import io

import datasets
import pandas as pd

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

_DESCRIPTION = """\
4083 sets, which includes 2 photos of a person from his documents and
13 selfies. 571 sets of Hispanics and 3512 sets of Caucasians.
Photo documents contains only a photo of a person.
All personal information from the document is hidden.
"""
_NAME = 'selfies_and_id'

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

_LICENSE = ""

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


class SelfiesAndId(datasets.GeneratorBasedBuilder):
    """Small sample of image-text pairs"""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                'id_1': datasets.Image(),
                'id_2': datasets.Image(),
                'selfie_1': datasets.Image(),
                'selfie_2': datasets.Image(),
                'selfie_3': datasets.Image(),
                'selfie_4': datasets.Image(),
                'selfie_5': datasets.Image(),
                'selfie_6': datasets.Image(),
                'selfie_7': datasets.Image(),
                'selfie_8': datasets.Image(),
                'selfie_9': datasets.Image(),
                'selfie_10': datasets.Image(),
                'selfie_11': datasets.Image(),
                'selfie_12': datasets.Image(),
                'selfie_13': datasets.Image(),
                'user_id': datasets.Value('string'),
                'set_id': datasets.Value('string'),
                'user_race': datasets.Value('string'),
                'name': datasets.Value('string'),
                'age': datasets.Value('int8'),
                'country': datasets.Value('string'),
                'gender': datasets.Value('string')
            }),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

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

    def _generate_examples(self, images, annotations):
        annotations_df = pd.read_csv(annotations, sep=';')
        images_data = pd.DataFrame(columns=['URL', 'Bytes'])
        for idx, (image_path, image) in enumerate(images):
            images_data.loc[idx] = {'URL': image_path, 'Bytes': image.read()}

        annotations_df = pd.merge(annotations_df,
                                  images_data,
                                  how='left',
                                  on=['URL'])
        for idx, worker_id in enumerate(pd.unique(annotations_df['UserId'])):
            annotation = annotations_df.loc[annotations_df['UserId'] ==
                                            worker_id]
            annotation = annotation.sort_values(['FName'])
            data = {
                row[5].lower(): {
                    'path': row[6],
                    'bytes': row[10]
                } for row in annotation.itertuples()
            }

            age = annotation.loc[annotation['FName'] ==
                                 'ID_1']['Age'].values[0]
            country = annotation.loc[annotation['FName'] ==
                                     'ID_1']['Country'].values[0]
            gender = annotation.loc[annotation['FName'] ==
                                    'ID_1']['Gender'].values[0]
            set_id = annotation.loc[annotation['FName'] ==
                                    'ID_1']['SetId'].values[0]
            user_race = annotation.loc[annotation['FName'] ==
                                       'ID_1']['UserRace'].values[0]
            name = annotation.loc[annotation['FName'] ==
                                  'ID_1']['Name'].values[0]

            data['user_id'] = worker_id
            data['age'] = age
            data['country'] = country
            data['gender'] = gender
            data['set_id'] = set_id
            data['user_race'] = user_race
            data['name'] = name

            yield idx, data