import csv
import json
import os

import datasets

_DESCRIPTION = """\
FIQA translated dataset to portuguese
"""

_URLS = {
    "corpus": "https://huggingface.co/datasets/leonardo-avila/fiqa_pt/blob/main/corpus_pt.tsv",
    "topics": "https://huggingface.co/datasets/leonardo-avila/fiqa_pt/blob/main/topics_pt.tsv",
    "qrel": "https://huggingface.co/qrel.tsv",
}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class BeirPT(datasets.GeneratorBasedBuilder):
    """BEIR BenchmarkDataset."""

    VERSION = datasets.Version("1.1.0")

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="corpus", version=VERSION, description="Load corpus"),
        datasets.BuilderConfig(name="topics", version=VERSION, description="Load topics"),
        datasets.BuilderConfig(name="qrel", version=VERSION, description="Load qrel"),
    ]

    DEFAULT_CONFIG_NAME = "corpus"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):

        if self.config.name in ["corpus", "topics"]:
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                }
            )
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
            features = datasets.Features(
                {
                    "query_id": datasets.Value("string"),
                    "doc_id": datasets.Value("string"),
                    "rel": datasets.Value("string"),
                }
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features
        )

    def _split_generators(self, dl_manager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urls = _URLS[self.config.name]
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=self.config.name,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": data_dir},
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as f:
            if self.config.name in ["corpus", "topics"]:
                for line in f:
                    fields = line.strip().split("\t")
                    idx = fields[0]
                    text = fields[1]
                    yield idx, text
            else:
                for line in f:
                    if "query-id" not in line:
                        fields = line.strip().split("\t")
                            query_id = fields[0]
                            doc_id = fields[1]
                            rel = int(fields[2])
                            yield query_id, doc_id, rel