import datasets
import os
import numpy as np

class DBP1M_EN_FR_Config(datasets.BuilderConfig):

    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
        super(DBP1M_EN_FR_Config, self).__init__(version=datasets.Version("0.0.1"), **kwargs)
        self.features = features
        self.label_classes = label_classes
        self.data_url = data_url
        self.citation = citation
        self.url = url


class DBP1M_EN_FR_(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        DBP1M_EN_FR_Config(
            name="source",
            features=["column1", "column2", "column3"],
            citation="TODO",
            url="TODO",
            data_url="https://huggingface.co/datasets/matchbench/DBP1M-EN-FR/resolve/main/DBP1M-EN-FR-src.zip"
        ),
        DBP1M_EN_FR_Config(
            name="target",
            features=["column1", "column2", "column3"],
            citation="TODO",
            url="TODO",
            data_url="https://huggingface.co/datasets/matchbench/DBP1M-EN-FR/resolve/main/DBP1M-EN-FR-tgt.zip"
        ),
        DBP1M_EN_FR_Config(
            name="pairs",
            features=["left_id", "right_id"],
            citation="TODO",
            url="TODO",
            data_url="https://huggingface.co/datasets/matchbench/DBP1M-EN-FR/resolve/main/DBP1M-EN-FR-pairs.zip"
        ),
    ]

    def _info(self):
        if self.config.name in ["source", "target"]:
            features = {feature: datasets.Value("string") for feature in self.config.features}
        elif self.config.name=="pairs":
            features = {feature: datasets.Value("int32") for feature in self.config.features}
        return datasets.DatasetInfo(
            features=datasets.Features(features)
        )

    def _split_generators(self, dl_manager):
        dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
        #task_name = _get_task_name_from_data_url(self.config.data_url)
        #dl_dir = os.path.join(dl_dir, task_name)
        if self.config.name == "source":
            return [
            datasets.SplitGenerator(
                name="ent_ids",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "ent_ids_1"),
                    "split": "ent_ids",
                },
            ),
            datasets.SplitGenerator(
                name="rel_ids",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "rel_ids_1"),
                    "split": "rel_ids",
                },
            ),
            datasets.SplitGenerator(
                name="rel_triples",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "triples_1"),
                    "split": "rel_triples",
                },
            ),
            ]
        elif self.config.name == "target":
            return [
            datasets.SplitGenerator(
                name="ent_ids",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "ent_ids_2"),
                    "split": "ent_ids",
                },
            ),
            datasets.SplitGenerator(
                name="rel_ids",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "rel_ids_2"),
                    "split": "rel_ids",
                },
            ),
            datasets.SplitGenerator(
                name="rel_triples",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "triples_2"),
                    "split": "rel_triples",
                },
            ),
            ]
        elif self.config.name == "pairs":
            return [
            datasets.SplitGenerator(
                name="train",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "ill_ent_ids_train"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name="valid",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "ill_ent_ids"),
                    "split": "valid",
                },
            ),
            datasets.SplitGenerator(
                name="test",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "ill_ent_ids"),
                    "split": "test",
                },
            ),
            ]


    def _generate_examples(self, data_file, split):
        f = open(data_file,"r",encoding='utf-8')
        data = f.readlines()
        for i in range(len(data)):
            if self.config.name in ["source", "target"]:
                if split in ["ent_ids","rel_ids"]:
                    row = data[i].strip('\n').split('\t')
                    yield i, {
                                "column1": row[0],
                                "column2": row[1],
                                "column3": None
                            }
                elif split in ["rel_triples"]:
                    row = data[i].strip('\n').split('\t')
                    yield i, {
                                "column1": row[0],
                                "column2": row[1],
                                "column3": row[2]
                            }
            if self.config.name == "pairs":
                row = data[i].strip('\n').split('\t')
                yield i, {
                            "left_id": row[0],
                            "right_id": row[1]
                }