import json
import os

import datasets
from citations_and_descriptions import (
    _SUMM_SCREEN_DESCRIPTION, _SUMM_SCREEN_CITATION,
    _GOV_REPORT_CITATION, _GOV_REPORT_DESCRIPTION,
    _ARXIV_CITATION, _ARXIV_DESCRIPTION,
    _FS_DESCRIPTION, _FS_CITATION,
)
from configs.arxiv import ArxivConfig
from configs.scrolls import ScrollsConfig
from configs.super_glue import BoolQConfig


class FS(datasets.GeneratorBasedBuilder):
    """The SCROLLS benchmark."""
    DEFAULT_WRITER_BATCH_SIZE = 1000  # because Narrative QA is a rather large dataset
    BUILDER_CONFIGS = [
        ScrollsConfig(
            additional_features=["id"],
            name="summ_screen_fd_debug",
            description=_SUMM_SCREEN_DESCRIPTION,
            data_url="https://huggingface.co/datasets/tau/fs/resolve/main/data/summ_screen_fd_debug.zip",
            citation=_SUMM_SCREEN_CITATION,
            url="https://github.com/mingdachen/SummScreen"
        ),
        ScrollsConfig(
            additional_features=["id"],
            name="gov_report",
            description=_GOV_REPORT_CITATION,
            data_url="https://huggingface.co/datasets/tau/fs/resolve/main/data/gov_report.zip",
            citation=_GOV_REPORT_DESCRIPTION,
            url="https://gov-report-data.github.io/"
        ),
        ArxivConfig(
            additional_features=['section_names', 'sections'],
            name="arxiv_debug",
            description=_ARXIV_CITATION,
            data_url="https://huggingface.co/datasets/tau/fs/resolve/main/data/arxiv_debug.zip",
            citation=_ARXIV_DESCRIPTION,
            url="https://github.com/armancohan/long-summarization"
        ),
        BoolQConfig(
            additional_features=[],
            name="boolq",
            description="",  # TODO
            data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/BoolQ.zip",
            citation=_ARXIV_DESCRIPTION,
            url=""  # TODO
        )
    ]

    def _info(self):
        features = {feature: datasets.Value("string") for feature in self.config.features}

        return datasets.DatasetInfo(
            description=_FS_DESCRIPTION + self.config.description,
            features=datasets.Features(features),
            homepage=self.config.url,
            citation=self.config.citation + "\n" + _FS_CITATION,
        )

    def _split_generators(self, dl_manager):
        dl_dir = dl_manager.download_and_extract(self.config.data_url)
        task_name = _get_task_name_from_data_url(self.config.data_url)
        dl_dir = os.path.join(dl_dir, task_name)

        data_files = {} if self.config.data_files is not None else None
        if data_files is not None:
            for split, paths in self.config.data_files.items():
                data_files[split] = paths[0]

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, self.config.train_file),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, self.config.validation_file),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, self.config.test_file),
                },
            ),
        ]

    def _generate_examples(self, data_file):
        with open(data_file, encoding="utf-8") as f:
            for line in f:
                row = json.loads(line)
                self.config.process(row)
                if self.config.target_key not in row:
                    row[self.config.target_key] = None
                yield row[self.config.id_key], row


def _get_task_name_from_data_url(data_url):
    return data_url.split("/")[-1].split(".")[0]