import datasets
import pandas as pd

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

_DESCRIPTION = """\
4000 people in this dataset. Each person took a selfie on a webcam,
took a selfie on a mobile phone. In addition, people recorded video from
the phone and from the webcam, on which they pronounced a given set of numbers.
Includes folders corresponding to people in the dataset. Each folder includes
8 files (4 images and 4 videos).
"""
_NAME = 'selfie_and_video'

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

_LICENSE = ""

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


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

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                'photo_1': datasets.Image(),
                'photo_2': datasets.Image(),
                'video_3': datasets.Value('string'),
                'video_4': datasets.Value('string'),
                'photo_5': datasets.Image(),
                'photo_6': datasets.Image(),
                'video_7': datasets.Value('string'),
                'video_8': datasets.Value('string'),
                'set_id': datasets.Value('string'),
                'worker_id': 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}data.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=['Link', 'Bytes'])
        for idx, (image_path, image) in enumerate(images):
            if image_path.lower().endswith('.jpg'):
                images_data.loc[idx] = {
                    'Link': image_path,
                    'Bytes': image.read()
                }

        annotations_df = pd.merge(annotations_df,
                                  images_data,
                                  on=['Link'],
                                  how='left')
        
        for idx, worker_id in enumerate(pd.unique(annotations_df['WorkerId'])):
            annotation = annotations_df.loc[annotations_df['WorkerId'] ==
                                            worker_id]
            annotation = annotation.sort_values(['Link'])
            data = {
                (f'photo_{row[7][37]}' if row[7].lower().endswith('.jpg') else f'video_{row[7][37]}'):
                    ({
                        'path': row[7],
                        'bytes': row[8]
                    } if row[7].lower().endswith('.jpg') else row[7])
                for row in annotation.itertuples()
            }

            age = annotation.loc[annotation['Link'].str.lower().str.endswith(
                '1.jpg')]['Age'].values[0]
            country = annotation.loc[annotation['Link'].str.lower().str.
                                     endswith('1.jpg')]['Country'].values[0]
            gender = annotation.loc[annotation['Link'].str.lower().str.
                                    endswith('1.jpg')]['Gender'].values[0]
            set_id = annotation.loc[annotation['Link'].str.lower().str.
                                    endswith('1.jpg')]['SetId'].values[0]

            data['worker_id'] = worker_id
            data['age'] = age
            data['country'] = country
            data['gender'] = gender
            data['set_id'] = set_id

            yield idx, data