# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
"""TODO: Add a description here."""

import json
import os

import PIL.Image
import datasets
import numpy as np

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

_URLS = {
    "8x8": [
        # Download the original images from the original repo
        "https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset/resolve/main/images.tar.gz?download=true",
        # Maks and metadata from the current
        "https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/annotations/{split}_annotations/mask.tar.gz?download=true",
        "https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/{split}.jsonl?download=true"
    ]
}


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

    BUILDER_CONFIGS = [
        # datasets.BuilderConfig(name="1x1", version=VERSION, description="Patchy Imagenet with 1x1 resolution (this is the original resolution)"),
        datasets.BuilderConfig(name="8x8", version=VERSION, description="Patchy Imagenet with 8x8 resolution"),
        # datasets.BuilderConfig(name="16x16", version=VERSION, description="Patchy Imagenet with 16x16 resolution"),
        # datasets.BuilderConfig(name="32x32", version=VERSION, description="Patchy Imagenet with 32x32 resolution"),
        # datasets.BuilderConfig(name="64x64", version=VERSION, description="Patchy Imagenet with 64x64 resolution"),
    ]
    DEFAULT_CONFIG_NAME = "8x8"

    def _info(self):
        features = datasets.Features(
            {
                "image": datasets.Image(),
                "patches": datasets.Features(
                    {
                        # This would be best but there are too many classes
                        # "categories": datasets.Sequence(datasets.ClassLabel(names=_IMAGENET_CLASSES)),
                        "categories": datasets.Sequence(datasets.Value("string")),
                        "scores": datasets.Sequence(datasets.Value("float32")),
                        "mask": datasets.Sequence(
                            datasets.Array2D(shape=(224 // 8, 224 // 8), dtype="bool")
                        ),
                        # Array2D is a bit annoying to use, otherwise use this
                        # "mask": datasets.Sequence(datasets.Image()),
                    }
                ),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        url_templates = _URLS[self.config.name]

        split_kwargs = {}
        for split in ["train", "test", "val"]:
            urls = [url.format(split=split) for url in url_templates]
            image_dir, mask_dir, metadata_file = dl_manager.download_and_extract(urls)
            split_kwargs[split] = {
                "meta_path": metadata_file,
                "image_dir": image_dir, "mask_dir": mask_dir,
                "split": split
            }

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=split_kwargs["train"]),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=split_kwargs["val"]),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=split_kwargs["test"]),
        ]

    def _generate_examples(self, meta_path, image_dir, mask_dir, split):
        with open(meta_path, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                image_path = os.path.join(image_dir, "images", f"{split}_images", data["file_name"])
                sample_name, _extension = os.path.splitext(data["file_name"])
                mask_file = os.path.join(mask_dir, "masks", sample_name + ".npy")
                mask = np.load(mask_file).astype(bool)
                # mask = np.load(mask_file).astype(np.uint8)
                yield key, {
                    "image": PIL.Image.open(image_path),
                    "patches": {
                        "categories": data["patches"]["categories"],
                        "scores": data["patches"]["scores"],
                        "mask": mask,
                    }
                }