# coding=utf-8
# Copyright 2020 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
#
# 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.
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# Lint as: python3
"""MIHVG TOTAWARPAD dataset."""

import os
import datasets

_CITATION = """\
@misc{
  author={Laszlo, David},
  title={totag-arpad},
  year={2023},
  howpublished={\\url{https://github.com/stedko/totagarpad}}
}"""

_DESCRIPTION = """\
Train dataset Tota W. Arpad cikkei a hvg-rol \
"""

class TotaGArpad(datasets.GeneratorBasedBuilder):
    """Tota Gep Arpad dataset builder."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({"text": datasets.Value("string")}),
            supervised_keys=None,
            homepage="https://github.com/stedko/totagarpad/blob/main/totawarpad-articles.txt",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        download_path = dl_manager.download_and_extract(
            "https://raw.githubusercontent.com/stedko/totagarpad/main/totawarpad-articles.txt"
        )
        if os.path.isdir(download_path):
            txt_path = os.path.join(download_path, "input.txt")
        else:
            txt_path = download_path
        with open(txt_path, "r", encoding="utf-8") as f:
            text = f.read()

        i = int(len(text) * 0.9)
        train_text, text = text[:i], text[i:]
        i = int(len(text) * 0.5)
        validation_text, text = text[:i], text[i:]
        test_text = text

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"split_key": "train", "split_text": train_text},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"split_key": "validation", "split_text": validation_text},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"split_key": "test", "split_text": test_text},
            ),
        ]

    def _generate_examples(self, split_key, split_text):
        data_key = split_key 
        feature_dict = {"text": split_text}
        yield data_key, feature_dict