# 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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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""SBU Captioned Photo Dataset"""

import json

import datasets


_CITATION = """\
@inproceedings{NIPS2011_5dd9db5e,
 author = {Ordonez, Vicente and Kulkarni, Girish and Berg, Tamara},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Im2Text: Describing Images Using 1 Million Captioned Photographs},
 url = {https://proceedings.neurips.cc/paper/2011/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf},
 volume = {24},
 year = {2011}
}
"""

_DESCRIPTION = """\
The SBU Captioned Photo Dataset is a collection of over 1 million images with associated text descriptions extracted from Flicker.
"""

_LICENSE = "unknown"

_HOMEPAGE = "https://www.cs.rice.edu/~vo9/sbucaptions/"

_URL = "https://www.cs.rice.edu/~vo9/sbucaptions/sbu-captions-all.tar.gz"

_FEATURES = datasets.Features(
    {"image_url": datasets.Value("string"), "user_id": datasets.Value("string"), "caption": datasets.Value("string")}
)

_MAP_SBU_FEATURES_TO_DATASETS_FEATURES = {"image_urls": "image_url", "user_ids": "user_id", "captions": "caption"}


class SBUCaptionedPhotoDatasetConfig(datasets.BuilderConfig):
    """BuilderConfig for SBU Captioned Photo dataset."""

    VERSION = datasets.Version("0.0.0")

    def __init__(self, version=None, *args, **kwargs):
        super().__init__(
            version=version or self.VERSION,
            *args,
            **kwargs,
        )


class SBUCaptionedPhotoDataset(datasets.GeneratorBasedBuilder):
    """SBU Captioned Photo dataset."""

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

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        archive = dl_manager.download(_URL)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "files": dl_manager.iter_archive(archive),
                },
            )
        ]

    def _generate_examples(self, files):
        annotations = None
        for path, f in files:
            if path.endswith("sbu-captions-all.json"):
                annotations = json.loads(f.read().decode("utf-8"))
                break

        # Sanity checks
        assert annotations is not None
        nb_samples = len(annotations[next(iter(annotations.keys()))])
        assert all(len(values) == nb_samples for values in annotations.values())
        keys = tuple(annotations.keys())

        for idx in range(nb_samples):
            yield idx, {_MAP_SBU_FEATURES_TO_DATASETS_FEATURES[key]: annotations[key][idx] for key in keys}