"""OAB Exams dataset"""

import datasets
import pandas as pd
import re
from collections import defaultdict
import os
import json

_CITATION = """@misc{almeida2023bluex,
      title={BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXams}, 
      author={Thales Sales Almeida and Thiago Laitz and Giovana K. Bonás and Rodrigo Nogueira},
      year={2023},
      eprint={2307.05410},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """
Despite Portuguese being the fifth most widely spoken language, there is a lack of freely available resources for evaluating language models in Portuguese. This repository contains a multimodal dataset consisting of the two leading university entrance exams conducted in Brazil: Convest (Unicamp) and Fuvest (USP), spanning from 2018 to 2024. The dataset comprises a total of 1260 questions, of which 724 do not have accompanying images.
"""

_HOMEPAGE="https://github.com/Portuguese-Benchmark-Datasets/BLUEX"

_URL = "portuguese-benchmark-datasets/BLUEX"
_URL = "https://raw.githubusercontent.com/Portuguese-Benchmark-Datasets/BLUEX/main/data/bluex_dataset.zip"

class BLUEX_without_images(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.1.0")
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "question_number": datasets.Value("int32"),
                    "exam_id": datasets.Value("string"),
                    "exam_year": datasets.Value("string"),
                    "university": datasets.Value("string"),
                    "question_type": datasets.Sequence(datasets.Value("string")),
                    "nullified": datasets.Value("bool"),
                    "question": datasets.Value("string"),
                    "choices": datasets.Sequence(feature={
                        "text": datasets.Value("string"),
                        "label": datasets.Value("string")
                    }),
                    "answerKey": datasets.Value("string"),
                    }),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        #dataset = datasets.load_dataset(_URL, split="questions")
        #remove questions that require images
        #dataset = dataset.filter(lambda example: not example['IU'] and example['alternatives_type'] == 'string')
        filedir = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filedir": os.path.join(filedir, 'questions')
                }
            )
        ]

    def _generate_examples(self, filedir):

        for university in os.listdir(filedir):
            years = sorted(os.listdir(os.path.join(filedir, university)))
            for year in years:
                days = [d for d in os.listdir(os.path.join(filedir, university, year)) if os.path.isdir(os.path.join(filedir, university, year, d))]
                if len(days) == 0:
                    days = ['']
                days = sorted(days)
                for day in days:
                    if day == '':
                        path = os.path.join(filedir, university, year)
                    else:
                        path = os.path.join(filedir, university, year, day)

                    exam_id = f"{university}_{year}" if day == '' else f"{university}_{year}_{day.replace('day', '')}"
                    filenames = sorted(os.listdir(path), key=lambda x: int(re.findall(r'\d+', x)[0]))
                    for filename in filenames:
                        if filename.endswith('.json'):
                            with open(os.path.join(path, filename), 'r') as f:
                                example = json.load(f)

                            if example['IU'] or example['alternatives_type'] != 'string' or example['has_associated_images']:
                                continue

                            choices = {
                                "text": [],
                                "label": ["A", "B", "C", "D", "E"]
                            }
                            for alternative in example['alternatives']:
                                choices['text'].append(alternative[3:].strip())
                            choices['label'] = choices['label'][:len(choices['text'])]

                            doc_id = f"{exam_id}_{example['number']}"
                            yield doc_id, {
                                "id": doc_id, 
                                "question_number": example['number'], 
                                "exam_id": exam_id, 
                                "exam_year": year,
                                "university": university,
                                "question_type": example['subject'], 
                                "nullified": None, 
                                "question": example['question'], 
                                "choices": choices,
                                "answerKey": example['answer'] 
                            }