import csv
import datasets

_CITATION = """\
@inproceedings{koto-etal-2023-indommlu,
    title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
    author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = December,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
}"""


subject2english = {
    'Sejarah': 'History',
     'Geografi': 'Geography',
     'Bahasa Lampung': 'Lampungic',
     'IPS': 'Social science',
     'Bahasa Bali': 'Balinese',
     'Bahasa Makassar': 'Makassarese',
     'Bahasa Banjar': 'Banjarese',
     'Kimia': 'Chemistry',
     'Biologi': 'Biology',
     'IPA': 'Science',
     'Agama Kristen': 'Christian religion',
     'Kesenian': 'Art',
     'Agama Islam': 'Islam religion',
     'Agama Hindu': 'Hindu religion',
     'Bahasa Madura': 'Madurese',
     'Penjaskes': 'Sport',
     'Bahasa Indonesia': 'Indonesian language',
     'Fisika': 'Physics',
     'Budaya Alam Minangkabau': 'Minangkabau culture',
     'Bahasa Dayak Ngaju': 'Dayak language',
     'Sosiologi': 'Sociology',
     'Ekonomi': 'Economy',
     'Bahasa Sunda': 'Sundanese',
     'Bahasa Jawa': 'Javanese',
     'PPKN': 'Civic education',
}

subject2group = {
     'Sejarah': 'Humanities',
     'Geografi': 'Social science',
     'Bahasa Lampung': 'Local languages and cultures',
     'IPS': 'Social science',
     'Bahasa Bali': 'Local languages and cultures',
     'Bahasa Makassar': 'Local languages and cultures',
     'Bahasa Banjar': 'Local languages and cultures',
     'Kimia': 'STEM',
     'Biologi': 'STEM',
     'IPA': 'STEM',
     'Agama Kristen': 'Humanities',
     'Kesenian': 'Humanities',
     'Agama Islam': 'Humanities',
     'Agama Hindu': 'Humanities',
     'Bahasa Madura': 'Local languages and cultures',
     'Penjaskes': 'Humanities',
     'Bahasa Indonesia': 'Indonesian language',
     'Fisika': 'STEM',
     'Budaya Alam Minangkabau': 'Local languages and cultures',
     'Bahasa Dayak Ngaju': 'Local languages and cultures',
     'Sosiologi': 'Social science',
     'Ekonomi': 'Social science',
     'Bahasa Sunda': 'Local languages and cultures',
     'Bahasa Jawa': 'Local languages and cultures',
     'PPKN': 'Social science',
}

special_case = ['SD-SMP-SMA', 'SD-SMP']
level_mapper = {
     'SMA': 'SMA',
     'Seleksi PTN': 'University entrance test',         
     'SD': 'SD',
     'SMP': 'SMP',
     'Kelas I SD': 'SD',
     'Kelas X SMA': 'SMA',
     'Kelas XI SMA': 'SMA',
     'Kelas XII SMA': 'SMA',
     'V SD': 'SD',
     'VI SD': 'SD',
     'VII SMP': 'SMP',
     'VIII SMP ': 'SMP',
     'IX SMP': 'SMP',
     'Kelas III SD':'SD',
     'Kelas IV SD': 'SD',
     'Kelas II SD': 'SD'
}

def fix_level(level, kelas):
    #Fixing Level
    if level in special_case:
        kelas = float(kelas)
        if kelas >=1 and kelas <= 6:
            level = 'SD'
        elif kelas >=7 and kelas <= 9:
            level = 'SMP'
        elif kelas >=10:
            level = 'SMA'
        else:
            print(level)
    fixed_level = level_mapper[level]

    #Fixing class
    fixed_kelas = -1
    kelas = str(kelas)
    if kelas.strip() in ['PTN', '2023-10-12 00:00:00']:
        fixed_kelas = 13
    elif kelas == '4,5,6':
        fixed_kelas = 6
    else:
        fixed_kelas = int(float(kelas.strip()))
    
    # sanity check over the level and kelas
    return fixed_level, fixed_kelas


_URL = {
    'test': "https://huggingface.co/datasets/indolem/IndoMMLU/resolve/main/IndoMMLU.csv",
}

class IndoMMLUConfig(datasets.BuilderConfig):
    """IndoMMLUConfig for IndoMMLU"""

    def __init__(self, **kwargs):
        """BuilderConfig for IndoStoryCloze.
        **kwargs: keyword arguments forwarded to super.
        """
        # Version history:
        # 1.0.0: Release version
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.features = ['subject', 'group', 'level', 'class', 'question', 'options', 'answer', 'is_for_fewshot']


class IndoMMLU(datasets.GeneratorBasedBuilder):
    """The IndoMMLU Datasets."""

    BUILDER_CONFIGS = [IndoMMLUConfig()]

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

        return datasets.DatasetInfo(
                        description='IndoMMLU',
                        features=datasets.Features(features),
                        homepage='https://github.com/fajri91/IndoMMLU',
                        citation=_CITATION
        )

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URL)

        return [           
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"data_file": downloaded_file['test']}),
        ]

    def _generate_examples(self, data_file):
        data = csv.DictReader(open(data_file, newline=''))
        for i, row in enumerate(data):
            fixed_level, fixed_kelas = fix_level(row['level'], row['kelas'])
            yield i, {
                "subject": subject2english[row['subject']],
                "group": subject2group[row['subject']],
                "level": fixed_level,
                "class": fixed_kelas,
                "question": row['soal'],
                "options": row['jawaban'].split('\n'),
                "answer": row['kunci'],
                "is_for_fewshot": row['is_for_fewshot']
            }