# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import json
import os
from typing import Dict, List
from PIL import Image

import datasets
from datasets import DownloadManager

# 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 = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    # "first_domain": "https://huggingface.co./great-new-dataset-first_domain.zip",
    # "second_domain": "https://huggingface.co./great-new-dataset-second_domain.zip",
}

_BASE_URL = ""


def get_download_url(config_name: str, partition: str) -> str:
    """Get download URL based on config name and parition (train/dev/test)

    Args:
        config_name (str): can be "v1", "v2",...
        partition (str): can be "train", "dev" or "test"

    Returns:
        str: URL to download file
    """
    return f"https://huggingface.co./datasets/RGBD-SOD/rgbdsod_datasets/resolve/main/data/{config_name}/{partition}.zip"


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class Test(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="v1",
            version=VERSION,
            description="RGB-D SOD Set 1",
        ),
        # datasets.BuilderConfig(
        #     name="v2",
        #     version=VERSION,
        #     description="RGB-D SOD Set 2",
        # ),
    ]

    DEFAULT_CONFIG_NAME = "v1"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        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=datasets.Features(
                {
                    "depth": datasets.Image(),
                    "rgb": datasets.Image(),
                    "gt": datasets.Image(),
                    "name": datasets.Value("string"),
                    # These are the features of your dataset like images, labels ...
                }
            ),  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: DownloadManager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        train_dir = dl_manager.download_and_extract(
            get_download_url(self.config.name, "train")
        )
        dev_dir = dl_manager.download_and_extract(
            get_download_url(self.config.name, "dev")
        )
        # test_dir = dl_manager.download_and_extract(
        #     get_download_url(self.config.name, "test")
        # )
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"dir_path": train_dir},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"dir_path": dev_dir},
            ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.TEST,
            #     # These kwargs will be passed to _generate_examples
            #     gen_kwargs={"dir_path": test_dir},
            # ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, dir_path: str):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(os.path.join(dir_path, "metadata.json"), "r") as f:
            json_object = json.load(f)

        metadata: List[Dict[str, str]] = json_object["metadata"]

        for key, row in enumerate(metadata):
            yield key, {
                "name": row["name"],
                "rgb": Image.open(os.path.join(dir_path, row["rgb"])).convert("RGB"),
                "gt": Image.open(os.path.join(dir_path, row["gt"])).convert("L"),
                "depth": Image.open(os.path.join(dir_path, row["depth"])).convert("L"),
            }