import datasets
from datasets import load_dataset, Dataset, Value, Sequence, Features, DatasetInfo, GeneratorBasedBuilder, Image

from pathlib import Path
import os
import pandas as pd

_DESCRIPTION = """\ The PatFig Dataset is a curated collection of over 18,000 patent images from more than 7,
000 European patent applications, spanning the year 2020. It aims to provide a comprehensive resource for research 
and applications in image captioning, abstract reasoning, patent analysis, and automated documentprocessing. The 
overarching goal of this dataset is to advance the research in visually situated language understanding towards more 
hollistic consumption of the visual and textual data.
"""

_URL = "https://huggingface.co/datasets/lcolonn/patfig/resolve/main/"
_URLS = {
    "train_images": "train_images.tar.gz",
    "test_images": "test_images.tar.gz",
    "annotations_train": "annotations_train.csv",
    "annotations_test": "annotations_test.csv",
}


class PatFig(GeneratorBasedBuilder):
    """DatasetBuilder for patfig dataset."""

    def _info(self):
        return DatasetInfo(
            description=_DESCRIPTION,
            features=Features({
                "image": Image(),
                "image_name": Value("string"),
                "pub_number": Value("string"),
                "title": Value("string"),
                "figs_norm": Sequence(feature=Value("string"), length=-1),
                "short_description": Sequence(feature=Value("string"), length=-1),
                "long_description": Sequence(feature=Value("string"), length=-1),
                "short_description_token_count": Value("int64"),
                "long_description_token_count": Value("int64"),
                "draft_class": Value("string"),
                "cpc_class": Value("string"),
                "relevant_terms": [{'element_identifier': Value("string"), "terms": Sequence(feature=Value("string"), length=-1)}],
                "associated_claims": Value("string"),
                "compound": Value("bool"),
                "references": Sequence(feature=Value(dtype='string'), length=-1),
            }),
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        # FIXME: Currently downloads all the files regardless of the split
        urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
        downloaded_files = dl_manager.download_and_extract(urls_to_download)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"images_dir": downloaded_files["train_images"], "annotations_dir": downloaded_files["annotations_train"]}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"images_dir": f'{downloaded_files["test_images"]}/test', "annotations_dir": downloaded_files["annotations_test"]}
            ),
        ]

    def _generate_examples(self, images_dir: str, annotations_dir: str):
        df = pd.read_csv(annotations_dir)

        for idx, row in df.iterrows():
            image_path = os.path.join(images_dir, row["pub_number"], row["image_name"])
            yield idx, {
                "image": image_path,
                **row.to_dict(),
            }