import os
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.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)

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


        # 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_iterator, supposed_labelset):

        #could also get the label from somewhere else 

        #data/train/categoryB/581261-brown-invoice-10-29-11-04-12-2-13530037073412-pdf.pdf
        #folder/labelset/label/filename

        #full_path = os.path.join(archive_iterator.args[0], file_path)

        archive_path = archive_iterator.args[0]

        extensions = {".pdf", ".PDF"}
        for file_path, file_obj in archive_iterator:
            folder, labelset, label, filename = file_path.split("/")
            if labelset != supposed_labelset:
                continue
           
            if filename.split(".")[-1] in extensions:
                import pdb; pdb.set_trace()  # breakpoint 75999c30 //
                
                images = pdf2image.convert_from_bytes(file_obj.read())

                simple = {"path": file_path, "bytes": file_obj.read()}

                yield file_path, {"file": images, "labels": label}

        #labels = self.info.features["labels"]
        #labels.encode_example(path.parent.name.lower())