Update and optimize loading script

search_qa.py

@@ -14,11 +14,10 @@
 # limitations under the License.
 
 # Lint as: python3
-"""SEMPRE dataset."""
-
+"""SearchQA dataset."""
 
+import itertools
 import json
-import os
 
 import datasets
 
@@ -57,8 +56,21 @@ Following this approach, we built SearchQA, which consists of more than 140k que
 """
 
 _DL_URLS = {
-    "raw_jeopardy": "https://drive.google.com/uc?export=download&id=1U7WdBpd9kJ85S7BbBhWUSiy9NnXrKdO6",
-    "train_test_val": "https://drive.google.com/uc?export=download&id=1aHPVfC5TrlnUjehtagVZoDfq4VccgaNT",
+    "raw_jeopardy": [
+        "data/raw_jeopardy/000000-029999.zip",
+        "data/raw_jeopardy/030000-49999.zip",
+        "data/raw_jeopardy/050000-059999.zip",
+        "data/raw_jeopardy/060000-089999.zip",
+        "data/raw_jeopardy/090000-119999.zip",
+        "data/raw_jeopardy/120000-149999.zip",
+        "data/raw_jeopardy/150000-179999.zip",
+        "data/raw_jeopardy/180000-216929.zip",
+    ],
+    "train_test_val": {
+        "train": "data/train_test_val/train.zip",
+        "test": "data/train_test_val/test.zip",
+        "validation": "data/train_test_val/val.zip",
+    },
 }
 # pylint: enable=line-too-long
 
@@ -110,59 +122,22 @@ class SearchQa(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # TODO(jeopardy): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-
+        data_dirs = dl_manager.download_and_extract(_DL_URLS[self.config.name])
         if self.config.name == "raw_jeopardy":
-            filepath = dl_manager.download_and_extract(_DL_URLS["raw_jeopardy"])
-            sub_folders = sorted(os.listdir(os.path.join(filepath, "jeopardy")))
-            all_files = []
-            for zip_folder in sub_folders:
-                if "lock" in zip_folder:
-                    continue
-                zip_folder_path = os.path.join(filepath, "jeopardy", zip_folder)
-                file_path = dl_manager.extract(zip_folder_path)
-                zip_folder = zip_folder.split(".")[0]
-                if os.path.isdir(os.path.join(file_path, zip_folder)):
-                    file_path = os.path.join(file_path, zip_folder)
-
-                else:
-                    # in some cases the subfolder name contains sapces as 050000 - 059999   and 050000-059999
-                    parts = zip_folder.split("-")
-                    zip_folder = parts[0] + " - " + parts[1]
-                    if os.path.isdir(os.path.join(file_path, zip_folder)):
-                        file_path = os.path.join(file_path, zip_folder)
-
-                files = sorted(os.listdir(file_path))
-
-                files_paths = [os.path.join(file_path, file) for file in files if "__MACOSX" not in file]
-                all_files.extend(files_paths)
-
+            filepaths = itertools.chain.from_iterable(dl_manager.iter_files(data_dir) for data_dir in data_dirs)
             return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": all_files}),
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": filepaths}),
             ]
         elif self.config.name == "train_test_val":
-            filepath = dl_manager.download_and_extract(_DL_URLS["train_test_val"])
-            train_path = dl_manager.extract(os.path.join(filepath, "data_json", "train.zip"))
-            test_path = dl_manager.extract(os.path.join(filepath, "data_json", "test.zip"))
-            val_path = dl_manager.extract(os.path.join(filepath, "data_json", "val.zip"))
-
-            train_files = [os.path.join(train_path, file) for file in sorted(os.listdir(train_path))]
-            test_files = [os.path.join(test_path, file) for file in sorted(os.listdir(test_path))]
-            val_files = [os.path.join(val_path, file) for file in sorted(os.listdir(val_path))]
             return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_files}),
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": test_files}),
-                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": val_files}),
+                datasets.SplitGenerator(name=split, gen_kwargs={"filepaths": dl_manager.iter_files(data_dirs[split])})
+                for split in (datasets.Split.TRAIN, datasets.Split.TEST, datasets.Split.VALIDATION)
             ]
 
     def _generate_examples(self, filepaths):
         """Yields examples."""
-        # TODO(searchQa): Yields (key, example) tuples from the dataset
         for i, filepath in enumerate(filepaths):
             with open(filepath, encoding="utf-8") as f:
-
                 data = json.load(f)
                 category = data["category"]
                 air_date = data["air_date"]