# coding=utf-8
# Copyright 2020 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.
# See the License for the specific language governing permissions and
# limitations under the License.


import datasets


_DESCRIPTION = """\
        mono corpus from http://www.opensubtitles.org/. Please check http://www.opensubtitles.org/ for the available corpora and licenses.
"""

_HOMEPAGE_URL = "http://opus.nlpl.eu"

_CITATION = """\
P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)
"""

_BASE_URL = "https://object.pouta.csc.fi/OPUS-{}/{}/mono/{}.txt.gz"

# Please note that only few pairs are shown here. You can use config to generate data for all language pairs
_LANGUAGES = [
    ("OpenSubtitles", "v2018", "en"),
]


class OpenSubtitlesConfig(datasets.BuilderConfig):
    def __init__(self, *args, corpus=None, lang=None, **kwargs):
        corpus.strip()
        splits = corpus.split()
        corpus = splits[0]
        corpus_version = splits[1]

        super().__init__(
            *args,
            name=f"{corpus}-{corpus_version}-{lang}",
            **kwargs,
        )
        self.corpus = corpus 
        self.corpus_version = corpus_version 
        self.lang = lang


class OpenSubtitles(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIG_CLASS = OpenSubtitlesConfig

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                },
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE_URL,
        )

    def _split_generators(self, dl_manager):
        def _base_url(corpus, corpus_version, lang):
            return _BASE_URL.format(corpus, corpus_version, lang)

        download_url = _base_url(self.config.corpus, self.config.corpus_version, self.config.lang)
        path = dl_manager.download_and_extract(download_url)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"datapath": path},
            )
        ]

    def _generate_examples(self, datapath):
        with open(datapath, encoding="utf-8") as f:
            for text_counter, line in enumerate(f):
                line = line.strip()

                result = (
                    text_counter,
                    {
                        "id": str(text_counter),
                        "text": line
                    },
                )
                text_counter += 1
                yield result