unit-test_PDFfolder.py

from pathlib import Path
from typing import List

import datasets
import pdf2image

logger = datasets.logging.get_logger(__name__)

_DESCRIPTION = "A generic pdf folder"

_CLASSES = ["categoryA", "categoryB"] #define in advance

_URL = "https://huggingface.co/datasets/jordyvl/unit-test_PDFfolder/resolve/main/data/data_dir.tar.gz"

#folder
#   train
#       categoryA
#           file1
#   test
#...

class PdfFolder(datasets.GeneratorBasedBuilder):
    def _info(self):

        """
        folder = None
        elif isinstance(self.config.data_files, str):
            folder = self.config.data_files
        elif isinstance(self.config.data_files, dict):
            folder = self.config.data_files.get("train", None)

        if folder is None:
            raise RuntimeError()
        """
        #classes = sorted([x.name.lower() for x in Path(_URL).glob("*/**")])

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "file": datasets.Sequence(datasets.Image()),
                    "labels": datasets.features.ClassLabel(names=_CLASSES),
                }
            ),
            task_templates=None,
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:

        archive_path = dl_manager.download(_URL)

        import pdb; pdb.set_trace()  # breakpoint aef4d417 //


        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "archive_iterator": dl_manager.iter_archive(archive_path),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "archive_iterator": dl_manager.iter_archive(archive_path),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "archive_iterator": dl_manager.iter_archive(archive_path),
                },
            ),
        ]


        # if isinstance(self.config.data_files, str):
        #     return [
        #         datasets.SplitGenerator(
        #             name=datasets.Split.TRAIN, gen_kwargs={"archive_path": self.config.data_files}
        #         )
        #     ]

        # splits = []
        # for split_name, folder in self.config.data_files.items():
        #     splits.append(
        #         datasets.SplitGenerator(name=split_name, gen_kwargs={"archive_path": folder})
        #     )

        # return splits

    def _generate_examples(self, archive_path):
        labels = self.info.features["labels"]
        logger.info("generating examples from = %s", archive_path)
        extensions = {".pdf"}
        for i, path in enumerate(Path(archive_path).glob("**/*")):
            if path.suffix in extensions:

                images = pdf2image.convert_from_bytes(path.posix()) #alternatively https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/main_classes#datasets.Dataset.set_transform

                # convert PDF to list of images
                yield i, {"file": images, "labels": labels.encode_example(path.parent.name.lower())}