# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace NLP 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
#
# 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.

# Lint as: python3
"""tweetyface dataset."""


import json

import datasets

_DESCRIPTION = """\
Dataset containing Tweets from prominent Twitter Users in various languages. \
The dataset has been created utilizing a crawler for the Twitter API.\n \
"""

_HOMEPAGE = "https://github.com/ml-projects-kiel/OpenCampus-ApplicationofTransformers"

URL = "https://raw.githubusercontent.com/ml-projects-kiel/OpenCampus-ApplicationofTransformers/qa/data/"

_URLs = {
    "english": {
        "train": URL + "tweetyface_en/train.json",
        "validation": URL + "tweetyface_en/validation.json",
    },
    "german": {
        "train": URL + "tweetyface_de/train.json",
        "validation": URL + "tweetyface_de/validation.json",
    },
}

_VERSION = "0.3.0"

_LICENSE = """
Apache License Version 2.0
"""


class TweetyFaceConfig(datasets.BuilderConfig):
    """BuilderConfig for TweetyFace."""

    def __init__(self, **kwargs):
        """BuilderConfig for TweetyFace.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(TweetyFaceConfig, self).__init__(**kwargs)


class TweetyFace(datasets.GeneratorBasedBuilder):
    """tweetyface"""

    BUILDER_CONFIGS = [
        TweetyFaceConfig(
            name=lang,
            description=f"{lang.capitalize()} Twitter Users",
            version=datasets.Version(_VERSION),
        )
        for lang in _URLs.keys()
    ]

    def _info(self):
        if self.config.name == "english":
            names = [
                "MKBHD",
                "elonmusk",
                "alyankovic",
                "Cristiano",
                "katyperry",
                "neiltyson",
                "BillGates",
                "BillNye",
                "GretaThunberg",
                "BarackObama",
                "Trevornoah",
            ]
        else:
            names = [
                "OlafScholz",
                "Karl_Lauterbach",
                "janboehm",
                "Markus_Soeder",
            ]
        return datasets.DatasetInfo(
            description=_DESCRIPTION + self.config.description,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=names),
                    "idx": datasets.Value("string"),
                    "ref_tweet": datasets.Value("bool"),
                    "reply_tweet": datasets.Value("bool"),
                }
            ),
            homepage=_HOMEPAGE,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        my_urls = _URLs[self.config.name]
        data_dir = dl_manager.download_and_extract(my_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": data_dir["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": data_dir["validation"]},
            ),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form by iterating on all the files."""
        with open(filepath, encoding="utf-8") as f:
            for row in f:
                data = json.loads(row)
                idx = data["idx"]
                yield idx, data