"""SciCo"""

import os
from datasets.arrow_dataset import DatasetTransformationNotAllowedError
from datasets.utils import metadata
import jsonlines
import datasets 




_CITATION = """\
        @inproceedings{
    cattan2021scico,
    title={SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts},
    author={Arie Cattan and Sophie Johnson and Daniel S. Weld and Ido Dagan and Iz Beltagy and Doug Downey and Tom Hope},
    booktitle={3rd Conference on Automated Knowledge Base Construction},
    year={2021},
    url={https://openreview.net/forum?id=OFLbgUP04nC}
}
"""

_DESCRIPTION = """\
        SciCo is a dataset for hierarchical cross-document coreference resolution
        over scientific papers in the CS domain. 
        """

_DATA_URL = "./data.tar"

class Scico(datasets.GeneratorBasedBuilder):    
    def _info(self):
        return datasets.DatasetInfo(
                description=_DESCRIPTION,
                homepage="https://scico.apps.allenai.org/",
                features=datasets.Features(
                    {
                        "flatten_tokens": datasets.features.Sequence(datasets.features.Value("string")),
                        "flatten_mentions": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Value("int32"), length=3)),
                        "tokens": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Value("string"))),
                        "doc_ids": datasets.features.Sequence(datasets.features.Value("int32")),
                        "metadata": datasets.features.Sequence(
                            {
                                "title": datasets.features.Value("string"),
                                "paper_sha": datasets.features.Value("string"),
                                "fields_of_study": datasets.features.Value("string"),
                                "Year": datasets.features.Value("string"),
                                "BookTitle": datasets.features.Value("string"),
                                "url": datasets.features.Value("string")
                            }
                        ),
                        "sentences": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Sequence(datasets.features.Value("int32")))),
                        "mentions": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Value("int32"), length=4)),
                        "relations": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Value("int32"), length=2)),
                        "id": datasets.Value("int32"),
                        "source": datasets.Value("string"),
                        "hard_10": datasets.features.Value("bool"),
                        "hard_20": datasets.features.Value("bool"),
                        "curated": datasets.features.Value("bool")
                    }
                ),
                supervised_keys=None,
                citation = _CITATION)


    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_DATA_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl")}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "dev.jsonl")}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "train.jsonl")}
            ),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        with jsonlines.open(filepath, 'r') as f:
            for i, topic in enumerate(f):
                topic['hard_10'] = topic['hard_10'] if 'hard_10' in topic else False
                topic['hard_20'] = topic['hard_20'] if 'hard_20' in topic else False
                topic["curated"] = topic["curated"] if "curated" in topic else False
                yield i, topic