import csv
import json
import os
import random

import datasets
import pandas as pd
from torchvision.io import read_video

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@misc{black2023vader,
      title={VADER: Video Alignment Differencing and Retrieval}, 
      author={Alexander Black and Simon Jenni and Tu Bui and Md. Mehrab Tanjim and Stefano Petrangeli and Ritwik Sinha and Viswanathan Swaminathan and John Collomosse},
      year={2023},
      eprint={2303.13193},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
ANAKIN is a dataset of mANipulated videos and mAsK annotatIoNs.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/AlexBlck/vader"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "cc-by-4.0"

# 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 = {
    "all": "https://huggingface.co/datasets/AlexBlck/ANAKIN/raw/main/metadata.csv",
}


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

    VERSION = datasets.Version("1.0.0")

    # 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="all",
            version=VERSION,
            description="Full video, trimmed video, edited video, masks (if exists), and edit description",
        ),
    ]

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

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "all":
            features = datasets.Features(
                {
                    # "full": datasets.Value("string"),
                    "trimmed": datasets.Value("string"),
                    "edited": datasets.Value("string"),
                    # "masks": datasets.Value("string"),
                    # "edit_description": datasets.Value("string"),
                }
            )
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
            features = datasets.Features(
                {
                    "sentence": datasets.Value("string"),
                    # These are the features of your dataset like images, labels ...
                }
            )
        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
            # 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):
        # 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
        urls = _URLS[self.config.name]
        metadata_dir = dl_manager.download_and_extract(urls)

        random.seed(47)
        root_url = "https://huggingface.co/datasets/AlexBlck/ANAKIN/resolve/main/"
        df = pd.read_csv(metadata_dir)
        ids = df["video-id"].to_list()
        random.shuffle(ids)

        data_urls = [
            {
                # "full": root_url + f"full/{idx}.mp4",
                "trimmed": root_url + f"trimmed/{idx}.mp4",
                "edited": root_url + f"edited/{idx}.mp4",
                # "masks": root_url + f"masks/{idx}/",
            }
            for idx in ids[:10]
        ]
        data_dir = dl_manager.download(data_urls)
        # data_dir = dl_manager.iter_files(data_dir)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "files": data_dir,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "files": data_dir,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "files": os.path.join(data_dir, "metadata.csv"),
                },
            ),
        ]

    def _generate_examples(self, files):
        for key, sample in enumerate(files):
            print(sample)
            yield key, sample