Apply isort and black reformatting

Signed-off-by: xuanzic <[email protected]>
xuanzic · Aug 2, 2024 · 1ca19ac · 1ca19ac
1 parent 002d8f9
commit 1ca19ac
Showing 1 changed file with 9 additions and 5 deletions.
diff --git a/nemo/collections/multimodal/models/multimodal_llm/neva/neva_model.py b/nemo/collections/multimodal/models/multimodal_llm/neva/neva_model.py
@@ -1327,19 +1327,23 @@ def build_train_valid_test_datasets_blend(self):
 
     def build_train_valid_test_datasets(self):
         logging.info('Building Neva datasets.')
-        
+
         if isinstance(self.cfg.data.data_path, (list, ListConfig)):
             if len(self.cfg.data.data_path) > 1:
                 # Only consider data blending if there are multiple dataset paths
                 if self.cfg.data.get('concat_sampling_probabilities') is None:
                     logging.warning("No sampling probabilities provided. Defaulting to uniform sampling.")
-                    self.cfg.data.concat_sampling_probabilities = [1 / len(self.cfg.data.data_path)] * len(self.cfg.data.data_path)
+                    self.cfg.data.concat_sampling_probabilities = [1 / len(self.cfg.data.data_path)] * len(
+                        self.cfg.data.data_path
+                    )
                 elif sum(self.cfg.data.concat_sampling_probabilities) != 1:
                     raise ValueError("Concat_sampling_probabilities must sum up to 1.")
                 return self.build_train_valid_test_datasets_blend()
-            elif len(self.cfg.data.data_path) == 1: 
+            elif len(self.cfg.data.data_path) == 1:
                 if self.cfg.data.concat_sampling_probabilities is not None:
-                    logging.warning("Using sampling probabilities with a single dataset has no effect. Defaulting to None and not using blend dataset.")
+                    logging.warning(
+                        "Using sampling probabilities with a single dataset has no effect. Defaulting to None and not using blend dataset."
+                    )
                     self.cfg.data.concat_sampling_probabilities = None
                 self.cfg.data.data_path = self.cfg.data.data_path[0]
             else:
@@ -1351,7 +1355,7 @@ def build_train_valid_test_datasets(self):
 
         if self.cfg.data.get("packed_sequence", False):
             assert self.cfg.micro_batch_size == 1, "Micro batch size must be 1 if using packed sequence"
-            
+
             self._train_ds = NevaPackedSeqDatatset(
                 self.cfg.data.data_path, self.cfg.mm_cfg.vision_encoder.get("crop_size")
             )