Revert change to enable push_to_hub at the subset level

ultravox/tools/ds_tool/ds_tool.py

@@ -7,8 +7,6 @@
 import jinja2
 import openai
 import simple_parsing
-from jinja2 import StrictUndefined
-from jinja2 import TemplateError
 
 from ultravox.data import text_proc
 from ultravox.tools.ds_tool import caching
@@ -51,8 +49,10 @@ def _map_sample(self, sample):
         # using a Jinja template for some added flexibility
         # The {{ var }} syntax is how Jinja denotes variables
         try:
-            text = jinja2.Template("{{" + self.column_name + "}}").render(**sample)
-        except TemplateError as e:
+            text = jinja2.Template(
+                "{{" + self.column_name + "}}", undefined=jinja2.StrictUndefined
+            ).render(**sample)
+        except jinja2.TemplateError as e:
             print(f"Error rendering template: {e}")
             print(f"column_name: {self.column_name}")
             print(f"sample keys: {list(sample.keys())}")
@@ -100,10 +100,10 @@ def _map_sample(self, sample):
         # using a Jinja template for some added flexibility, template can include variables and functions
         # e.g., {{ text }} or {{ text_proc.format_asr_text(text) }}
         try:
-            rendered = jinja2.Template(self.template, undefined=StrictUndefined).render(
-                **sample, json_dump=json.dumps, text_proc=text_proc
-            )
-        except TemplateError as e:
+            rendered = jinja2.Template(
+                self.template, undefined=jinja2.StrictUndefined
+            ).render(**sample, json_dump=json.dumps, text_proc=text_proc)
+        except jinja2.TemplateError as e:
             print(f"Error rendering template: {e}")
             print(f"template: {self.template}")
             print(f"sample keys: {list(sample.keys())}")
@@ -177,49 +177,54 @@ def __post_init__(self):
 def main(args: DatasetToolArgs):
     ds_name = args.dataset_name
     print(f'Loading dataset "{ds_name}" for task {args.task}')
-    ds: datasets.DatasetDict = datasets.load_dataset(
+    data_dict: datasets.DatasetDict = datasets.load_dataset(
         ds_name, args.dataset_subset, split=args.dataset_split
     )
 
-    if isinstance(ds, datasets.Dataset):
-        ds = datasets.DatasetDict({args.upload_split: ds})
+    if isinstance(data_dict, datasets.Dataset):
+        data_dict = datasets.DatasetDict({args.upload_split: data_dict})
 
-    if len(ds) > 1 and args.upload_split:
+    if len(data_dict) > 1 and args.upload_split:
         raise ValueError("Cannot upload multiple splits to a single split")
 
-    hub_args: Dict[str, Any] = {
-        "config_name": args.upload_subset or "default",
-        "token": args.token or os.environ.get("HF_TOKEN"),
-        "revision": args.upload_branch,
-        "private": args.private,
-    }
-    if args.num_shards is not None:
-        hub_args["num_shards"] = args.num_shards
-
-    for split, ds_split in ds.items():
+    for split, ds_split in data_dict.items():
         print(
             f"Processing dataset: {ds_name}, subset {args.dataset_subset}, split {args.dataset_split}, containing {len(ds_split)} samples"
         )
         if args.shuffle:
             ds_split = ds_split.shuffle(seed=args.shuffle_seed)
         if args.num_samples:
             ds_split = ds_split.select(range(args.num_samples))
-        ds_split = args.task.map_split(
+        data_dict[split] = args.task.map_split(
             ds_split, args.num_workers, args.writer_batch_size
         )
 
-        upload_split = args.upload_split or split
+    hub_args: Dict[str, Any] = {
+        "config_name": args.upload_subset or "default",
+        "token": args.token or os.environ.get("HF_TOKEN"),
+        "revision": args.upload_branch,
+        "private": args.private,
+    }
 
-        try:
-            ds_split.push_to_hub(args.upload_name, split=upload_split, **hub_args)
-        except Exception as e:
-            print(f"Failed to push to hub: {e}")
+    if args.num_shards is not None:
+        hub_args["num_shards"] = args.num_shards
 
-            # If the push fails or upload_name is not specified, save the data locally.
-            output_name = f"{args.upload_subset}-{upload_split}-00000-of-00001.parquet"
-            ds_split.to_parquet(output_name)
+    try:
+        if args.dataset_split:
+            data_dict[args.dataset_split].push_to_hub(
+                args.upload_name, split=args.upload_split, **hub_args
+            )
+        else:
+            data_dict.push_to_hub(args.upload_name, **hub_args)
+    except Exception as e:
+        print(f"Failed to push to hub: {e}")
+
+        # If the push fails or upload_name is not specified, save the data locally.
+        for split in data_dict.keys():
+            output_name = f"{split}-00000-of-00001.parquet"
+            data_dict[split].to_parquet(output_name)
             print(f"Saved to {output_name}")
-            print(f"Sample {0} of {args.upload_subset}: {ds[0]}")
+            print(f"Sample {0} of {split}: {data_dict[split][0]}")
 
 
 if __name__ == "__main__":