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")

    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):
        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 `self.helper_to_find_first_paragraphs` function.
    START_PARAGRAPHS = {
        3: 5,
        6: 27,
        7: 3,
        9: 4,
        10: 3,
        12: 11,
        18: 5,
        20: 3,
        21: 5,
    }

    @staticmethod
    def helper_to_find_first_paragraphs(paragraphs, title, book_number, n=30):
        """
        Helps to eyeball first few paragraphs of a book to skip junk paragraphs
        in the beginning and manually construct the `tart_paragraphs` dict.
        """
        found_paragraphs = []
        skipping = True
        for i, p in enumerate(list(paragraphs)[:n]):
            if p.text is None:
                continue
            if (
                book_number in Builder.START_PARAGRAPHS
                and i >= Builder.START_PARAGRAPHS[book_number]
            ):
                skipping = False
            if skipping and p.text.lower() == title.lower():
                skipping = False
            if not skipping:
                found_paragraphs.append(f"   {i} {p.text}")

        if found_paragraphs:
            print("✅")
            print("\n".join(found_paragraphs))

        else:
            print("❌")
            for i, p in enumerate(list(paragraphs)[:30]):
                print(f"   {i} {p.text}")