"""Comments from Jigsaw Toxic Comment Classification Kaggle Competition """

import json

import pandas as pd

import datasets


_DESCRIPTION = """\
This dataset consists of a large number of Wikipedia comments translated to Finnish which have been labeled by human raters for toxic behavior.
"""

_HOMEPAGE = "https://turkunlp.org/"

_URLS = {
    "train": "https://huggingface.co/datasets/TurkuNLP/wikipedia-toxicity-data-fi/resolve/main/train_fi_deepl.jsonl.bz2",
    "test": "https://huggingface.co/datasets/TurkuNLP/wikipedia-toxicity-data-fi/resolve/main/test_fi_deepl.jsonl.bz2"
}


class JigsawToxicityPred(datasets.GeneratorBasedBuilder):
    """This is a dataset of comments from Wikipedia’s talk page edits which have been labeled by human raters for toxic behavior."""

    VERSION = datasets.Version("1.1.0")


    def _info(self):

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label_toxicity": datasets.ClassLabel(names=["false", "true"]),
                    "label_severe_toxicity": datasets.ClassLabel(names=["false", "true"]),
                    "label_obscene": datasets.ClassLabel(names=["false", "true"]),
                    "label_threat": datasets.ClassLabel(names=["false", "true"]),
                    "label_insult": datasets.ClassLabel(names=["false", "true"]),
                    "label_identity_attack": datasets.ClassLabel(names=["false", "true"]),
                }
            ),
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        urls_to_download = _URLS
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": downloaded_files["train"]}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files["test"],
                },
            ),
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        # This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
        # The key is not important, it's more here for legacy reason (legacy from tfds)

      # read the json into dictionaries
        with open(filepath, 'r') as json_file:
            json_list = list(json_file)
            lines = [json.loads(jline) for jline in json_list]
        
        for data in lines:
            example = {}
            example["text"] = data["text"]
            
            for label in ["label_toxicity", "label_severe_toxicity", "label_obscene", "label_threat", "label_insult", "label_identity_attack"]:
                example[label] = int(data[label])

            yield (data["id"], example)