Update vasr.py

vasr.py

@@ -50,8 +50,8 @@ class Vasr(datasets.GeneratorBasedBuilder):
     # data = datasets.load_dataset('vasr', 'test')
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name="TEST", version=VERSION, description="vasr dataset gold test"),
-        datasets.BuilderConfig(name="VALIDATION", version=VERSION, description="vasr dataset gold validation"),
-        datasets.BuilderConfig(name="TRAIN", version=VERSION, description="vasr dataset gold train")
+        # datasets.BuilderConfig(name="VALIDATION", version=VERSION, description="vasr dataset gold validation"),
+        # datasets.BuilderConfig(name="TRAIN", version=VERSION, description="vasr dataset gold train")
     ]
     IMAGE_EXTENSION = "jpg"
 
@@ -92,24 +92,30 @@ class Vasr(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        # downloaded_files = dl_manager.download_and_extract(_URLS)
         data_dir = dl_manager.download_and_extract({
+            "examples_csv": hf_hub_url("datasets/nlphuji/vasr", filename="test_gold.csv"),
             "images_dir": hf_hub_url("datasets/nlphuji/vasr", filename="vasr_images.zip")
         })
 
-        # return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TEST,
-                                    gen_kwargs={**data_dir, **{'filepath': downloaded_files["test"]}}),
-            datasets.SplitGenerator(name=datasets.Split.TRAIN,
-                                    gen_kwargs={**data_dir, **{'filepath': downloaded_files["train"]}}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION,
-                                    gen_kwargs={**data_dir, **{'filepath': downloaded_files["dev"]}}),
-        ]
+        return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        # data_dir = dl_manager.download_and_extract({
+        #     "images_dir": hf_hub_url("datasets/nlphuji/vasr", filename="vasr_images.zip")
+        # })
+        #
+        # # return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]
+        # return [
+        #     datasets.SplitGenerator(name=datasets.Split.TEST,
+        #                             gen_kwargs={**data_dir, **{'filepath': downloaded_files["test"]}}),
+        #     datasets.SplitGenerator(name=datasets.Split.TRAIN,
+        #                             gen_kwargs={**data_dir, **{'filepath': downloaded_files["train"]}}),
+        #     datasets.SplitGenerator(name=datasets.Split.VALIDATION,
+        #                             gen_kwargs={**data_dir, **{'filepath': downloaded_files["dev"]}}),
+        # ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, examples_csv, images_dir):