# coding=utf-8
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"""AfriSenti: A Twitter sentiment dataset for 14 African languages"""



_HOMEPAGE = "https://github.com/afrisenti-semeval/afrisent-semeval-2023"

_DESCRIPTION = """\
AfriSenti is the largest sentiment analysis benchmark dataset for under-represented African languages---covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and yoruba).
"""


_CITATION = """\
@inproceedings{muhammad-etal-2023-semeval,
  title="{S}em{E}val-2023 Task 12:  Sentiment Analysis for African Languages ({A}fri{S}enti-{S}em{E}val)",
  author="Muhammad, Shamsuddeen Hassan and
   Yimam, Seid and 
   Abdulmumin, Idris and 
   Ahmad, Ibrahim Sa'id  and 
   Ousidhoum, Nedjma, and
   Ayele, Abinew, and 
   Adelani, David and 
   Ruder, Sebastian and  
   Beloucif, Meriem and 
   Bello, Shehu Bello and 
   Mohammad, Saif M.",
  booktitle="Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
  month=jul,
  year="2023",
}
"""


import csv
import textwrap
import pandas as pd

import datasets

LANGUAGES = ['amh', 'hau', 'ibo', 'arq', 'ary', 'yor', 'por', 'twi', 'tso', 'tir', 'orm', 'pcm', 'kin', 'swa']

class AfriSentiConfig(datasets.BuilderConfig):
    """BuilderConfig for AfriSenti"""

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

        Args:
          text_features: `dict[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(AfriSentiConfig, 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 AfriSenti(datasets.GeneratorBasedBuilder):
    """AfriSenti benchmark"""

    BUILDER_CONFIGS = []

    for lang in LANGUAGES:
        BUILDER_CONFIGS.append(
            AfriSentiConfig(
                name=lang,
                description=textwrap.dedent(
                    f"""\
                    {lang} dataset."""
                ),
                text_features={"tweet": "tweet"},
                label_classes=["positive", "neutral", "negative"],
                label_column="label",
                train_url=f"https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/data/{lang}/train.tsv",
                valid_url=f"https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/data/{lang}/dev.tsv",
                test_url=f"https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/data/{lang}/test.tsv",
                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):
        df = pd.read_csv(filepath, sep='\t')

        print('-'*100)
        print(df.head())
        print('-'*100)

        for id_, row in df.iterrows():
            tweet = row["tweet"]
            label = row["label"]

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