# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3


import csv
import os
import textwrap
import numpy as np
import datasets
import pandas as pd


_CITATION = """
@article{sileo2023generating,
  title={Generating multiple-choice questions for medical question answering with distractors and cue-masking},
  author={Sileo, Damien and Uma, Kanimozhi and Moens, Marie-Francine},
  journal={arXiv preprint arXiv:2303.07069},
  year={2023}
}
"""

_DESCRIPTION = """\
Anonymous submission
"""

URL = 'https://sileod.s3.eu-west-3.amazonaws.com/wikimedqa/'


class WikiMedQAConfig(datasets.BuilderConfig):
    """BuilderConfig for WikiMedQA."""

    def __init__(
        self,
        data_dir,
        label_classes=None,
        process_label=lambda x: x,
        **kwargs,
    ):

        super(WikiMedQAConfig, self).__init__(version=datasets.Version("1.0.5", ""), **kwargs)
        self.text_features = {k:k for k in ['text']+[f'option_{i}' for i in range(8)]}
        self.label_column = 'label'
        self.label_classes = list('01234567')
        self.data_url = URL
        self.url=URL
        self.data_dir=data_dir
        self.citation = _CITATION
        self.process_label = process_label


class WikiMedQA(datasets.GeneratorBasedBuilder):
    """Evaluation of word estimative of probability understanding"""

    BUILDER_CONFIGS = [
        WikiMedQAConfig(
            name="medwiki",
            data_dir="medwiki"),
         WikiMedQAConfig(
            name="wikem",
            data_dir="wikem"),
        WikiMedQAConfig(
            name="wikidoc",
            data_dir="wikidoc"),
        ]

    def _info(self):
        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
        features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
        features["idx"] = datasets.Value("int32")

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            homepage=self.config.url,
            citation=self.config.citation + "\n" + _CITATION,
        )
    def _split_generators(self, dl_manager):
        
        data_dirs=[]
        for split in ['train','validation','test']:
            url=f'{URL}{self.config.data_dir}.csv'
            print(url)
            data_dirs+=[dl_manager.download(url)]
        print(data_dirs)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_file": data_dirs[0],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "data_file": data_dirs[1],
                    "split": "dev",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "data_file": data_dirs[2],
                    "split": "test",
                },
            ),
        ]

    def _generate_examples(self, data_file, split):
        df = pd.read_csv(data_file)
        df=df[['text','options','label']]
        train, dev, test = np.split(df.sample(frac=1, random_state=42), 
                       [int(.9*len(df)), int(.95*len(df))])
        df=eval(split)
        df['options']=df['options'].map(eval)
        for i in range(8):
            df[f'option_{i}']=df.options.map(lambda x:x[i])
        del df['options']
        df['idx']=df.index
        for idx, example in df.iterrows():
            yield idx, dict(example)