import os

import datasets
import pandas as pd

_CITATION = """No citation information available."""

_DESCRIPTION = """\
This dataset contains a sample of sentences taken from the FLORES-101 dataset that were either translated 
from scratch or post-edited from an existing automatic translation by three human translators. 
Translation were performed for the English-Italian language pair, and translators' behavioral data 
(keystrokes, pauses, editing times) were collected using the PET platform.
"""

_HOMEPAGE = "https://www.rug.nl/masters/information-science/?lang=en"

_LICENSE = "Sharing and publishing of the data is not allowed at the moment."

_SPLITS = {
    "train": os.path.join("IK_NLP_22_HTSTYLE", "train.csv"),
    "test": os.path.join("IK_NLP_22_HTSTYLE", "test.csv")
}


class IkNlp22HtStyleConfig(datasets.BuilderConfig):
    """BuilderConfig for the IK NLP '22 HT-Style Dataset."""

    def __init__(
        self,
        features,
        **kwargs,
    ):
        """
        Args:
        features: `list[string]`, list of the features that will appear in the
            feature dict. Should not include "label".
        **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.features = features


class IkNlp22HtStyle(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        IkNlp22HtStyleConfig(
            name="main",
            features=[
                "item",
                "subject",
                "tasktype",
                "sl_text",
                "mt_text",
                "tl_text",
                "len_sl_chr",
                "len_tl_chr",
                "len_sl_wrd",
                "len_tl_wrd",
                "edit_time",
                "k_total",
                "k_letter",
                "k_digit",
                "k_white",
                "k_symbol",
                "k_nav",
                "k_erase",
                "k_copy",
                "k_cut",
                "k_paste",
                "np_300",
                "lp_300",
                "np_1000",
                "lp_1000",
                "mt_tl_bleu",
                "mt_tl_chrf",
                "mt_tl_ter",
            ],
        ),
    ]

    DEFAULT_CONFIG_NAME = "main"

    @property

    def manual_download_instructions(self):
        return (
            "The access to the data is restricted to students of the IK MSc NLP 2022 course working on a related project."
            "To load the data using this dataset, download and extract the IK_NLP_22_HTSTYLE folder you were provided upon selecting the final project."
            "After extracting it, the folder (referred to as root) must contain a IK_NLP_22_HTSTYLE subfolder, containing train.csv and test.csv files."
            "Then, load the dataset with: `datasets.load_dataset('GroNLP/ik-nlp-22_htstyle', 'main', data_dir='path/to/root/folder')`"
        )

    def _info(self):
        features = {feature: datasets.Value("int32") for feature in self.config.features}
        features["subject"] = datasets.Value("string")
        features["tasktype"] = datasets.Value("string")
        features["sl_text"] = datasets.Value("string")
        features["mt_text"] = datasets.Value("string")
        features["tl_text"] = datasets.Value("string")
        features["edit_time"] = datasets.Value("float32")
        features["mt_tl_bleu"] = datasets.Value("float32")
        features["mt_tl_chrf"] = datasets.Value("float32")
        features["mt_tl_ter"] = datasets.Value("float32")
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
        if not os.path.exists(data_dir):
            raise FileNotFoundError(
                "{} does not exist. Make sure you insert the unzipped IK_NLP_22_HTSTYLE dir via "
                "`datasets.load_dataset('GroNLP/ik-nlp-22_htstyle', data_dir=...)`"
                "Manual download instructions: {}".format(
                    data_dir, self.manual_download_instructions
                )
            )
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, _SPLITS["train"]),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, _SPLITS["test"]),
                },
            ),
        ]
    
    def _generate_examples(self, filepath: str):
        """Yields examples as (key, example) tuples."""
        data = pd.read_csv(filepath)
        print(data.shape)
        for id_, row in data.iterrows():
            yield id_, row.to_dict()