import datasets
import pandas as pd

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {license_plates},
author = {TrainingDataPro},
year = {2023}
}
"""

_DESCRIPTION = """\
Over 1.2 million annotated license plates from vehicles around the world.
This dataset is tailored for License Plate Recognition tasks and includes
images from both YouTube and PlatesMania. 
Annotation details are provided in the About section below. 
"""
_NAME = 'license_plates'

_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"

_LICENSE = ""

_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"


class LicensePlates(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="Brazil_youtube"),
        datasets.BuilderConfig(name="Estonia_platesmania"),
        datasets.BuilderConfig(name="Finland_platesmania"),
        datasets.BuilderConfig(name="Kazakhstan_platesmania"),
        datasets.BuilderConfig(name="Kazakhstan_youtube"),
        datasets.BuilderConfig(name="Lithuania_platesmania"),
        datasets.BuilderConfig(name="Serbia_platesmania"),
        datasets.BuilderConfig(name="Serbia_youtube"),
        datasets.BuilderConfig(name="UAE_platesmania"),
        datasets.BuilderConfig(name="UAE_youtube")
    ]

    DEFAULT_CONFIG_NAME = "Brazil"

    def _info(self):

        features = datasets.Features({
            'bbox_id': datasets.Value('uint32'),
            'bbox': datasets.Value('string'),
            'image': datasets.Image(),
            'labeled_image': datasets.Image(),
            'license_plate.id': datasets.Value('string'),
            'license_plate.visibility': datasets.Value('string'),
            'license_plate.rows_count': datasets.Value('uint8'),
            'license_plate.number': datasets.Value('string'),
            'license_plate.serial': datasets.Value('string'),
            'license_plate.country': datasets.Value('string'),
            'license_plate.mask': datasets.Value('string')
        })

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data = dl_manager.download(f"{_DATA}{self.config.name}.tar.gz")
        data = dl_manager.iter_archive(data)
        annotations = dl_manager.download(f'{_DATA}{self.config.name}.csv')
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                    gen_kwargs={
                                        "data": data,
                                        'annotations': annotations
                                    }),
        ]

    def _generate_examples(self, data, annotations):

        annotations_df = pd.read_csv(annotations, sep=',', index_col=0)
        images = {}

        for idx, (file_path, file) in enumerate(data):
            file_name = file_path.split('/')[-1]
            images[file_name] = (file_path, file.read())

        annotations_df.drop(
            columns=['license_plate.region', 'license_plate.color'],
            inplace=True,
            errors='ignore')

        annotations_df.fillna(0, inplace=True)
        annotations_df.sort_values(by='file_name', inplace=True)

        for row in annotations_df.itertuples(index=True):
            image = images[row[1]]
            name, ext = row[1].split('.')
            labeled_image = images[f'{name}_labeled.{ext}']

            yield idx, {
                'bbox_id': row[0],
                'bbox': row[2],
                "image": {
                    "path": image[0],
                    "bytes": image[1]
                },
                "labeled_image": {
                    "path": labeled_image[0],
                    "bytes": labeled_image[1]
                },
                'license_plate.id': row[3],
                'license_plate.visibility': row[4],
                'license_plate.rows_count': row[5],
                'license_plate.number': row[6],
                'license_plate.serial': row[7],
                'license_plate.country': row[8],
                'license_plate.mask': row[9]
            }