# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os
from typing import List
import datasets
import logging

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
_URLS = {
    "train": _URL + "train-v1.1.json",
    "dev": _URL + "dev-v1.1.json",
}

# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class SquadDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    _URLS = _URLS
    VERSION = datasets.Version("1.1.0")

    def _info(self):
      raise ValueError('woops!')
      return datasets.DatasetInfo(
          description=_DESCRIPTION,
          features=datasets.Features(
              {
                  "id": datasets.Value("string"),
                  "title": datasets.Value("string"),
                  "context": datasets.Value("string"),
                  "question": datasets.Value("string"),
                  "answers": datasets.features.Sequence(
                      {"text": datasets.Value("string"), "answer_start": datasets.Value("int32"),}
                  ),
              }
          ),
          # No default supervised_keys (as we have to pass both question
          # and context as input).
          supervised_keys=None,
          homepage="https://rajpurkar.github.io/SQuAD-explorer/",
          citation=_CITATION,
      )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
      urls_to_download = self._URLS
      downloaded_files = dl_manager.download_and_extract(urls_to_download)

      return [
          datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
          datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
      ]
      
    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logging.info("generating examples from = %s", filepath)
        with open(filepath) as f:
            squad = json.load(f)
            for article in squad["data"]:
                title = article.get("title", "").strip()
                for paragraph in article["paragraphs"]:
                    context = paragraph["context"].strip()
                    for qa in paragraph["qas"]:
                        question = qa["question"].strip()
                        id_ = qa["id"]

                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
                        answers = [answer["text"].strip() for answer in qa["answers"]]

                        # Features currently used are "context", "question", and "answers".
                        # Others are extracted here for the ease of future expansions.
                        yield id_, {
                            "title": title,
                            "context": context,
                            "question": question,
                            "id": id_,
                            "answers": {"answer_start": answer_starts, "text": answers,},
                        }