murakami.py

"""
Parse all paragraphs from all *.fb2 files in the input directory, create a Huggingface Dataset and push it 
to the Hub as `vldsavelyev/murakami`.
"""


import os
from pathlib import Path
from lxml import etree
import datasets

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.
"""


class Builder(datasets.GeneratorBasedBuilder):
    """Murakami novels, translated to Russian."""

    VERSION = datasets.Version("1.1.0")

    # Small chapters are usually the footnotes and the title of the book, skipping by default as it's
    # not helping to capture the style of the author anyway.
    MIN_CHAPTER_SIZE = 500

    def _info(self):
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features({"text": datasets.Value("string")}),
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        fb2_dir = dl_manager.download_and_extract("data.zip")
        fb2_paths = list(Path(fb2_dir).glob("**/*.fb2"))
        if len(fb2_paths) > 0:
            print(f"Found {len(fb2_paths)} fb2 files")
        else:
            raise ValueError(f"No fb2 files found in {fb2_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):
            title, chapters = self._extract_text_from_fb2(filepath, fileidx)
            for i, chapter in enumerate(chapters):
                yield f"{title} {i}", {"text": chapter}

    @staticmethod
    def _extract_text_from_fb2(filepath: Path, fileidx: int) -> tuple[str, list[str]]:
        """
        Parse a FB2 file and return book chapters, along with the book title.
        """
        # Load the FB2 format file
        with filepath.open("rb") as file:
            fb2_data = file.read()

        # 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

        # UNCOMMENT THIS TO BUILD `START_PARAGRAPHS`
        # helper_to_find_first_paragraphs(root, title, bi)
        # continue

        # All text is stored in <p> tags. There are also <section> tags, which do not have any content,
        # but serve as chapters separators. So we will merge all <p> tags contents between two <section>.
        chapters: list[str] = []

        def _add_chapter(text: str):
            if not text:
                return
            if (
                Builder.MIN_CHAPTER_SIZE is not None
                and len(text) < Builder.MIN_CHAPTER_SIZE
            ):
                # print(f"Skipping chapter of length {len(text)}")
                pass
            else:
                # print(f"Adding chapter of length {len(text)}")
                chapters.append(text)

        chapter = ""
        for e in root.iter():
            if e.tag.endswith("}p"):
                chapter += (e.text or "") + (e.tail or "")
            elif e.tag.endswith("}section"):
                _add_chapter(chapter)
                chapter = ""
        _add_chapter(chapter)

        print(f'{filepath}: "{title}", found {len(chapters)} chapters')
        # print(f"Chapter sizes: {', '.join(str(len(c)) for c in chapters)}")
        # print()
        return title, chapters