# 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.
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"""Machine translation EN-AZ dataset based on Google Translate and National Library of Azerbaijan."""


import os
import datasets


_CITATION = """\
@InProceedings{
huggingface:dataset,
title={Machine translation EN-AZ dataset},
author={Learning Machine LLC},
year={2022}
}
"""

_DESCRIPTION = """\
Machine translation EN-AZ dataset based on Google Translate and National Library of Azerbaijan.
"""

_HOMEPAGE = "https://huggingface.co/datasets/learningmachineaz/translate_enaz_10m"

_LICENSE = "Apache"

_URL = "https://learningmachine.az/datasets/translate_enaz_10m.zip"


class TranslateEnaz10m(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "translation": datasets.Value("string"),
                "source_text": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, 
                gen_kwargs={
                    "file_path": os.path.join(data_dir, "dataset_enaz_10m.tsv")
                }
            )
        ]

    def _generate_examples(self, file_path):
        with open(file_path, "r", encoding="utf-8") as f:
            for id_, row in enumerate(f):
                row = row.split("\t")
                yield id_, {
                    "translation": row[0].strip(),
                    "source_text": row[1].strip(),
                }