# LICENSE HEADER MANAGED BY add-license-header
#
# Copyright 2018 Kornia Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

import pytest
import torch

import kornia

from testing.base import BaseTester


class TestMeanAveragePrecision(BaseTester):
    def test_smoke(self, device, dtype):
        boxes = torch.tensor([[100, 50, 150, 100.0]], device=device, dtype=dtype)
        labels = torch.tensor([1], device=device, dtype=torch.long)
        scores = torch.tensor([0.7], device=device, dtype=dtype)

        gt_boxes = torch.tensor([[100, 50, 150, 100.0]], device=device, dtype=dtype)
        gt_labels = torch.tensor([1], device=device, dtype=torch.long)

        mean_ap = kornia.metrics.mean_average_precision([boxes], [labels], [scores], [gt_boxes], [gt_labels], 2)

        self.assert_close(mean_ap[0], torch.tensor(1.0, device=device, dtype=dtype))
        self.assert_close(mean_ap[1][1], 1.0)

    def test_raise(self, device, dtype):
        boxes = torch.tensor([[100, 50, 150, 100.0]], device=device, dtype=dtype)
        labels = torch.tensor([1], device=device, dtype=torch.long)
        scores = torch.tensor([0.7], device=device, dtype=dtype)

        gt_boxes = torch.tensor([[100, 50, 150, 100.0]], device=device, dtype=dtype)
        gt_labels = torch.tensor([1], device=device, dtype=torch.long)

        with pytest.raises(AssertionError):
            _ = kornia.metrics.mean_average_precision(boxes[0], [labels], [scores], [gt_boxes], [gt_labels], 2)