# coding=utf-8
# Copyright 2021 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# Lint as: python3
"""CST Wikinews classification dataset."""

import csv
from typing import List, Tuple, Dict, Generator

import datasets


_DESCRIPTION = """CST Wikinews dataset."""

_URLS = {
    "train": "https://huggingface.co/datasets/clarin-pl/cst-wikinews/resolve/main/train.csv",
    "test": "https://huggingface.co/datasets/clarin-pl/cst-wikinews/resolve/main/test.csv",
}

_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/305"

_LABELS=[
    "Brak_relacji",
    "Dalsze_informacje",
    "Krzyżowanie_się",
    "Opis",
    "Parafraza",
    "Spełnienie",
    "Streszczenie",
    "Tożsamość",
    "Tło_historyczne",
    "Uszczegółowienie",
    "Zawieranie",
    "Źródło",
]


class CSTWikinews(datasets.GeneratorBasedBuilder):

    def _info(self) -> datasets.DatasetInfo:
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "sentence_1": datasets.Value("string"),
                    "sentence_2": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(
                        names=_LABELS,
                        num_classes=len(_LABELS)
                    ),
                }
            ),
            homepage=_HOMEPAGE,
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:
        urls_to_download = _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.TEST,
                gen_kwargs={"filepath": downloaded_files["test"]},
            ),
        ]

    def _generate_examples(
        self, filepath: str
    ) -> Generator[Tuple[int, Dict[str, str]], None, None]:
        with open(filepath, mode="r", encoding="utf-8") as csv_file:
            csv_reader = csv.reader(
                csv_file,
                delimiter=",",
                quoting=csv.QUOTE_ALL,
                skipinitialspace=True,
            )
            next(csv_reader, None)  # skip the headers
            for row_id, (s1, s2, label) in enumerate(csv_reader):
                label = int(label)
                yield row_id, {
                    "sentence_1": s1,
                    "sentence_2": s2,
                    "label": label,
                }