# coding=utf-8

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

import json

import datasets

_CITATION = """
@misc{bajaj2018ms,
      title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, 
      author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu 
      and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song
       and Alina Stoica and Saurabh Tiwary and Tong Wang},
      year={2018},
      eprint={1611.09268},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = "MSMARCO Passage Ranking datas"

_DATASET_URLS = {
        'corpus': "https://huggingface.co/datasets/zyznull/msmarco-passage-corpus/resolve/main/collection.tsv.gz",
        'train_query': "https://huggingface.co/datasets/zyznull/msmarco-passage-corpus/resolve/main/train_queries.tsv.gz",
        'dev_query': "https://huggingface.co/datasets/zyznull/msmarco-passage-corpus/resolve/main/dev_queries.tsv.gz",
}


class MsMarcoPassage(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