style(pre-commit.ci): auto fixes [...]

src/careamics/careamist.py

@@ -52,7 +52,7 @@ class CAREamist:
         by default None.
     callbacks : list of Callback, optional
         List of callbacks to use during training and prediction, by default None.
-        Note: ModelCheckpoint configuration should be set through the Configuration 
+        Note: ModelCheckpoint configuration should be set through the Configuration
         object (config.training_config.model_checkpoint) rather than through callbacks.
 
     Attributes
@@ -221,9 +221,9 @@ def __init__(
     def _define_callbacks(self, callbacks: Optional[list[Callback]] = None) -> None:
         """Define the callbacks for the training loop.
 
-        ModelCheckpoint configuration should be provided through the Configuration 
+        ModelCheckpoint configuration should be provided through the Configuration
         object (config.training_config.model_checkpoint) rather than through callbacks.
-        If no ModelCheckpoint is specified in the configuration, default checkpoint 
+        If no ModelCheckpoint is specified in the configuration, default checkpoint
         settings will be used.
 
         Parameters
@@ -254,12 +254,14 @@ def _define_callbacks(self, callbacks: Optional[list[Callback]] = None) -> None:
         self.callbacks.extend(
             [
                 HyperParametersCallback(self.cfg),
-                self.cfg.training_config.model_checkpoint
-                if self.cfg.training_config.model_checkpoint is not None
-                else ModelCheckpoint(
-                    dirpath=self.work_dir / Path("checkpoints"),
-                    filename=self.cfg.experiment_name,
-                    **self.cfg.training_config.checkpoint_callback.model_dump(),
+                (
+                    self.cfg.training_config.model_checkpoint
+                    if self.cfg.training_config.model_checkpoint is not None
+                    else ModelCheckpoint(
+                        dirpath=self.work_dir / Path("checkpoints"),
+                        filename=self.cfg.experiment_name,
+                        **self.cfg.training_config.checkpoint_callback.model_dump(),
+                    )
                 ),
                 ProgressBarCallback(),
             ]

src/careamics/config/configuration_factories.py

@@ -304,7 +304,7 @@ def _create_configuration(
         num_epochs=num_epochs,
         batch_size=batch_size,
         logger=None if logger == "none" else logger,
-        model_checkpoint=model_checkpoint, 
+        model_checkpoint=model_checkpoint,
     )
 
     # create configuration
@@ -496,7 +496,7 @@ def create_care_configuration(
     dataloader_params : dict, optional
         Parameters for the dataloader, see PyTorch notes, by default None.
     modelcheckpoint : ModelCheckpoint, optional
-        PyTorch Lightning ModelCheckpoint configuration. If not provided, 
+        PyTorch Lightning ModelCheckpoint configuration. If not provided,
         default checkpoint settings will be used.
 
     Returns

src/careamics/config/training_model.py

@@ -6,9 +6,10 @@
 from typing import Literal, Optional, Union
 
 from pydantic import BaseModel, ConfigDict, Field, field_validator
+from pytorch_lightning.callbacks import ModelCheckpoint
 
 from .callback_model import CheckpointModel, EarlyStoppingModel
-from pytorch_lightning.callbacks import ModelCheckpoint
+
 
 class TrainingConfig(BaseModel):
     """
@@ -28,7 +29,7 @@ class TrainingConfig(BaseModel):
     # Pydantic class configuration
     model_config = ConfigDict(
         validate_assignment=True,
-        arbitrary_types_allowed=True, #test - Diya 27.1.25
+        arbitrary_types_allowed=True,  # test - Diya 27.1.25
     )
 
     num_epochs: int = Field(default=20, ge=1)