# 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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# limitations under the License.

# Lint as: python3
"""The Arabic United nation Corpus dataset."""

from __future__ import absolute_import, division, print_function

import glob
import os
import re

import datasets


_DESCRIPTION = """\
The corpus is a part of the MultiUN corpus.\
It is a collection of translated documents from the United Nations.\
The corpus is download from the following website : \
[open parallel corpus](http://opus.datasetsl.eu/)  \
"""

_CITATION = """\
@inproceedings{eisele2010multiun,
  title={MultiUN: A Multilingual Corpus from United Nation Documents.},
  author={Eisele, Andreas and Chen, Yu},
  booktitle={LREC},
  year={2010}
}

"""

URL = "https://object.pouta.csc.fi/OPUS-MultiUN/v1/mono/ar.txt.gz"


class AracorpusConfig(datasets.BuilderConfig):
    """BuilderConfig for BookCorpus."""

    def __init__(self, **kwargs):
        """BuilderConfig for BookCorpus.
        Args:
        **kwargs: keyword arguments forwarded to super.
        """
        super(AracorpusConfig, self).__init__(
            version=datasets.Version("1.0.0", "New split API (https://tensorflow.org/datasets/splits)"), **kwargs
        )


class Aracorpus(datasets.GeneratorBasedBuilder):
    """BookCorpus dataset."""

    BUILDER_CONFIGS = [AracorpusConfig(name="plain_text", description="Plain text",)]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({"text": datasets.Value("string"),}),
            supervised_keys=None,
            homepage="http://opus.datasetsl.eu/",
            citation=_CITATION,
        )

    def _vocab_text_gen(self, archive):
        for _, ex in self._generate_examples(archive):
            yield ex["text"]

    def _split_generators(self, dl_manager):
        arch_path = dl_manager.download_and_extract(URL)
	
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"directory": arch_path}),
        ]

    def _generate_examples(self, directory):
        index=directory.rfind("datasets")
        index=index+8
        url=directory[:index]
        direct_name=directory[index+1:]
        directory=url

        files = [
            os.path.join(directory, direct_name),
        ]

        _id = 0
        for txt_file in files:
            with open(txt_file, mode="r") as f:
                for line in f:
                    yield _id, {"text": line.strip()}
                    _id += 1