# 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.


import csv
import textwrap
import pandas as pd

import datasets

LANGUAGES = ['malay', 'hindi', 'japanese', 'german',
             'italian', 'english', 'portuguese', 'french',
             'spanish', 'chinese', 'indonesian', 'arabic'
             ]


class MultilingualSentimentsConfig(datasets.BuilderConfig):
    """BuilderConfig for Multilingual Sentiments"""

    def __init__(
        self,
        text_features,
        label_column,
        label_classes,
        train_url,
        valid_url,
        test_url,
        citation,
        **kwargs,
    ):
        """BuilderConfig for Multilingual Sentiments.

        Args:
          text_features: `dict[string, string]`, map from the name of the feature
            dict for each text field to the name of the column in the txt/csv/tsv file
          label_column: `string`, name of the column in the txt/csv/tsv file corresponding
            to the label
          label_classes: `list[string]`, the list of classes if the label is categorical
          train_url: `string`, url to train file from
          valid_url: `string`, url to valid file from
          test_url: `string`, url to test file from
          citation: `string`, citation for the data set
          **kwargs: keyword arguments forwarded to super.
        """
        super(MultilingualSentimentsConfig, self).__init__(
            version=datasets.Version("1.0.0", ""), **kwargs)
        self.text_features = text_features
        self.label_column = label_column
        self.label_classes = label_classes
        self.train_url = train_url
        self.valid_url = valid_url
        self.test_url = test_url
        self.citation = citation


class MultilingualSentiments(datasets.GeneratorBasedBuilder):
    """Multilingual Sentiments benchmark"""

    BUILDER_CONFIGS = []

    BUILDER_CONFIGS.append(
        MultilingualSentimentsConfig(
            name="all",
            description=textwrap.dedent(
                f"""\
                All datasets."""
            ),
            text_features={"text": "text", "language": "language"},
            label_classes=["positive", "neutral", "negative"],
            label_column="label",
            train_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/train.csv",
            valid_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/valid.csv",
            test_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/test.csv",
            citation=textwrap.dedent(
                f"""\
            All citation"""
            ),
        ),
    )

    for lang in LANGUAGES:
        BUILDER_CONFIGS.append(
            MultilingualSentimentsConfig(
                name=lang,
                description=textwrap.dedent(
                    f"""\
                    {lang} dataset."""
                ),
                text_features={"text": "text"},
                label_classes=["positive", "neutral", "negative"],
                label_column="label",
                train_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/{lang}/train.csv",
                valid_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/{lang}/valid.csv",
                test_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/{lang}/test.csv",
                citation=textwrap.dedent(
                    f"""\
                {lang} citation"""
                ),
            ),
        )

    def _info(self):
        features = {text_feature: datasets.Value(
            "string") for text_feature in self.config.text_features}

        features["label"] = datasets.features.ClassLabel(
            names=self.config.label_classes)

        return datasets.DatasetInfo(
            description=self.config.description,
            features=datasets.Features(features),
            citation=self.config.citation,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        train_path = dl_manager.download_and_extract(self.config.train_url)
        valid_path = dl_manager.download_and_extract(self.config.valid_url)
        test_path = dl_manager.download_and_extract(self.config.test_url)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
                                    "filepath": train_path}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={
                                    "filepath": valid_path}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={
                                    "filepath": test_path}),
        ]

    def _generate_examples(self, filepath):

        # with open(filepath, encoding="utf-8") as f:
        #     reader = csv.reader(f, quoting=csv.QUOTE_NONE)
        df = pd.read_csv(filepath)

        # next(reader, None)  # skip the headers

        for id_, row in df.iterrows():

            if self.config.name != "all":
                text, label = row["text"], row["label"]

                yield id_, {"text": text, "label": label}

            else:
                text, label, language = row["text"], row["label"], row["language"]

                yield id_, {"text": text, "label": label, "language": language}