pred.squeeze(), labels.squeeze()) diff --git a/atomgen/models/schnet.py b/atomgen/models/schnet.py index aaa5164..e524cf0 100644 --- a/atomgen/models/schnet.py +++ b/atomgen/models/schnet.py @@ -10,7 +10,7 @@ from transformers.modeling_utils import PreTrainedModel -class SchNetConfig(PretrainedConfig): +class SchNetConfig(PretrainedConfig): # type: ignore[no-untyped-call] r""" Stores the configuration of a :class:`~transformers.SchNetModel`. @@ -134,7 +134,7 @@ class SchNetPreTrainedModel(PreTrainedModel): # type: ignore[no-untyped-call] simple interface for loading and exporting models. """ - config_class = SchNetConfig # type: ignore[assignment] + config_class = SchNetConfig base_model_prefix = "model" supports_gradient_checkpointing = False diff --git a/atomgen/models/tokengt.py b/atomgen/models/tokengt.py index 3549de1..69c4930 100644 --- a/atomgen/models/tokengt.py +++ b/atomgen/models/tokengt.py @@ -2329,7 +2329,7 @@ def forward( return out -class TransformerConfig(PretrainedConfig): +class TransformerConfig(PretrainedConfig): # type: ignore[no-untyped-call] """Configuration class to store the configuration of a TokenGT model.""" def __init__( @@ -2510,7 +2510,7 @@ def custom_forward(*inputs: Any) -> Any: class TransformerPreTrainedModel(PreTrainedModel): # type: ignore[no-untyped-call] """Base class for all transformer models.""" - config_class = TransformerConfig # type: ignore[assignment] + config_class = TransformerConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["ParallelBlock"] diff --git 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