import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = ""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This is an ancient mortars dataset.
"""

# 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 = "https://huggingface.co/apetulante/mortars_test/mortars_data.zip"

_METADATA_URLS = {
    "train": "https://huggingface.co/apetulante/mortars_test/train.txt",
    "valid": "https://huggingface.co/apetulante/mortars_test/valid.txt",
    "test": "https://huggingface.co/apetulante/mortars_test/test.txt",
}

_NAMES = {
"Chert",
"Obsidian",
"Sand"
}


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class MortarsData(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')

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.ClassLabel(names=_NAMES)
                    # 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):
        archive_path = dl_manager.download_and_extract(_BASE_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path)
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path)
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path)
                },
            ),
        ]
        
    def _generate_examples(self, images, metadata_path):
    """Generate images and labels for splits."""
        with open(metadata_path, encoding="utf-8") as f:
            files_to_keep = set(f.read().split("\n"))
        for file_path, file_obj in images:
            if file_path.startswith(_IMAGES_DIR):
                if file_path[len(_IMAGES_DIR) : -len(".bmp")] in files_to_keep:
                    label = file_path.split("/")[-2].split('-')[0]
                    yield file_path, {
                        "image": {"path": file_path, "bytes": file_obj.read()},
                        "label": label,
                    }