import datasets
import os

class EntityAlignmentConfig(datasets.BuilderConfig):
    """BuilderConfig for SuperGLUE."""

    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
        """BuilderConfig for SuperGLUE.
        Args:
          features: `list[string]`, list of the features that will appear in the
            feature dict. Should not include "label".
          data_url: `string`, url to download the zip file from.
          citation: `string`, citation for the data set.
          url: `string`, url for information about the data set.
          label_classes: `list[string]`, the list of classes for the label if the
            label is present as a string. Non-string labels will be cast to either
            'False' or 'True'.
          **kwargs: keyword arguments forwarded to super.
        """
        # Version history:
        # 1.0.3: Fix not including entity position in ReCoRD.
        # 1.0.2: Fixed non-nondeterminism in ReCoRD.
        # 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to
        #        the full release (v2.0).
        # 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
        # 0.0.2: Initial version.
        super(EntityAlignmentConfig, self).__init__(version=datasets.Version("1.0.3"), **kwargs)
        self.features = features
        self.label_classes = label_classes
        self.data_url = data_url
        self.citation = citation
        self.url = url


class EntityAlignment(datasets.GeneratorBasedBuilder):
    """The SuperGLUE benchmark."""

    BUILDER_CONFIGS = [
        EntityAlignmentConfig(
            name="source",
            features=["column1", "column2", "column3"],
            data_url="https://www.dropbox.com/s/j55ly9i7w7t4tnn/dbp15k-fr-en-src.zip"
        ),
        EntityAlignmentConfig(
            name="target",
            features=["column1", "column2", "column3"],
            data_url="https://www.dropbox.com/s/eo2huntzhfti1p1/dbp15k-fr-en-tgt.zip"
        ),
        EntityAlignmentConfig(
            name="pairs",
            features=["left_id", "right_id"],
            data_url="https://www.dropbox.com/s/5lhkfka1imuum1o/dbp15k-fr-en-pairs.zip"
        ),
    ]

    def _info(self):
        features = {feature: datasets.Value("string") 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="attr_triples",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "attr_triples_1"),
                    "split": "attr_triples",
                },
            ),
            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="attr_triples",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "attr_triples_2"),
                    "split": "attr_triples",
                },
            ),
            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, "sup_pairs"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name="test",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "ref_pairs"),
                    "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)):
            row = data[i].strip('\n').split('\t')
            if self.config.name in ["source", "target"]:
                if split in ["ent_ids","rel_ids"]:
                    yield i, {
                                "column1": row[0],
                                "column2": row[1],
                                "column3": None
                            }
                else:
                    yield i, {
                                "column1": row[0],
                                "column2": row[1],
                                "column3": row[2]
                            }
            if self.config.name == "pairs":
                yield i, {
                            "left_id": row[0],
                            "right_id": row[1]
                }