import json
import datasets

# _SPLIT = ['test']
_CITATION = """\
@inproceedings{10.5555/1859664.1859668,
author = {Kim, Su Nam and Medelyan, Olena and Kan, Min-Yen and Baldwin, Timothy},
title = {SemEval-2010 Task 5: Automatic Keyphrase Extraction from Scientific Articles},
year = {2010},
publisher = {Association for Computational Linguistics},
address = {USA},
abstract = {This paper describes Task 5 of the Workshop on Semantic Evaluation 2010 (SemEval-2010). Systems are to automatically assign keyphrases or keywords to given scientific articles. The participating systems were evaluated by matching their extracted keyphrases against manually assigned ones. We present the overall ranking of the submitted systems and discuss our findings to suggest future directions for this task.},
booktitle = {Proceedings of the 5th International Workshop on Semantic Evaluation},
pages = {21–26},
numpages = {6},
location = {Los Angeles, California},
series = {SemEval '10}
}

"""

_DESCRIPTION = """\

"""

_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here

_URLS = {
    "test": "test.jsonl",
    "train": "train.jsonl"
}


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class SemEval2010(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("0.0.1")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="extraction", version=VERSION,
                               description="This part of my dataset covers extraction"),
        datasets.BuilderConfig(name="generation", version=VERSION,
                               description="This part of my dataset covers generation"),
        datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data"),
    ]

    DEFAULT_CONFIG_NAME = "extraction"

    def _info(self):
        if self.config.name == "extraction":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document": datasets.features.Sequence(datasets.Value("string")),
                    "doc_bio_tags": datasets.features.Sequence(datasets.Value("string"))

                }
            )
        elif self.config.name == "generation":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document": datasets.features.Sequence(datasets.Value("string")),
                    "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
                    "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string"))

                }
            )
        else:
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document": datasets.features.Sequence(datasets.Value("string")),
                    "doc_bio_tags": datasets.features.Sequence(datasets.Value("string")),
                    "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
                    "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
                    "other_metadata": datasets.features.Sequence(
                        {
                            "text": datasets.features.Sequence(datasets.Value("string")),
                            "bio_tags": datasets.features.Sequence(datasets.Value("string"))
                        }
                    )

                }
            )
        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=features,
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        data_dir = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir['train'],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir['test'],
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                if self.config.name == "extraction":
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "id": data['paper_id'],
                        "document": data["document"],
                        "doc_bio_tags": data.get("doc_bio_tags")
                    }
                elif self.config.name == "generation":
                    yield key, {
                        "id": data['paper_id'],
                        "document": data["document"],
                        "extractive_keyphrases": data.get("extractive_keyphrases"),
                        "abstractive_keyphrases": data.get("abstractive_keyphrases")
                    }
                else:
                    yield key, {
                        "id": data['paper_id'],
                        "document": data["document"],
                        "doc_bio_tags": data.get("doc_bio_tags"),
                        "extractive_keyphrases": data.get("extractive_keyphrases"),
                        "abstractive_keyphrases": data.get("abstractive_keyphrases"),
                        "other_metadata": data.get("other_metadata")
                    }