# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3

import json

import datasets
from dataclasses import dataclass

_CITATION = '''
@article{mrtydi,
      title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, 
      author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
      year={2021},
      journal={arXiv:2108.08787},
}
'''

_DESCRIPTION = 'dataset load script for Mr. TyDi'

_DATASET_URLS = {
    'train': f'https://huggingface.co/datasets/castorini/mr-tydi-corpus/resolve/main/mrtydi-v1.1-english/corpus.jsonl.gz',
}


class XorTyDiCorpus(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            version=datasets.Version('1.1.0'),
            description=f'Same with English Mr TyDi dataset.'
        ),
    ]

    def _info(self):
        features = datasets.Features({
            'docid': datasets.Value('string'), 
            'title': 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='https://github.com/Tevatron/xor-tydi-corpus',
            # 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='train',
                gen_kwargs={
                    'filepath': downloaded_files['train'],
                },
            ),
        ]
        return splits

    def _generate_examples(self, filepath):
        with open(filepath, encoding="utf-8") as f:
            for line in f:
                data = json.loads(line)
                yield data['docid'], data