# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
"""No Language Left Behind (NLLB)"""

import datasets
import csv
import json

_CITATION = ""  # TODO


_DESCRIPTION = ""  # TODO

_HOMEPAGE = ""  # TODO

_LICENSE = ""  # TODO

from .nllb_lang_pairs import LANG_PAIRS as _LANGUAGE_PAIRS

_URL_BASE = "https://storage.googleapis.com/allennlp-data-bucket/nllb/"

_URLs = {
    f"{src_lg}-{trg_lg}": f"{_URL_BASE}{src_lg}-{trg_lg}.gz"
    for src_lg, trg_lg in _LANGUAGE_PAIRS
}


class NLLBTaskConfig(datasets.BuilderConfig):
    """BuilderConfig for No Language Left Behind Dataset."""

    def __init__(self, src_lg, tgt_lg, **kwargs):
        super(NLLBTaskConfig, self).__init__(**kwargs)
        self.src_lg = src_lg
        self.tgt_lg = tgt_lg


class NLLB(datasets.GeneratorBasedBuilder):
    """No Language Left Behind Dataset."""

    BUILDER_CONFIGS = [
        NLLBTaskConfig(
            name=f"{src_lg}-{tgt_lg}",
            version=datasets.Version("1.0.0"),
            description=f"No Language Left Behind (NLLB): {src_lg} - {tgt_lg}",
            src_lg=src_lg,
            tgt_lg=tgt_lg,
        )
        for (src_lg, tgt_lg) in _LANGUAGE_PAIRS
    ]
    BUILDER_CONFIG_CLASS = NLLBTaskConfig

    def _info(self):
        # define feature types
        features = datasets.Features(
            {
                "translation": datasets.Translation(
                    languages=(self.config.src_lg, self.config.tgt_lg)
                ),
                "laser_score": datasets.Value("float32"),
                "source_sentence_lid": datasets.Value("float32"),
                "target_sentence_lid": datasets.Value("float32"),
                "source_sentence_source": datasets.Value("string"),
                "source_sentence_url": datasets.Value("string"),
                "target_sentence_source": datasets.Value("string"),
                "target_sentence_url": 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."""
        pair = f"{self.config.src_lg}-{self.config.tgt_lg}"  # string identifier for language pair
        url = _URLs[pair]  # url for download of pair-specific file
        data_file = dl_manager.download_and_extract(
            url
        )  # extract downloaded data and store path in data_file

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_file,
                    "source_lg": self.config.src_lg,
                    "target_lg": self.config.tgt_lg,
                },
            )
        ]

    def _generate_examples(self, filepath, source_lg, target_lg):
        with open(filepath, encoding="utf-8") as f:
            # reader = csv.reader(f, delimiter="\t")
            for id_, example in enumerate(f):
                try:
                    datarow = example.split("\t")
                    row = {}
                    row["translation"] = {
                        source_lg: datarow[0],
                        target_lg: datarow[1],
                    }  # create translation json
                    row["laser_score"] = float(datarow[2])
                    row["source_sentence_lid"] = float(datarow[3])
                    row["target_sentence_lid"] = float(datarow[4])
                    row["source_sentence_source"] = datarow[5]
                    row["source_sentence_url"] = datarow[6]
                    row["target_sentence_source"] = datarow[7]
                    row["target_sentence_url"] = datarow[8]
                    row = {
                        k: None if not v else v for k, v in row.items()
                    }  # replace empty values
                except:
                    print(datarow)
                    raise
                yield id_, row


# to test the script, go to the root folder of the repo (nllb) and run:
# datasets-cli test nllb --save_infos --all_configs