import json
import gzip
import datasets
import os


_CITATION = """\

"""

_DESCRIPTION = """\

"""

_HOMEPAGE = ""


_LICENSE = ""

# TODO: Add link to the official dataset URLs here

_FILES = {
 '0': ['part_0_0.jsonl.gz', 'part_0_1.jsonl.gz', 'part_0_2.jsonl.gz'],
 '1': ['part_1_0.jsonl.gz', 'part_1_1.jsonl.gz', 'part_1_2.jsonl.gz'],
 '2': ['part_2_0.jsonl.gz', 'part_2_1.jsonl.gz', 'part_2_2.jsonl.gz'],
 '3': ['part_3_0.jsonl.gz', 'part_3_1.jsonl.gz', 'part_3_2.jsonl.gz'],
 '4': ['part_4_0.jsonl.gz', 'part_4_1.jsonl.gz', 'part_4_2.jsonl.gz'],
 '5': ['part_5_0.jsonl.gz', 'part_5_1.jsonl.gz', 'part_5_2.jsonl.gz'],
 '6': ['part_6_0.jsonl.gz', 'part_6_1.jsonl.gz'],
 '7': ['part_7_0.jsonl.gz', 'part_7_1.jsonl.gz', 'part_7_2.jsonl.gz'],
 '8': ['part_8_0.jsonl.gz', 'part_8_1.jsonl.gz'],
 '9': ['part_9_0.jsonl.gz', 'part_9_1.jsonl.gz'],
 '10': ['part_10_0.jsonl.gz', 'part_10_1.jsonl.gz'],
 '11': ['part_11_0.jsonl.gz', 'part_11_1.jsonl.gz'],
 '12': ['part_12_0.jsonl.gz', 'part_12_1.jsonl.gz'],
 '13': ['part_13_0.jsonl.gz', 'part_13_1.jsonl.gz'],
 '14': ['part_14_0.jsonl.gz', 'part_14_1.jsonl.gz']
 }
 
_URLS = {
    "all_data": "data/all_data"
}


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class OAGKx(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):
        _URLS['all_data']=['data/' + filename for part in _FILES for filename in _FILES[part]]
        if self.config.name == "extraction":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "id": datasets.Value("int64"),
                    "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("int64"),
                    "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("int64"),
                    "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)
        print(data_dir["all_data"])
        return [
            datasets.SplitGenerator(
                name="all_data",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepaths": data_dir["all_data"],
                    "split": "all_data",
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepaths, split):
        for filepath in filepaths:
            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.get("paper_id"),
                            "document": data["document"],
                            "doc_bio_tags": data.get("doc_bio_tags")
                        }
                    elif self.config.name == "generation":
                        yield key, {
                            "id": data.get("paper_id"),
                            "document": data["document"],
                            "extractive_keyphrases": data.get("extractive_keyphrases"),
                            "abstractive_keyphrases": data.get("abstractive_keyphrases")
                        }
                    else:
                        yield key, {
                            "id": data.get("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["other_metadata"]
                        }