# 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
"""FeedbackQA: An Retrieval-based Question Answering Dataset with User Feedback"""


import json

import datasets
import os

logger = datasets.logging.get_logger(__name__)


_CITATION = """
"""

_DESCRIPTION = """\
FeedbackQA is a retrieval-based QA dataset \
that contains interactive feedback from users. \
It has two parts: the first part contains a conventional RQA dataset, \
whilst this repo contains the second part, which contains feedback(ratings and natural language explanations) for QA pairs.
"""

_URL = "https://drive.google.com/drive/folders/1mIcxZZ643k6SVJnZw1FmEOhndaFx4_PG?usp=sharing"
#_URLS = {
#    "train": _URL + "train-v1.1.json",
#    "dev": _URL + "dev-v1.1.json",
#}


class FeedbackConfig(datasets.BuilderConfig):
    """BuilderConfig for FeedbackQA."""

    def __init__(self, **kwargs):
        """BuilderConfig for FeedbackQA.

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


class FeedbackQA(datasets.GeneratorBasedBuilder):
    """FeedbackQA: retrieval-based QA dataset that contains interactive feedback from users."""

    BUILDER_CONFIGS = [
        FeedbackConfig(
            name="plain_text",
            version=datasets.Version("1.0.0", ""),
            description="Plain text",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    #"id": datasets.Value("string"),
                    #"title": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "answer": datasets.Value("string"),
                    "feedback": datasets.features.Sequence(
                        {
                            "rating": datasets.Value("string"),
                            "explanation": datasets.Value("string"),
                        }
                    ),
                }
            ),
            # No default supervised_keys (as we have to pass both question
            # and context as input).
            supervised_keys=None,
            homepage="https://mcgill-nlp.github.io/feedbackQA_data/",
            citation=_CITATION
        )

    def _split_generators(self, dl_manager):
        downloaded_files_path = dl_manager.download_and_extract(_URL)
        train_file = os.path.join(downloaded_files_path, 'feedback_train.json')
        valid_file = os.path.join(downloaded_files_path, 'feedback_valid.json')
        test_file = os.path.join(downloaded_files_path, 'feedback_test.json')

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_file}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_file}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_file}),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        key = 0
        with open(filepath, encoding="utf-8") as f:
            fbqa = json.load(f)
            for dict_item in fbqa:
                question = dict_item['question']
                passage_text = ''
                if dict_item['passage']['reference']['page_title']:
                    passage_text += dict_item['passage']['reference']['page_title'] + '\n'
                if dict_item['passage']['reference']['section_headers']:
                    passage_text += '\n'.join(dict_item['passage']['reference']['section_headers']) + '\n'
                if dict_item['passage']['reference']['section_content']:
                    passage_text += dict_item['passage']['reference']['section_content']

                yield key, {
                            "question": question,
                            "answer": passage_text,
                            "feedback": {
                                "rating": dict_item['rating'],
                                "explanation": dict_item['feedback'],
                            },
                        }
                key += 1