# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
Mirror of https://github.com/Alab-NII/2wikimultihop
"""

import json
import datasets
import pandas as pd

_DESCRIPTION = """\
Mirror of https://github.com/Alab-NII/2wikimultihop
"""

CITATION = """
@inproceedings{xanh2020_2wikimultihop,
    title = "Constructing A Multi-hop {QA} Dataset for Comprehensive Evaluation of Reasoning Steps",
    author = "Ho, Xanh  and
      Duong Nguyen, Anh-Khoa  and
      Sugawara, Saku  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.580",
    pages = "6609--6625",
}
"""

DEV_URL = "https://huggingface.co/datasets/somebody-had-to-do-it/2WikiMultihopQA/resolve/main/dev.parquet?download=true"
TRAIN_URL = "https://huggingface.co/datasets/somebody-had-to-do-it/2WikiMultihopQA/resolve/main/train.parquet?download=true"
TEST_URL = "https://huggingface.co/datasets/somebody-had-to-do-it/2WikiMultihopQA/resolve/main/test.parquet?download=true"


class Dataset2WikiMultihopQa(datasets.GeneratorBasedBuilder):
    """Mirror of https://github.com/Alab-NII/2wikimultihop"""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "_id": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "context": datasets.features.Sequence(
                        {
                            "title": datasets.Value("string"),
                            "content": datasets.features.Sequence(
                                datasets.Value("string")
                            ),
                        }
                    ),
                    "supporting_facts": datasets.features.Sequence(
                        {
                            "title": datasets.Value("string"),
                            "sent_id": datasets.Value("int32"),
                        }
                    ),
                    "evidences": datasets.features.Sequence(
                        {
                            "fact": datasets.Value("string"),
                            "relation": datasets.Value("string"),
                            "entity": datasets.Value("string"),
                        }
                    ),
                    "answer": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/Alab-NII/2wikimultihop",
            citation=CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        train_uri = dl_manager.download(TRAIN_URL)
        dev_uri = dl_manager.download(DEV_URL)
        test_uri = dl_manager.download(TEST_URL)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_uri}
            ),
            datasets.SplitGenerator(
                name=datasets.Split("dev"),
                gen_kwargs={"filepath": dev_uri, "split": "dev"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": test_uri},
            ),
        ]

    def _generate_examples(self, filepath, *args, **kwargs):
        """Yields examples from a Parquet file."""
        # Read the Parquet file into a DataFrame
        df = pd.read_parquet(filepath)

        # Iterate over each row in the DataFrame
        for idx, row in df.iterrows():
            yield idx, {
                "_id": row["_id"],
                "type": row["type"],
                "question": row["question"],
                "context": [
                    {"title": item[0], "content": item[1]}
                    for item in json.loads(row["context"])
                ],
                "supporting_facts": [
                    {"title": fact[0], "sent_id": fact[1]}
                    for fact in json.loads(row["supporting_facts"])
                ],
                "evidences": [
                    {
                        "fact": evidence[0],
                        "relation": evidence[1],
                        "entity": evidence[2],
                    }
                    for evidence in json.loads(row["evidences"])
                ],
                "answer": row["answer"],
            }