# coding=utf-8
# Copyright 2019 The TensorFlow 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.

"""SQUAD: The Stanford Question Answering Dataset."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import json
import os

from absl import logging
import tensorflow as tf
import tensorflow_datasets.public_api as tfds

_CITATION = """\
@article{2016arXiv160605250R,
       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
                 Konstantin and {Liang}, Percy},
        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
      journal = {arXiv e-prints},
         year = 2016,
          eid = {arXiv:1606.05250},
        pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
       eprint = {1606.05250},
}
"""

_DESCRIPTION = """\
Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
articles, where the answer to every question is a segment of text, or span, \
from the corresponding reading passage, or the question might be unanswerable.
"""


class SquadConfig(tfds.core.BuilderConfig):
    """BuilderConfig for SQUAD."""

    @tfds.core.disallow_positional_args
    def __init__(self, **kwargs):
        """BuilderConfig for SQUAD.

    Args:
      **kwargs: keyword arguments forwarded to super.
    """
        super(SquadConfig, self).__init__(**kwargs)


class Squad(tfds.core.GeneratorBasedBuilder):
    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""

    _URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
    _DEV_FILE = "dev-v1.1.json"
    _TRAINING_FILE = "train-v1.1.json"

    BUILDER_CONFIGS = [
        SquadConfig(
            name="plain_text",
            version=tfds.core.Version(
                "0.1.0", experiments={tfds.core.Experiment.S3: False}
            ),
            supported_versions=[
                tfds.core.Version(
                    "1.0.0", "New split API (https://tensorflow.org/datasets/splits)"
                ),
            ],
            description="Plain text",
        ),
    ]

    def _info(self):
        return tfds.core.DatasetInfo(
            builder=self,
            description=_DESCRIPTION,
            features=tfds.features.FeaturesDict(
                {
                    "id": tf.string,
                    "title": tfds.features.Text(),
                    "context": tfds.features.Text(),
                    "question": tfds.features.Text(),
                    "answers": tfds.features.Sequence(
                        {"text": tfds.features.Text(), "answer_start": tf.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):
        urls_to_download = {
            "train": os.path.join(self._URL, self._TRAINING_FILE),
            "dev": os.path.join(self._URL, self._DEV_FILE),
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            tfds.core.SplitGenerator(
                name=tfds.Split.TRAIN,
                num_shards=10,
                gen_kwargs={"filepath": downloaded_files["train"]},
            ),
            tfds.core.SplitGenerator(
                name=tfds.Split.VALIDATION,
                num_shards=1,
                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 tf.io.gfile.GFile(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,
                            },
                        }
