import datasets
import os

class Dbp15kFrEnConfig(datasets.BuilderConfig):

    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(Dbp15kFrEnConfig, 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 Dbp15kFrEn(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        Dbp15kFrEnConfig(
            name="source",
            features=["column1", "column2", "column3"],
            citation="TODO",
            url="TODO",
            data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-src.zip"
        ),
        Dbp15kFrEnConfig(
            name="target",
            features=["column1", "column2", "column3"],
            citation="TODO",
            url="TODO",
            data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-tgt.zip"
        ),
        Dbp15kFrEnConfig(
            name="pairs",
            features=["left_id", "right_id"],
            citation="TODO",
            url="TODO",
            data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-pairs.zip"
        ),
    ]

    def _info(self):
        if self.config.name=="source":
            features = {feature: datasets.Value("string") for feature in self.config.features}
        elif self.config.name=="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="attr_triples",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "att_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, "att_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)):
            #print(row)
            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]
                            }
                else:
                    row = data[i].strip('\n').split(' ')
                    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]
                }