# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest

import numpy as np
from mmdet.structures import DetDataSample
from mmdet.structures.mask import BitmapMasks
from mmengine.structures import InstanceData, PixelData

from mmyolo.datasets.transforms import PackDetInputs


class TestPackDetInputs(unittest.TestCase):

    def setUp(self):
        """Setup the model and optimizer which are used in every test method.

        TestCase calls functions in this order: setUp() -> testMethod() ->
        tearDown() -> cleanUp()
        """
        data_prefix = osp.join(osp.dirname(__file__), '../../data')
        img_path = osp.join(data_prefix, 'color.jpg')
        rng = np.random.RandomState(0)
        self.results1 = {
            'img_id': 1,
            'img_path': img_path,
            'ori_shape': (300, 400),
            'img_shape': (600, 800),
            'scale_factor': 2.0,
            'flip': False,
            'img': rng.rand(300, 400),
            'gt_seg_map': rng.rand(300, 400),
            'gt_masks':
            BitmapMasks(rng.rand(3, 300, 400), height=300, width=400),
            'gt_bboxes_labels': rng.rand(3, ),
            'gt_ignore_flags': np.array([0, 0, 1], dtype=bool),
            'proposals': rng.rand(2, 4),
            'proposals_scores': rng.rand(2, )
        }
        self.results2 = {
            'img_id': 1,
            'img_path': img_path,
            'ori_shape': (300, 400),
            'img_shape': (600, 800),
            'scale_factor': 2.0,
            'flip': False,
            'img': rng.rand(300, 400),
            'gt_seg_map': rng.rand(300, 400),
            'gt_masks':
            BitmapMasks(rng.rand(3, 300, 400), height=300, width=400),
            'gt_bboxes_labels': rng.rand(3, ),
            'proposals': rng.rand(2, 4),
            'proposals_scores': rng.rand(2, )
        }
        self.results3 = {
            'img_id': 1,
            'img_path': img_path,
            'ori_shape': (300, 400),
            'img_shape': (600, 800),
            'scale_factor': 2.0,
            'flip': False,
            'img': rng.rand(300, 400),
            'gt_seg_map': rng.rand(300, 400),
            'gt_masks':
            BitmapMasks(rng.rand(3, 300, 400), height=300, width=400),
            'gt_panoptic_seg': rng.rand(1, 300, 400),
            'gt_bboxes_labels': rng.rand(3, ),
            'proposals': rng.rand(2, 4),
            'proposals_scores': rng.rand(2, )
        }
        self.meta_keys = ('img_id', 'img_path', 'ori_shape', 'scale_factor',
                          'flip')

    def test_transform(self):
        transform = PackDetInputs(meta_keys=self.meta_keys)
        results = transform(copy.deepcopy(self.results1))
        self.assertIn('data_samples', results)
        self.assertIsInstance(results['data_samples'], DetDataSample)
        self.assertIsInstance(results['data_samples'].gt_instances,
                              InstanceData)
        self.assertIsInstance(results['data_samples'].ignored_instances,
                              InstanceData)
        self.assertEqual(len(results['data_samples'].gt_instances), 2)
        self.assertEqual(len(results['data_samples'].ignored_instances), 1)
        self.assertIsInstance(results['data_samples'].gt_sem_seg, PixelData)

    def test_transform_without_ignore(self):
        transform = PackDetInputs(meta_keys=self.meta_keys)
        results = transform(copy.deepcopy(self.results2))
        self.assertIn('data_samples', results)
        self.assertIsInstance(results['data_samples'], DetDataSample)
        self.assertIsInstance(results['data_samples'].gt_instances,
                              InstanceData)
        self.assertIsInstance(results['data_samples'].ignored_instances,
                              InstanceData)
        self.assertEqual(len(results['data_samples'].gt_instances), 3)
        self.assertEqual(len(results['data_samples'].ignored_instances), 0)
        self.assertIsInstance(results['data_samples'].gt_sem_seg, PixelData)

    def test_transform_with_panoptic_seg(self):
        transform = PackDetInputs(meta_keys=self.meta_keys)
        results = transform(copy.deepcopy(self.results3))
        self.assertIn('data_samples', results)
        self.assertIsInstance(results['data_samples'], DetDataSample)
        self.assertIsInstance(results['data_samples'].gt_instances,
                              InstanceData)
        self.assertIsInstance(results['data_samples'].ignored_instances,
                              InstanceData)
        self.assertEqual(len(results['data_samples'].gt_instances), 3)
        self.assertEqual(len(results['data_samples'].ignored_instances), 0)
        self.assertIsInstance(results['data_samples'].gt_sem_seg, PixelData)
        self.assertIsInstance(results['data_samples'].gt_panoptic_seg,
                              PixelData)

    def test_repr(self):
        transform = PackDetInputs(meta_keys=self.meta_keys)
        self.assertEqual(
            repr(transform), f'PackDetInputs(meta_keys={self.meta_keys})')