# 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.
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

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

class RumourEval2019Config(datasets.BuilderConfig):

    def __init__(self, **kwargs):
        super(RumourEval2019Config, self).__init__(**kwargs)

class RumourEval2019(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("0.9.0")

    BUILDER_CONFIGS = [
        RumourEval2019Config(name="RumourEval2019", version=VERSION, description="Stance Detection texts"),
    ]
    
    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "source_text": datasets.Value("string"),
                "reply_text": datasets.Value("string"),
                "label": datasets.features.ClassLabel(
                    names=[
                        "support",
                        "query",
                        "deny",
                        "comment"
                    ]
                )
            }
        )

        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("rumoureval2019_train.csv")
        validation_text = dl_manager.download_and_extract("rumoureval2019_val.csv")
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_text, "split": "train"}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_text, "split": "validation"})
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        if split == "train" or split == "validation":
            with open(filepath, encoding="utf-8") as f:
                reader = csv.DictReader(f, delimiter=",")
                guid = 0
                for instance in reader:
                    instance["source_text"] = instance.pop("source_text")
                    instance["reply_text"] = instance.pop("reply_text")
                    instance["label"] = instance.pop("label")
                    instance['id'] = str(guid)
                    yield guid, instance
                    guid += 1