# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset"""
import csv

import datasets


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{srinivasan2021wit,
  title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
  author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
  journal={arXiv preprint arXiv:2103.01913},
  year={2021}
}
"""

# You can copy an official description
_DESCRIPTION = """\
Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.
Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
"""

_HOMEPAGE = "https://github.com/google-research-datasets/wit"

_LICENSE = "Data is available under the Creative Commons Attribution-ShareAlike 3.0 Unported license."

_URLs = [f"https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-{i:05}-of-00010.tsv.gz" for i in range(0, 10)]

_FEATURES = datasets.Features(
    {
        "language": datasets.Value("string"),
        "page_url": datasets.Value("string"),
        "image_url": datasets.Value("string"),
        "page_title": datasets.Value("string"),
        "section_title": datasets.Value("string"),
        "hierarchical_section_title": datasets.Value("string"),
        "caption_reference_description": datasets.Value("string"),
        "caption_attribution_description": datasets.Value("string"),
        "caption_alt_text_description": datasets.Value("string"),
        "mime_type": datasets.Value("string"),
        "original_height": datasets.Value("int32"),
        "original_width": datasets.Value("int32"),
        "is_main_image": datasets.Value("bool"),
        "attribution_passes_lang_id": datasets.Value("bool"),
        "page_changed_recently": datasets.Value("bool"),
        "context_page_description": datasets.Value("string"),
        "context_section_description": datasets.Value("string"),
    }
)


class WIT(datasets.GeneratorBasedBuilder):
    """Builder for WIT."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=_FEATURES,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        files = dl_manager.download_and_extract(_URLs)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "files": files,
                },
            ),
        ]

    def _generate_examples(self, files):
        idx = 0
        for file in files:
            with open(file, "r", encoding="utf-8") as f:
                examples = csv.DictReader(f, delimiter="\t")
                for example in examples:
                    yield idx, {k: v if v != "" else None for k, v in example.items()}
                    idx += 1