Format Python code with psf/black push (#325)

* 🎨 Format Python code with psf/black

pina/callbacks/__init__.py

@@ -2,8 +2,8 @@ __all__ = [
     "SwitchOptimizer",
     "R3Refinement",
     "MetricTracker",
-    "PINAProgressBar"
-    ]
+    "PINAProgressBar",
+]
 
 from .optimizer_callbacks import SwitchOptimizer
 from .adaptive_refinment_callbacks import R3Refinement

pina/callbacks/processing_callbacks.py

@@ -6,9 +6,12 @@ import torch
 import copy
 
 from pytorch_lightning.callbacks import Callback, TQDMProgressBar
-from lightning.pytorch.callbacks.progress.progress_bar import get_standard_metrics
+from lightning.pytorch.callbacks.progress.progress_bar import (
+    get_standard_metrics,
+)
 from pina.utils import check_consistency
 
+
 class MetricTracker(Callback):
 
     def __init__(self):
@@ -37,7 +40,7 @@ class MetricTracker(Callback):
 
     def on_train_epoch_end(self, trainer, pl_module):
         """
-        Collect and track metrics at the end of each training epoch. 
+        Collect and track metrics at the end of each training epoch.
 
         :param trainer: The trainer object managing the training process.
         :type trainer: pytorch_lightning.Trainer
@@ -68,7 +71,8 @@ class MetricTracker(Callback):
 class PINAProgressBar(TQDMProgressBar):
 
     BAR_FORMAT = "{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_noinv_fmt}{postfix}]"
-    def __init__(self, metrics='mean', **kwargs):
+
+    def __init__(self, metrics="mean", **kwargs):
         """
         PINA Implementation of a Lightning Callback for enriching the progress
         bar.
@@ -123,7 +127,7 @@ class PINAProgressBar(TQDMProgressBar):
         if pbar_metrics:
             pbar_metrics = {
                 key: pbar_metrics[key] for key in self._sorted_metrics
-                }
+            }
         duplicates = list(standard_metrics.keys() & pbar_metrics.keys())
         if duplicates:
             rank_zero_warn(
@@ -133,7 +137,7 @@ class PINAProgressBar(TQDMProgressBar):
             )
 
         return {**standard_metrics, **pbar_metrics}
-    
+
     def on_fit_start(self, trainer, pl_module):
         """
         Check that the metrics defined in the initialization are available,
@@ -145,9 +149,13 @@ class PINAProgressBar(TQDMProgressBar):
         """
         # Check if all keys in sort_keys are present in the dictionary
         for key in self._sorted_metrics:
-            if key not in trainer.solver.problem.conditions.keys() and key != 'mean':
+            if (
+                key not in trainer.solver.problem.conditions.keys()
+                and key != "mean"
+            ):
                 raise KeyError(f"Key '{key}' is not present in the dictionary")
         # add the loss pedix
         self._sorted_metrics = [
-            metric + '_loss' for metric in self._sorted_metrics]
-        return super().on_fit_start(trainer, pl_module)
+            metric + "_loss" for metric in self._sorted_metrics
+        ]
+        return super().on_fit_start(trainer, pl_module)