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_PESTYLE", "train.tsv"),
    "test_mask_subject": os.path.join("IK_NLP_22_PESTYLE", "test.tsv"),
    "test_mask_modality": os.path.join("IK_NLP_22_PESTYLE", "test.tsv"),
    "test_mask_time": os.path.join("IK_NLP_22_PESTYLE", "test.tsv")
}

_ALL_FIELDS = [
    "item_id", "subject_id", "modality",
    "src_text", "mt_text", "tgt_text",
    "edit_time", "k_total", "k_letter", "k_digit", "k_white", "k_symbol", "k_nav", "k_erase", 
    "k_copy", "k_cut", "k_paste", "n_pause_geq_300", "len_pause_geq_300", 
    "n_pause_geq_1000", "len_pause_geq_1000", "num_annotations",
    "n_insert", "n_delete", "n_substitute", "n_shift", "bleu", "chrf", "ter", "aligned_edit"
]

_FIELDS_MASK_SUBJECT = [f for f in _ALL_FIELDS if f not in ["subject_id"]]
_FIELDS_MASK_MODALITY = [f for f in _ALL_FIELDS if f not in [
    "modality", "mt_text", "n_insert", "n_delete", "n_substitute", 
    "n_shift", "ter", "bleu", "chrf", "aligned_edit"
]]
_FIELDS_MASK_TIME = [f for f in _ALL_FIELDS if f not in [
    "edit_time", "n_pause_geq_300", "len_pause_geq_300", 
    "n_pause_geq_1000", "len_pause_geq_1000"
]]

_DICT_FIELDS = {
    "train": _ALL_FIELDS,
    "test_mask_subject": _FIELDS_MASK_SUBJECT,
    "test_mask_modality": _FIELDS_MASK_MODALITY,
    "test_mask_time": _FIELDS_MASK_TIME
}

class IkNlp22PEStyleConfig(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 IkNlp22PEStyle(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        IkNlp22PEStyleConfig(
            name="main",
            features=_ALL_FIELDS,
        ),
    ]

    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_PESTYLE folder you were provided upon selecting the final project."
            "After extracting it, the folder (referred to as root) must contain a IK_NLP_22_PESTYLE subfolder, containing train.csv and test.csv files."
            "Then, load the dataset with: `datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', 'main', data_dir='path/to/root/folder')`"
        )

    def _info(self):
        features = {feature: datasets.Value("int32") for feature in self.config.features}
        features["subject_id"] = datasets.Value("string")
        features["modality"] = datasets.Value("string")
        features["src_text"] = datasets.Value("string")
        features["mt_text"] = datasets.Value("string")
        features["tgt_text"] = datasets.Value("string")
        features["aligned_edit"] = datasets.Value("string")
        features["edit_time"] = datasets.Value("float32")
        features["bleu"] = datasets.Value("float32")
        features["chrf"] = datasets.Value("float32")
        features["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_PESTYLE dir via "
                "`datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', data_dir=...)`"
                "Manual download instructions: {}".format(
                    data_dir, self.manual_download_instructions
                )
            )
        return [
            datasets.SplitGenerator(
                name=name,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, path),
                    "fields": _DICT_FIELDS[name],
                },
            )
            for name, path in _SPLITS.items()
        ]
    
    def _generate_examples(self, filepath: str, fields):
        """Yields examples as (key, example) tuples."""
        data = pd.read_csv(filepath)
        data = data[fields]
        print(data.shape)
        for id_, row in data.iterrows():
            yield id_, row.to_dict()