# Copyright 2022 Mads Kongsbak and Leon Derczynski
#
# 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.
"""DataLoader for ANS, an Arabic News Stance corpus"""


import csv
import json
import os

import datasets

_CITATION = """\
@inproceedings{,
    title = "Stance Prediction and Claim Verification: An {A}rabic Perspective", 
    author = "Khouja, Jude",
    booktitle = "Proceedings of the Third Workshop on Fact Extraction and {VER}ification ({FEVER})",
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
}
"""

_DESCRIPTION = """\
The dataset is a collection of news titles in arabic along with paraphrased and corrupted titles. The stance prediction version is a 3-class classification task. Data contains three columns: s1, s2, stance.
"""

_HOMEPAGE = ""

_LICENSE = "apache-2.0"

class ANSStanceConfig(datasets.BuilderConfig):

    def __init__(self, **kwargs):
        super(ANSStanceConfig, self).__init__(**kwargs)

class ANSStance(datasets.GeneratorBasedBuilder):
    """ANS dataset made in triples of (s1, s2, stance)"""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        ANSStanceConfig(name="stance", version=VERSION, description=""),
    ]
    
    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "s1": datasets.Value("string"),
                "s2": datasets.Value("string"),
                "stance": datasets.features.ClassLabel(
                    names=[
                        "disagree",
                        "agree",
                        "other"
                    ]
                )
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        train_text = dl_manager.download_and_extract("ans_train.csv")
        valid_text = dl_manager.download_and_extract("ans_dev.csv")
        test_text  = dl_manager.download_and_extract("ans_test.csv")
        
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,      gen_kwargs={"filepath": train_text, "split": "train"}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_text, "split": "validation"}),
            datasets.SplitGenerator(name=datasets.Split.TEST,       gen_kwargs={"filepath": test_text,  "split": "test"}),
        ]

    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter=",")
            guid = 0
            for instance in reader:
                instance["s1"] = instance.pop("s1")
                instance["s2"] = instance.pop("s2")
                instance["stance"] = instance.pop("stance")
                instance['id'] = str(guid)
                yield guid, instance
                guid += 1