# coding: utf-8

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"""Test auto data set"""
import os
import tempfile
import pandas as pd
from gluoncv.auto.data.dataset import ImageClassificationDataset
from gluoncv.auto.data.dataset import ObjectDetectionDataset


def test_image_datasets_from_csv():
    N = 10
    def gen_fake_data(dir_name):
        paths = [f'{i}.jpg' for i in range(N)]
        labels = [i for i in range(N)]
        df = pd.DataFrame({'image': paths, 'label': labels})
        df.to_csv(os.path.join(dir_name, 'normal.csv'), index=False)
        df1 = pd.DataFrame({'some_image': paths, 'some_label': labels})
        df1.to_csv(os.path.join(dir_name, 'abnormal.csv'), index=False)

    with tempfile.TemporaryDirectory() as tmpdirname:
        gen_fake_data(tmpdirname)
        normal_csv = os.path.join(tmpdirname, 'normal.csv')
        abnormal_csv = os.path.join(tmpdirname, 'abnormal.csv')
        d1 = ImageClassificationDataset.from_csv(normal_csv, root=tmpdirname)
        assert 'image' in d1.columns
        assert 'label' in d1.columns
        d1 = ImageClassificationDataset(normal_csv)
        d2 = ImageClassificationDataset(abnormal_csv, image_column='some_image', label_column='some_label')
        d2 = ImageClassificationDataset.from_csv(abnormal_csv, image_column='some_image', label_column='some_label')

def test_object_detection_data():
    df = ObjectDetectionDataset.from_voc('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip')
    if df.is_packed():
        df2 = df.unpack().pack()
    else:
        df2 = df.pack().unpack()
    assert df2.equals(df), 'pack unpack inconsistent'

if __name__ == '__main__':
    import nose
    nose.runmodule()
