Update openwebtext_split.py

openwebtext_split.py

@@ -37,6 +37,34 @@ An open-source replication of the WebText dataset from OpenAI.
 
 _URL = "https://zenodo.org/record/3834942/files/openwebtext.tar.xz"
 
+def custom_iter_archive(path_or_buf, _filter=lambda x: True):
+    def _iter_archive(f):
+        stream = tarfile.open(fileobj=f, mode="r|*")
+        for i, tarinfo in enumerate(stream):
+            if not _filter(i):
+                continue
+            file_path = tarinfo.name
+            if not tarinfo.isreg():
+                continue
+            if file_path is None:
+                continue
+            if os.path.basename(file_path).startswith(".") or os.path.basename(file_path).startswith("__"):
+                # skipping hidden files
+                continue
+            if not file_path.endswith('xz'):
+                continue
+            file_obj = stream.extractfile(tarinfo)
+            print(file_obj)
+            for txt_file in file_obj:
+                if txt_file:
+                    yield file_path, file_obj
+            stream.members = []
+        del stream
+    if hasattr(path_or_buf, "read"):
+        yield from _iter_archive(path_or_buf)
+    else:
+        with open(path_or_buf, "rb") as f:
+            yield from _iter_archive(f)
 
 class Openwebtext(datasets.GeneratorBasedBuilder):
     """The Open WebText dataset."""
@@ -58,29 +86,15 @@ class Openwebtext(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_URL)
-        owt_dir = os.path.join(dl_dir, "openwebtext")
-        subset_xzs = [
-            os.path.join(owt_dir, file_name)
-            for file_name in sorted(os.listdir(owt_dir))
-            if file_name.endswith("xz")  # filter out ...xz.lock
-        ]
-        ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count() * 0.75))
-        nested_txt_files = [
-            [
-                os.path.join(ex_dir, txt_file_name)
-                for txt_file_name in sorted(os.listdir(ex_dir))
-                if txt_file_name.endswith("txt")
-            ]
-            for ex_dir in ex_dirs
-        ]
-        txt_files = chain(*nested_txt_files)
-        train_end_idx = int(0.9 * len(txt_files))
-        val_end_idx = train_end_idx + int(0.05 * len(txt_files))
+        archive = dl_manager.download(_URL)
+        
+        train_filter = lambda x : (x%10) < 8
+        val_filter = lambda x: (x%10) == 8
+        test_filter = lambda x: (x%10) == 9
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"txt_files": txt_files[:train_end_idx], "split": "train"}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"txt_files": txt_files[train_end_idx:val_end_idx], "split": "validation"}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"txt_files": txt_files[val_end_idx:], "split": "test"})
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": custom_iter_archive(archive, train_filter)}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"files": custom_iter_archive(archive, val_filter)}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"files": custom_iter_archive(archive, test_filter)}),
         ]
 
     def _generate_examples(self, txt_files):