Update wiki_dpr.py

wiki_dpr.py

@@ -1,6 +1,7 @@
 import os
 
-import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
 
 import datasets
 
@@ -37,7 +38,14 @@ _NQ_VECTORS_URL = "https://dl.fbaipublicfiles.com/dpr/data/wiki_encoded/single/n
 
 _MULTISET_VECTORS_URL = "https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_{i}"
 
-_INDEX_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wiki_dpr"
+_DATA_TO_NUM_SHARDS = {
+    "nq": 157,
+    "multiset": 157,
+    "no_embeddings": 28,
+    "dummy.nq": 1,
+    "dummy.multiset": 1,
+    "dummy.no_embeddings": 1,
+}
 
 
 class WikiDprConfig(datasets.BuilderConfig):
@@ -95,7 +103,7 @@ class WikiDprConfig(datasets.BuilderConfig):
             self.index_file = "dummy." + self.index_file
 
 
-class WikiDpr(datasets.GeneratorBasedBuilder):
+class WikiDpr(datasets.ArrowBasedBuilder):
     BUILDER_CONFIG_CLASS = WikiDprConfig
     BUILDER_CONFIGS = [
         WikiDprConfig(
@@ -130,48 +138,24 @@ class WikiDpr(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        files_to_download = {"data_file": _DATA_URL}
-        downloaded_files = dl_manager.download_and_extract(files_to_download)
-        if self.config.with_embeddings:
-            vectors_url = _NQ_VECTORS_URL if self.config.embeddings_name == "nq" else _MULTISET_VECTORS_URL
-            if self.config.dummy:
-                downloaded_files["vectors_files"] = dl_manager.download([vectors_url.format(i=0)])
-            else:
-                downloaded_files["vectors_files"] = dl_manager.download([vectors_url.format(i=i) for i in range(50)])
+        data_dir = self.config.embeddings_name if self.config.with_embeddings else "no_embeddings"
+        if self.config.dummy:
+            data_dir = "dummy." + data_dir
+        num_shards = _DATA_TO_NUM_SHARDS[data_dir]
+        data_dir = os.path.join("data", self.config.wiki_split, data_dir)
+        files = [os.path.join(data_dir, f"train-{i:05d}-of-{num_shards:05d}.parquet") for i in range(num_shards)]
+        downloaded_files = dl_manager.download_and_extract(files)
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=downloaded_files),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": downloaded_files}),
         ]
 
-    def _generate_examples(self, data_file, vectors_files=None):
-        vec_idx = 0
-        vecs = []
-        lines = open(data_file, "r", encoding="utf-8")
-        next(lines)  # skip headers
-        for i, line in enumerate(lines):
-            if self.config.dummy and i == 10000:
-                break
-            if i == 21015300:
-                break  # ignore the last 24 examples for which the embeddings are missing.
-            id, text, title = line.strip().split("\t")
-            text = text[1:-1]  # remove " symbol at the beginning and the end
-            text = text.replace('""', '"')  # replace double quotes by simple quotes
-            if self.config.with_embeddings:
-                if vec_idx >= len(vecs):
-                    if len(vectors_files) == 0:
-                        logger.warning(f"Ran out of vector files at index {i}")
-                        break
-                    vecs = np.load(open(vectors_files.pop(0), "rb"), allow_pickle=True)
-                    vec_idx = 0
-                vec_id, vec = vecs[vec_idx]
-                assert int(id) == int(vec_id), f"ID mismatch between lines {id} and vector {vec_id}"
-                yield id, {"id": id, "text": text, "title": title, "embeddings": vec}
-                vec_idx += 1
-            else:
-                yield id, {
-                    "id": id,
-                    "text": text,
-                    "title": title,
-                }
+    def _generate_tables(self, files):
+        for file in files:
+            with open(file, "rb") as f:
+                f = pq.ParquetFile(f)  
+                for batch_idx, batch in enumerate(f.iter_batches(batch_size=1000)):
+                    yield batch_idx, pa.Table.from_batches([batch])
+                    batch_idx += 1
 
     def _post_processing_resources(self, split):
         if self.config.with_index:
@@ -182,10 +166,17 @@ class WikiDpr(datasets.GeneratorBasedBuilder):
     def _download_post_processing_resources(self, split, resource_name, dl_manager):
         if resource_name == "embeddings_index":
             try:
-                downloaded_resources = dl_manager.download_and_extract(
-                    {"embeddings_index": _INDEX_URL + "/" + self.config.index_file.format(split=split)}
-                )
-                return downloaded_resources["embeddings_index"]
+                if not self.config.dummy:
+                    index_file = os.path.join(
+                        "data",
+                        self.config.wiki_split,
+                        self.config.embeddings_name,
+                        os.path.basename(self.config.index_file.format(split=split).split(".", 2)[-1])
+                    )
+                    downloaded_resources = dl_manager.download_and_extract(
+                        {"embeddings_index": index_file}
+                    )
+                    return downloaded_resources["embeddings_index"]
             except (FileNotFoundError, ConnectionError):  # index doesn't exist
                 pass