Move parsing fb2s to the self._generate_examples func. Use self.base_path to find fb2s in the repo

murakami.py

@@ -1,5 +1,6 @@
 """
-Parse all paragraphs from all *.fb2 files in the input directory, create a Huggingface Dataset and push it to the Hub as `vldsavelyev/murakami`.
+Parse all paragraphs from all *.fb2 files in the input directory, create a Huggingface Dataset and push it 
+to the Hub as `vldsavelyev/murakami`.
 """
 
 
@@ -7,14 +8,13 @@ import os
 from pathlib import Path
 from lxml import etree
 import datasets
-from datasets import Dataset
-from huggingface_hub import create_repo
 
 datasets.logging.set_verbosity_info()
 
 
 _DESCRIPTION = """\
-Russian translations of Murakami novels, to fine-tune a generative language model. Source is FB2 files from http://flibusta.is/a/8570.
+Russian translations of Murakami novels, to fine-tune a generative language model. Source is FB2 files 
+from http://flibusta.is/a/8570.
 """
 
 
@@ -23,20 +23,7 @@ class Builder(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("1.1.0")
 
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-
     def _info(self):
-        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
@@ -44,9 +31,95 @@ class Builder(datasets.GeneratorBasedBuilder):
             features=datasets.Features({"text": datasets.Value("string")}),
         )
 
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        data_dir = Path(self.base_path) / "data"
+        fb2_paths = list(data_dir.glob("*.fb2"))
+        if len(fb2_paths) > 0:
+            print(f"Found {len(fb2_paths)} fb2 files in {data_dir}")
+        else:
+            raise ValueError(f"No fb2 files found in {data_dir}")
+
+        smallest_path = min(fb2_paths, key=os.path.getsize)
+        print(f"Using smallest title as a training example: {smallest_path}")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": [p for p in fb2_paths if p != smallest_path],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": [smallest_path],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepaths):
+        for fileidx, filepath in enumerate(filepaths):
+            print(fileidx, filepath)
+            title, text = self._extract_text_from_fb2(filepath, fileidx)
+            yield title, {"text": text}
+
+    @staticmethod
+    def _extract_text_from_fb2(filepath: Path, fileidx: int) -> tuple[str, str]:
+        """
+        Parse FB2 file and return the concatenation of its paragraphs, along with the title.
+        """
+        # Load the FB2 format file
+        with filepath.open("rb") as file:
+            fb2_data = file.read()
+
+        # Print structure of the FB2 format file
+        # print(etree.tostring(etree.fromstring(fb2_data), pretty_print=True))
+
+        # Parse the FB2 format file using lxml
+        root = etree.fromstring(fb2_data)
+
+        # Get the title of the book
+        title = root.xpath(
+            "//fb:title-info/fb:book-title",
+            namespaces={"fb": "http://www.gribuser.ru/xml/fictionbook/2.0"},
+        )[0].text
+        print(title)
+
+        # Get all book paragraphs
+        paragraphs = root.xpath(
+            "//fb:p",
+            namespaces={"fb": "http://www.gribuser.ru/xml/fictionbook/2.0"},
+        )
+
+        # UNCOMMENT THE LINE BELOW TO BUILD `START_PARAGRAPHS`:
+        # self.helper_to_find_first_paragraphs(paragraphs, title, bi)
+
+        found_paragraphs = []
+        skipping = True
+        for pi, p in enumerate(paragraphs):
+            if p.text is None:
+                continue
+            if (
+                fileidx in Builder.START_PARAGRAPHS
+                and pi >= Builder.START_PARAGRAPHS[fileidx]
+            ):
+                skipping = False
+            if skipping and p.text.lower() == title.lower():
+                skipping = False
+            if not skipping:
+                found_paragraphs.append(p)
+        print(f"Found {len(found_paragraphs)} paragraphs")
+        text = ""
+        for p in found_paragraphs:
+            text += p.text.replace(" ", " ") + "\n"
+        text += "\n"
+        return title, text
+
     # Number of initial <p> element to take from each fb2, by number. This allows to skip
     # intros and other junk in the beginning of an fb2. This is built semi-manually using
-    # the `helper_to_find_first_paragraphs` func.
+    # the `self.helper_to_find_first_paragraphs` function.
     START_PARAGRAPHS = {
         3: 5,
         6: 27,
@@ -88,105 +161,3 @@ class Builder(datasets.GeneratorBasedBuilder):
             print("❌")
             for i, p in enumerate(list(paragraphs)[:30]):
                 print(f"   {i} {p.text}")
-
-    def _split_generators(self, dl_manager):
-        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-        data_dir = "data"
-        text_by_name = {}
-        fb2s = list(Path(data_dir).glob("*.fb2"))
-        if len(fb2s) > 0:
-            print(f"Found {len(fb2s)} fb2 files in {data_dir}")
-        else:
-            raise ValueError(f"No fb2 files found in {data_dir}")
-
-        for bi, path in enumerate(fb2s):
-            print(bi, path)
-
-            # Load the FB2 format file
-            with path.open("rb") as file:
-                fb2_data = file.read()
-
-            # Print structure of the FB2 format file
-            # print(etree.tostring(etree.fromstring(fb2_data), pretty_print=True))
-
-            # Parse the FB2 format file using lxml
-            root = etree.fromstring(fb2_data)
-
-            # Get the title of the book
-            title = root.xpath(
-                "//fb:title-info/fb:book-title",
-                namespaces={"fb": "http://www.gribuser.ru/xml/fictionbook/2.0"},
-            )[0].text
-            print(title)
-
-            # Get all book paragraphs
-            paragraphs = root.xpath(
-                "//fb:p",
-                namespaces={"fb": "http://www.gribuser.ru/xml/fictionbook/2.0"},
-            )
-
-            # UNCOMMENT THE LINE BELOW TO BUILD `START_PARAGRAPHS`:
-            # self.helper_to_find_first_paragraphs(paragraphs, title, bi)
-
-            found_paragraphs = []
-            skipping = True
-            for pi, p in enumerate(paragraphs):
-                if p.text is None:
-                    continue
-                if (
-                    bi in Builder.START_PARAGRAPHS
-                    and pi >= Builder.START_PARAGRAPHS[bi]
-                ):
-                    skipping = False
-                if skipping and p.text.lower() == title.lower():
-                    skipping = False
-                if not skipping:
-                    found_paragraphs.append(p)
-            print(f"Found {len(found_paragraphs)} paragraphs")
-
-            text_by_name[title] = ""
-            for p in found_paragraphs:
-                text_by_name[title] += p.text.replace(" ", " ") + "\n"
-            text_by_name[title] += "\n"
-
-        print("Novel by size:")
-        for title, text in text_by_name.items():
-            print(f"  {title}: {len(text):,} characters")
-
-        smallest_title = min(text_by_name, key=lambda k: len(text_by_name[k]))
-        print(
-            f"Using smallest novel {smallest_title} "
-            f"({len(text_by_name[smallest_title]):,} characters) as a test set"
-        )
-
-        test_titles = [smallest_title]
-        train_titles = [t for t in text_by_name if t not in test_titles]
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "titles": train_titles,
-                    "texts": [text_by_name[t] for t in train_titles],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "titles": test_titles,
-                    "texts": [text_by_name[t] for t in test_titles],
-                    "split": "test",
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, titles, texts, split):
-        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        for title, text in zip(titles, texts):
-            yield title, {"text": text}