Remove download_custom

README.md

@@ -31,16 +31,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 11453688524
+    num_bytes: 11453688452
     num_examples: 28602
   - name: validation
-    num_bytes: 17402307
+    num_bytes: 17402295
     num_examples: 50
   - name: test
-    num_bytes: 40482864
+    num_bytes: 40482852
     num_examples: 100
-  download_size: 11740484131
-  dataset_size: 11511573695
+  download_size: 11740397875
+  dataset_size: 11511573599
 ---
 
 # Dataset Card for "pg19"

data/test_files.txt

@@ -0,0 +1,100 @@
+test/10146.txt
+test/10321.txt
+test/10356.txt
+test/10762.txt
+test/12204.txt
+test/15562.txt
+test/22424.txt
+test/24553.txt
+test/2544.txt
+test/25646.txt
+test/25773.txt
+test/25830.txt
+test/26183.txt
+test/26239.txt
+test/26493.txt
+test/26618.txt
+test/27454.txt
+test/28444.txt
+test/28988.txt
+test/29594.txt
+test/29973.txt
+test/30312.txt
+test/30752.txt
+test/30754.txt
+test/30909.txt
+test/30981.txt
+test/31065.txt
+test/3129.txt
+test/31974.txt
+test/3247.txt
+test/32761.txt
+test/3340.txt
+test/33426.txt
+test/33756.txt
+test/34467.txt
+test/35205.txt
+test/35246.txt
+test/3608.txt
+test/36256.txt
+test/37006.txt
+test/37328.txt
+test/37403.txt
+test/37443.txt
+test/3754.txt
+test/37702.txt
+test/38106.txt
+test/3890.txt
+test/38929.txt
+test/38955.txt
+test/4047.txt
+test/40579.txt
+test/40700.txt
+test/4128.txt
+test/41603.txt
+test/41607.txt
+test/42081.txt
+test/42655.txt
+test/43536.txt
+test/43845.txt
+test/44099.txt
+test/44557.txt
+test/45313.txt
+test/45881.txt
+test/45888.txt
+test/46915.txt
+test/47068.txt
+test/47558.txt
+test/47581.txt
+test/47676.txt
+test/48693.txt
+test/49078.txt
+test/49529.txt
+test/49596.txt
+test/50287.txt
+test/51410.txt
+test/53345.txt
+test/5396.txt
+test/54537.txt
+test/54624.txt
+test/55339.txt
+test/55871.txt
+test/56410.txt
+test/5734.txt
+test/5770.txt
+test/57791.txt
+test/58473.txt
+test/58553.txt
+test/58598.txt
+test/5956.txt
+test/5962.txt
+test/6412.txt
+test/6941.txt
+test/7412.txt
+test/7987.txt
+test/8197.txt
+test/8559.txt
+test/860.txt
+test/8788.txt
+test/9315.txt
+test/9931.txt

data/validation_files.txt

@@ -0,0 +1,50 @@
+validation/1022.txt
+validation/11155.txt
+validation/13089.txt
+validation/16959.txt
+validation/1925.txt
+validation/2383.txt
+validation/23956.txt
+validation/24360.txt
+validation/25066.txt
+validation/27688.txt
+validation/28213.txt
+validation/28776.txt
+validation/29981.txt
+validation/32629.txt
+validation/34016.txt
+validation/34056.txt
+validation/34100.txt
+validation/356.txt
+validation/35816.txt
+validation/36402.txt
+validation/37833.txt
+validation/38214.txt
+validation/38403.txt
+validation/4024.txt
+validation/41074.txt
+validation/42067.txt
+validation/42142.txt
+validation/42306.txt
+validation/43423.txt
+validation/44896.txt
+validation/44912.txt
+validation/4533.txt
+validation/48089.txt
+validation/48461.txt
+validation/48677.txt
+validation/49091.txt
+validation/50355.txt
+validation/51859.txt
+validation/5195.txt
+validation/5321.txt
+validation/53682.txt
+validation/54098.txt
+validation/555.txt
+validation/55658.txt
+validation/56719.txt
+validation/57843.txt
+validation/58093.txt
+validation/6404.txt
+validation/7510.txt
+validation/8545.txt

pg19.py

@@ -2,11 +2,7 @@
 
 
 import csv
-import json
 import os
-from operator import itemgetter
-
-import requests
 
 import datasets
 
@@ -38,9 +34,9 @@ To compare models we propose to continue measuring the word-level perplexity, by
 One could use this dataset for benchmarking long-range language models, or use it to pre-train for other natural language processing tasks which require long-range reasoning, such as LAMBADA or NarrativeQA. We would not recommend using this dataset to train a general-purpose language model, e.g. for applications to a production-system dialogue agent, due to the dated linguistic style of old texts and the inherent biases present in historical writing.
 """
 
-_ASSET_ROOT_URL = "https://storage.googleapis.com/deepmind-gutenberg/"
-_STORAGE_API_ROOT_URL = "https://storage.googleapis.com/storage/v1/b/deepmind-gutenberg/o/"
 
+_SPLIT_FILES_PATH = "data/{split}_files.txt"
+_ASSET_ROOT_URL = "https://storage.googleapis.com/deepmind-gutenberg/"
 _METADATA_URL = _ASSET_ROOT_URL + "metadata.csv"
 
 
@@ -80,97 +76,61 @@ class Pg19(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # TODO(pg19): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-
-        def fetch_all_pages(url, prefix):
-            pageToken = None
-            payload = {"prefix": prefix}
-
-            while True:
-                resp = requests.get(url, params={"pageToken": pageToken, **payload})
-                json = resp.json()
-                yield json
-
-                pageToken = json.pop("nextPageToken", None)
-                if pageToken is None:
-                    break
-
-        def get_filename(path):
-            return os.path.splitext(os.path.basename(path))[0]
-
-        def download_listdir(url, local_filepath):
-            root_url, prefix = url.rsplit("/", 1)
-            pages = fetch_all_pages(root_url, prefix + "/")
-            items = flat_map(itemgetter("items"), pages)
-            names = sorted(map(itemgetter("name"), items))
-
-            with open(local_filepath, "w") as f:
-                f.write(json.dumps(names))
-            return local_filepath
-
-        def filepath_to_json(path):
-            with open(path, "r", encoding="utf-8") as f:
-                return json.load(f)
-
         splits = ["train", "validation", "test"]
-        split_paths = map(lambda path: _STORAGE_API_ROOT_URL + path, splits)
-        split_paths = dl_manager.download_custom(dict(zip(splits, split_paths)), download_listdir)
-
-        file_urls = list(map(filepath_to_json, split_paths.values()))
-
-        complete_file_urls = [
-            list(map(lambda url: _ASSET_ROOT_URL + url, urls)) for (split_path, urls) in zip(split_paths, file_urls)
-        ]
-        urls_to_download = {(get_filename(url)): url for urls in complete_file_urls for url in urls}
-
-        metadata = dl_manager.download({"metadata": _METADATA_URL})
-        downloaded_files = dl_manager.download(urls_to_download)
-
-        ids_in_split = list(map(lambda urls: list(map(get_filename, urls)), file_urls))
-        split_ids_index = dict(zip(split_paths, ids_in_split))
+        files = dl_manager.download({split: _SPLIT_FILES_PATH.format(split=split) for split in splits})
+
+        for split, names_file in list(files.items()):
+            with open(names_file, encoding="utf-8") as f:
+                split_files = f.read().splitlines()
+            split_files = sorted(split_files)
+            split_files = {
+                os.path.splitext(os.path.basename(file))[0]: _ASSET_ROOT_URL + file
+                for file in split_files
+            }
+            files[split] = split_files
 
+        metadata = dl_manager.download(_METADATA_URL)
+        downloaded_files = dl_manager.download(files)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "ids": split_ids_index["train"],
-                    "metadata_filepath": metadata["metadata"],
-                    "filepaths": downloaded_files,
+                    "ids": list(downloaded_files["train"]),
+                    "metadata": metadata,
+                    "files": downloaded_files["train"],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "ids": split_ids_index["validation"],
-                    "metadata_filepath": metadata["metadata"],
-                    "filepaths": downloaded_files,
+                    "ids": list(downloaded_files["validation"]),
+                    "metadata": metadata,
+                    "files": downloaded_files["validation"],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "ids": split_ids_index["test"],
-                    "metadata_filepath": metadata["metadata"],
-                    "filepaths": downloaded_files,
+                    "ids": list(downloaded_files["test"]),
+                    "metadata": metadata,
+                    "files": downloaded_files["test"],
                 },
             ),
         ]
 
-    def _generate_examples(self, ids, metadata_filepath, filepaths):
+    def _generate_examples(self, ids, metadata, files):
         """Yields examples."""
         # TODO(pg19): Yields (key, example) tuples from the dataset
 
-        with open(metadata_filepath, encoding="utf-8") as f:
-            metadata_dict = csv.DictReader(f, fieldnames=["_id", "short_book_title", "publication_date", "url"])
-            indexed_metadata = {row["_id"]: row for row in metadata_dict}
+        with open(metadata, encoding="utf-8") as f:
+            reader = csv.DictReader(f, fieldnames=["_id", "short_book_title", "publication_date", "url"])
+            id2metadata = {row["_id"]: row for row in reader}
 
         for _id in ids:
-            data = indexed_metadata[_id]
-            filepath = filepaths[_id]
+            data = id2metadata[_id]
+            file = files[_id]
 
-            with open(filepath, encoding="utf-8") as f:
+            with open(file, encoding="utf-8") as f:
                 text = f.read()
 
             _id = data["_id"]