formatting

cloudops_tsf.py

@@ -269,19 +269,22 @@ class CloudOpsTSF(datasets.ArrowBasedBuilder):
     def _split_generators(self, dl_manager) -> list[datasets.SplitGenerator]:
         generators = []
         if self.config.train_test:
-            downloaded_files = dl_manager.download_and_extract(f"{self.config.name}/train_test.zip")
+            downloaded_files = dl_manager.download_and_extract(
+                f"{self.config.name}/train_test.zip"
+            )
             generators.append(
                 datasets.SplitGenerator(
                     name=TRAIN_TEST if self.config.train_test else PRETRAIN,
-                    gen_kwargs={"filepath": downloaded_files}
+                    gen_kwargs={"filepath": downloaded_files},
                 )
             )
         if self.config.pretrain:
-            downloaded_files = dl_manager.download_and_extract(f"{self.config.name}/pretrain.zip")
+            downloaded_files = dl_manager.download_and_extract(
+                f"{self.config.name}/pretrain.zip"
+            )
             generators.append(
                 datasets.SplitGenerator(
-                    name=PRETRAIN,
-                    gen_kwargs={"filepath": downloaded_files}
+                    name=PRETRAIN, gen_kwargs={"filepath": downloaded_files}
                 )
             )
         return generators
@@ -294,13 +297,21 @@ class CloudOpsTSF(datasets.ArrowBasedBuilder):
             schema = batch.schema
             if self.config.include_metadata:
                 freq = pa.array([self.config.freq] * len(batch))
-                prediction_length = pa.array([self.config.prediction_length] * len(batch))
-                rolling_evaluations = pa.array([self.config.rolling_evaluations] * len(batch))
+                prediction_length = pa.array(
+                    [self.config.prediction_length] * len(batch)
+                )
+                rolling_evaluations = pa.array(
+                    [self.config.rolling_evaluations] * len(batch)
+                )
                 stride = pa.array([self.config.stride] * len(batch))
                 columns += [freq, prediction_length, rolling_evaluations, stride]
-                for pa_field in [pa.field('freq', pa.string()),
-                                 pa.field('prediction_length', pa.int32()),
-                                 pa.field('rolling_evaluations', pa.int32()),
-                                 pa.field('stride', pa.int32())]:
+                for pa_field in [
+                    pa.field("freq", pa.string()),
+                    pa.field("prediction_length", pa.int32()),
+                    pa.field("rolling_evaluations", pa.int32()),
+                    pa.field("stride", pa.int32()),
+                ]:
                     schema = schema.append(pa_field)
-            yield batch[FieldName.ITEM_ID].to_pylist(), pa.Table.from_arrays(columns, schema=schema)
+            yield batch[FieldName.ITEM_ID].to_pylist(), pa.Table.from_arrays(
+                columns, schema=schema
+            )