# coding=utf-8

# Lint as: python3
"""Passage Ranking fintune dataset."""

import json

import datasets

_CITATION = """
@article{Qiu2022DuReader\_retrievalAL,
  title={DuReader\_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine},
  author={Yifu Qiu and Hongyu Li and Yingqi Qu and Ying Chen and Qiaoqiao She and Jing Liu and Hua Wu and Haifeng Wang},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.10232}
}
"""

_DESCRIPTION = "DuReader-retrieval datas"

_DATASET_URLS = {
        'corpus': "https://huggingface.co/datasets/zyznull/dureader-retrieval-corpus/resolve/main/passage_collection.tsv.gz",
        'train_query': "https://huggingface.co/datasets/zyznull/dureader-retrieval-corpus/resolve/main/queries.train.tsv.gz",
        'dev_query': "https://huggingface.co/datasets/zyznull/dureader-retrieval-corpus/resolve/main/queries.dev.tsv.gz",
}


class Dureader(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("0.0.1")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(version=VERSION,
                               description="MS MARCO passage corpus"),
    ]

    def _info(self):
        features = datasets.Features({
            '_id': datasets.Value('string'),
            'text': datasets.Value('string'),
        })
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage="",
            # License for the dataset if available
            license="",
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
        splits = [
            datasets.SplitGenerator(
                name=split,
                gen_kwargs={
                    "files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split],
                },
            ) for split in downloaded_files
        ]
        return splits
        
    def _generate_examples(self, files):
        """Yields examples."""
        for filepath in files:
            with open(filepath, encoding="utf-8") as f:
                for i, line in enumerate(f):
                    line = line.strip().split('\t')
                    item = {'_id': line[0], 'text': line[1]}
                    yield i, item