# coding=utf-8
# 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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# limitations under the License.
"""Liv4ever dataset."""


import json

import datasets


_CITATION = """\
@inproceedings{rikters-etal-2022,
    title = "Machine Translation for Livonian: Catering for 20 Speakers",
    author = "Rikters, Matīss  and
      Tomingas, Marili and
      Tuisk, Tuuli and
      Valts, Ernštreits  and
      Fishel, Mark",
    booktitle = "Proceedings of ACL 2022",
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics"
}
"""


_DESCRIPTION = """\
Livonian is one of the most endangered languages in Europe with just a tiny handful of speakers and virtually no publicly available corpora. 
In this paper we tackle the task of developing neural machine translation (NMT) between Livonian and English, with a two-fold aim: on one hand, 
preserving the language and on the other – enabling access to Livonian folklore, lifestories and other textual intangible heritage as well as 
making it easier to create further parallel corpora. We rely on Livonian's linguistic similarity to Estonian and Latvian and collect parallel 
and monolingual data for the four languages for translation experiments. We combine different low-resource NMT techniques like zero-shot translation, 
cross-lingual transfer and synthetic data creation to reach the highest possible translation quality as well as to find which base languages are 
empirically more helpful for transfer to Livonian. The resulting NMT systems and the collected monolingual and parallel data, including a manually 
translated and verified translation benchmark, are publicly released.

Fields:
- source: source of the data
- en: sentence in English
- liv: sentence in Livonian
"""

_HOMEPAGE = "https://huggingface.co/datasets/tartuNLP/liv4ever-data"

_LICENSE = "CC BY-NC-SA 4.0"

_REPO = "https://huggingface.co/datasets/tartuNLP/liv4ever/raw/main/"

_URLs = {
    "train": _REPO + "train.json",
    "dev": _REPO + "dev.json",
    "test": _REPO + "test.json",
}


class liv4ever(datasets.GeneratorBasedBuilder):
    """Liv4ever dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "source": datasets.Value("string"),
                "en": datasets.Value("string"),
                "liv: datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        data_dir = dl_manager.download_and_extract(_URLs)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_dir["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": data_dir["test"], "split": "test"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": data_dir["dev"],
                    "split": "dev",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        """Yields examples."""

        with open(filepath, encoding="utf-8") as f:
            data = json.load(f)
                
                
            for dialogue in data:
                source = dialogue["source"]
                sentences = dialogue["sentences"]
                
                i=0

                for turn in sentences:
                    i = i+1
                    sent_no = i
                    en = dialogue["en"]
                    liv = dialogue["liv"]

                    yield f"{sent_no}", {
                        "no": sent_no,
                        "source": source,
                        "en": en,
                        "liv": liv,
                    }