# 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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"""A new corpus of tagged data that can be useful for handling the issues in recognition of Classical Arabic named entities"""


import csv
import os

import datasets


_CITATION = """\
@article{article,
author = {Salah, Ramzi and Zakaria, Lailatul},
year = {2018},
month = {12},
pages = {},
title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)},
volume = {96},
journal = {Journal of Theoretical and Applied Information Technology}
}
"""

_DESCRIPTION = """\
Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.
"""

_HOMEPAGE = "https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

_URL = "https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus/archive/master.zip"


class Caner(datasets.GeneratorBasedBuilder):
    """Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities"""

    VERSION = datasets.Version("1.1.0")

    def _info(self):

        features = datasets.Features(
            {
                "token": datasets.Value("string"),
                "ner_tag": datasets.ClassLabel(
                    names=[
                        "Allah",
                        "Book",
                        "Clan",
                        "Crime",
                        "Date",
                        "Day",
                        "Hell",
                        "Loc",
                        "Meas",
                        "Mon",
                        "Month",
                        "NatOb",
                        "Number",
                        "O",
                        "Org",
                        "Para",
                        "Pers",
                        "Prophet",
                        "Rlig",
                        "Sect",
                        "Time",
                    ]
                ),
            }
        )

        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_dir = dl_manager.download_and_extract(my_urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, "Classical-Arabic-Named-Entity-Recognition-Corpus-master/CANERCorpus.csv"
                    ),
                    "split": "train",
                },
            )
        ]

    def _generate_examples(self, filepath, split):
        """Yields examples."""

        with open(filepath, encoding="utf-8") as csv_file:
            reader = csv.reader(csv_file, delimiter=",")
            next(reader, None)

            for id_, row in enumerate(reader):

                yield id_, {
                    "token": row[0],
                    "ner_tag": row[1],
                }