import csv
from typing import List, Generator, Tuple, Dict

import datasets
from datasets import DownloadManager
from datasets.info import SupervisedKeysData

_DESCRIPTION = """AspectEmo 1.0 dataset: Multi-Domain Corpus of Consumer Reviews for Aspect-Based 
                Sentiment Analysis"""

_CLASSES = ['O',
            'B-a_plus_m',
            'B-a_minus_m',
            'B-a_zero',
            'B-a_minus_s',
            'B-a_plus_s',
            'B-a_amb',
            'B-a_minus_m:B-a_minus_m',
            'B-a_minus_m:B-a_minus_m:B-a_minus_m',
            'B-a_plus_m:B-a_plus_m',
            'B-a_plus_m:B-a_plus_m:B-a_plus_m',
            'B-a_zero:B-a_zero:B-a_zero',
            'B-a_zero:B-a_zero',
            'I-a_plus_m',
            'B-a_zero:B-a_plus_m',
            'B-a_minus_m:B-a_zero',
            'B-a_minus_s:B-a_minus_s:B-a_minus_s',
            'B-a_amb:B-a_amb',
            'I-a_minus_m',
            'B-a_minus_s:B-a_minus_s',
            'B-a_plus_s:B-a_plus_s:B-a_plus_s',
            'B-a_plus_m:B-a_plus_m:B-a_plus_m:B-a_plus_m:B-a_plus_m:B-a_plus_m',
            'B-a_plus_m:B-a_amb',
            'B-a_minus_m:B-a_plus_m',
            'B-a_amb:B-a_amb:B-a_amb',
            'I-a_zero',
            'B-a_plus_s:B-a_plus_s',
            'B-a_plus_m:B-a_plus_s',
            'B-a_plus_m:B-a_zero',
            'B-a_zero:B-a_zero:B-a_zero:B-a_zero:B-a_zero:B-a_zero',
            'B-a_zero:B-a_minus_m',
            'B-a_amb:B-a_plus_s',
            'B-a_zero:B-a_minus_s']

_URLS = {
    "train": "https://huggingface.co/datasets/clarin-pl/aspectemo/resolve/main/data/train.tsv",
    "validation": "https://huggingface.co/datasets/clarin-pl/aspectemo/resolve/main/data/val.tsv",
    "test": "https://huggingface.co/datasets/clarin-pl/aspectemo/resolve/main/data/test.tsv",
}


class AspectEmo(datasets.GeneratorBasedBuilder):
    def _info(self) -> datasets.DatasetInfo:
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "orth": datasets.Sequence(datasets.Value("string")),
                    "ctag": datasets.Sequence(datasets.Value("string")),
                    "sentiment": datasets.Sequence(datasets.features.ClassLabel(
                        names=_CLASSES,
                        num_classes=len(_CLASSES)
                    )),
                }
            ),
            supervised_keys=SupervisedKeysData(input="orth", output="sentiment"),
            homepage="https://clarin-pl.eu/dspace/handle/11321/849",
        )

    def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
        urls_to_download = _URLS
        downloaded_files = dl_manager.download_and_extract(urls_to_download)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": downloaded_files["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": downloaded_files["validation"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": downloaded_files["test"]},
            ),
        ]

    def _generate_examples(
            self, filepath: str
    ) -> Generator[Tuple[int, Dict[str, str]], None, None]:
        with open(filepath, "r", encoding="utf-8") as f:
            reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            next(reader, None)  # skip header
            id_, orth, ctag, sentiment = set(), [], [], []
            for line in reader:
                if not line:
                    assert len(id_) == 1
                    yield id_.pop(), {"orth": orth, "ctag": ctag, "sentiment": sentiment, }
                    id_, orth, ctag, sentiment = set(), [], [], []
                else:
                    id_.add(line[0])
                    orth.append(line[1])
                    ctag.append(line[2])
                    sentiment.append(line[3])