# coding=utf-8
# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""The Rationalized English-French Semantic Divergences (REFreSD) dataset."""


import csv

import datasets


_CITATION = """\
@inproceedings{briakou-carpuat-2020-detecting,
    title = "Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank",
    author = "Briakou, Eleftheria and Carpuat, Marine",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.121",
    pages = "1563--1580",
}
"""

_DESCRIPTION = """\
The Rationalized English-French Semantic Divergences (REFreSD) dataset consists of 1,039
 English-French sentence-pairs annotated with sentence-level divergence judgments and token-level
 rationales. For any questions, write to ebriakou@cs.umd.edu.
"""

_HOMEPAGE = "https://github.com/Elbria/xling-SemDiv/tree/master/REFreSD"

_LICENSE = "MIT"

_URL = "https://raw.githubusercontent.com/Elbria/xling-SemDiv/master/REFreSD/REFreSD_rationale"


class Refresd(datasets.GeneratorBasedBuilder):
    """The Rationalized English-French Semantic Divergences (REFreSD) dataset."""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "sentence_pair": datasets.Translation(languages=["en", "fr"]),
                "label": datasets.features.ClassLabel(names=["divergent", "equivalent"]),
                "all_labels": datasets.features.ClassLabel(
                    names=["unrelated", "some_meaning_difference", "no_meaning_difference"]
                ),
                "rationale_en": datasets.features.Sequence(datasets.Value("int32")),
                "rationale_fr": datasets.features.Sequence(datasets.Value("int32")),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        my_urls = _URL
        data_file_path = dl_manager.download_and_extract(my_urls)
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file_path})]

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            for idx, row in enumerate(reader):
                yield idx, {
                    "sentence_pair": {"fr": row["#french_sentence"], "en": row["#english_sentence"]},
                    "label": row["#binary_label"],
                    "all_labels": row["#3_labels"],
                    "rationale_en": [int(v) for v in row["#english_rational"].split(" ")],
                    "rationale_fr": [int(v) for v in row["#french_rationale"].split(" ")],
                }