from __future__ import annotations

import numpy as np
import pytest

from supervision.detection.utils.converters import (
    xcycwh_to_xyxy,
    xywh_to_xyxy,
    xyxy_to_mask,
    xyxy_to_xcycarh,
    xyxy_to_xywh,
)


@pytest.mark.parametrize(
    ("xywh", "expected_result"),
    [
        (np.array([[10, 20, 30, 40]]), np.array([[10, 20, 40, 60]])),  # standard case
        (np.array([[0, 0, 0, 0]]), np.array([[0, 0, 0, 0]])),  # zero size bounding box
        (
            np.array([[50, 50, 100, 100]]),
            np.array([[50, 50, 150, 150]]),
        ),  # large bounding box
        (
            np.array([[-10, -20, 30, 40]]),
            np.array([[-10, -20, 20, 20]]),
        ),  # negative coordinates
        (np.array([[50, 50, 0, 30]]), np.array([[50, 50, 50, 80]])),  # zero width
        (np.array([[50, 50, 20, 0]]), np.array([[50, 50, 70, 50]])),  # zero height
        (np.array([]).reshape(0, 4), np.array([]).reshape(0, 4)),  # empty array
    ],
)
def test_xywh_to_xyxy(xywh: np.ndarray, expected_result: np.ndarray) -> None:
    result = xywh_to_xyxy(xywh)
    np.testing.assert_array_equal(result, expected_result)


@pytest.mark.parametrize(
    ("xyxy", "expected_result"),
    [
        (np.array([[10, 20, 40, 60]]), np.array([[10, 20, 30, 40]])),  # standard case
        (np.array([[0, 0, 0, 0]]), np.array([[0, 0, 0, 0]])),  # zero size bounding box
        (
            np.array([[50, 50, 150, 150]]),
            np.array([[50, 50, 100, 100]]),
        ),  # large bounding box
        (
            np.array([[-10, -20, 20, 20]]),
            np.array([[-10, -20, 30, 40]]),
        ),  # negative coordinates
        (np.array([[50, 50, 50, 80]]), np.array([[50, 50, 0, 30]])),  # zero width
        (np.array([[50, 50, 70, 50]]), np.array([[50, 50, 20, 0]])),  # zero height
        (np.array([]).reshape(0, 4), np.array([]).reshape(0, 4)),  # empty array
    ],
)
def test_xyxy_to_xywh(xyxy: np.ndarray, expected_result: np.ndarray) -> None:
    result = xyxy_to_xywh(xyxy)
    np.testing.assert_array_equal(result, expected_result)


@pytest.mark.parametrize(
    ("xyxy", "expected_result"),
    [
        # Empty and zero cases
        (np.array([]).reshape(0, 4), np.array([]).reshape(0, 4)),  # empty array
        (
            np.array([[0, 0, 0, 0]]),
            np.array([[0, 0, 0.0, 0]]),
        ),  # zero size bounding box
        (
            np.array([[10, 10, 10, 10]]),
            np.array([[10, 10, 0.0, 0]]),
        ),  # point (x1=x2, y1=y2)
        # Zero width/height cases
        (np.array([[50, 50, 80, 50]]), np.array([[65, 50, 0.0, 0]])),  # zero height
        (np.array([[50, 50, 50, 80]]), np.array([[50, 65, 0.0, 30]])),  # zero width
        # Standard cases
        (np.array([[10, 20, 40, 60]]), np.array([[25, 40, 0.75, 40]])),  # standard case
        (
            np.array([[-30, -40, -10, -20]]),
            np.array([[-20, -30, 1.0, 20]]),
        ),  # all negative values
        (
            np.array([[0.1, 0.2, 0.4, 0.6]]),
            np.array([[0.25, 0.4, 0.75, 0.4]]),
        ),  # values between 0-1
        # Different aspect ratios
        (
            np.array([[10, 20, 50, 100]]),
            np.array([[30, 60, 0.5, 80]]),
        ),  # tall rectangle (height > width)
        (
            np.array([[20, 10, 100, 50]]),
            np.array([[60, 30, 2.0, 40]]),
        ),  # wide rectangle (width > height)
        (
            np.array([[50, 50, 150, 150]]),
            np.array([[100, 100, 1.0, 100]]),
        ),  # height == width
        # Multiple boxes in one array
        (
            np.array([[0, 0, 0, 0], [10, 20, 40, 60]]),
            np.array([[0, 0, 0.0, 0], [25, 40, 0.75, 40]]),
        ),  # one zero-sized box and one normal box
    ],
)
def test_xyxy_to_xcycarh(xyxy: np.ndarray, expected_result: np.ndarray) -> None:
    result = xyxy_to_xcycarh(xyxy)
    np.testing.assert_allclose(result, expected_result)


@pytest.mark.parametrize(
    ("xcycwh", "expected_result"),
    [
        (np.array([[50, 50, 20, 30]]), np.array([[40, 35, 60, 65]])),  # standard case
        (np.array([[0, 0, 0, 0]]), np.array([[0, 0, 0, 0]])),  # zero size bounding box
        (
            np.array([[50, 50, 100, 100]]),
            np.array([[0, 0, 100, 100]]),
        ),  # large bounding box centered at (50, 50)
        (
            np.array([[-10, -10, 20, 30]]),
            np.array([[-20, -25, 0, 5]]),
        ),  # negative coordinates
        (np.array([[50, 50, 0, 30]]), np.array([[50, 35, 50, 65]])),  # zero width
        (np.array([[50, 50, 20, 0]]), np.array([[40, 50, 60, 50]])),  # zero height
        (np.array([]).reshape(0, 4), np.array([]).reshape(0, 4)),  # empty array
    ],
)
def test_xcycwh_to_xyxy(xcycwh: np.ndarray, expected_result: np.ndarray) -> None:
    result = xcycwh_to_xyxy(xcycwh)
    np.testing.assert_array_equal(result, expected_result)


@pytest.mark.parametrize(
    ("boxes", "resolution_wh", "expected"),
    [
        # 0) Empty input
        (
            np.array([], dtype=float).reshape(0, 4),
            (5, 4),
            np.array([], dtype=bool).reshape(0, 4, 5),
        ),
        # 1) Single pixel box
        (
            np.array([[2, 1, 2, 1]], dtype=float),
            (5, 4),
            np.array(
                [
                    [
                        [False, False, False, False, False],
                        [False, False, True, False, False],
                        [False, False, False, False, False],
                        [False, False, False, False, False],
                    ]
                ],
                dtype=bool,
            ),
        ),
        # 2) Horizontal line, inclusive bounds
        (
            np.array([[1, 2, 3, 2]], dtype=float),
            (5, 4),
            np.array(
                [
                    [
                        [False, False, False, False, False],
                        [False, False, False, False, False],
                        [False, True, True, True, False],
                        [False, False, False, False, False],
                    ]
                ],
                dtype=bool,
            ),
        ),
        # 3) Vertical line, inclusive bounds
        (
            np.array([[3, 0, 3, 2]], dtype=float),
            (5, 4),
            np.array(
                [
                    [
                        [False, False, False, True, False],
                        [False, False, False, True, False],
                        [False, False, False, True, False],
                        [False, False, False, False, False],
                    ]
                ],
                dtype=bool,
            ),
        ),
        # 4) Proper rectangle fill
        (
            np.array([[1, 1, 3, 2]], dtype=float),
            (5, 4),
            np.array(
                [
                    [
                        [False, False, False, False, False],
                        [False, True, True, True, False],
                        [False, True, True, True, False],
                        [False, False, False, False, False],
                    ]
                ],
                dtype=bool,
            ),
        ),
        # 5) Negative coordinates clipped to [0, 0]
        (
            np.array([[-2, -1, 1, 1]], dtype=float),
            (5, 4),
            np.array(
                [
                    [
                        [True, True, False, False, False],
                        [True, True, False, False, False],
                        [False, False, False, False, False],
                        [False, False, False, False, False],
                    ]
                ],
                dtype=bool,
            ),
        ),
        # 6) Overflow coordinates clipped to width-1 and height-1
        (
            np.array([[3, 2, 10, 10]], dtype=float),
            (5, 4),
            np.array(
                [
                    [
                        [False, False, False, False, False],
                        [False, False, False, False, False],
                        [False, False, False, True, True],
                        [False, False, False, True, True],
                    ]
                ],
                dtype=bool,
            ),
        ),
        # 7) Invalid box where max < min after ints, mask stays empty
        (
            np.array([[3, 2, 1, 4]], dtype=float),
            (5, 4),
            np.array(
                [
                    [
                        [False, False, False, False, False],
                        [False, False, False, False, False],
                        [False, False, False, False, False],
                        [False, False, False, False, False],
                    ]
                ],
                dtype=bool,
            ),
        ),
        # 8) Fractional coordinates are floored by int conversion
        #    (0.2,0.2)-(2.8,1.9) -> (0,0)-(2,1)
        (
            np.array([[0.2, 0.2, 2.8, 1.9]], dtype=float),
            (5, 4),
            np.array(
                [
                    [
                        [True, True, True, False, False],
                        [True, True, True, False, False],
                        [False, False, False, False, False],
                        [False, False, False, False, False],
                    ]
                ],
                dtype=bool,
            ),
        ),
        # 9) Multiple boxes, separate masks
        (
            np.array([[0, 0, 1, 0], [2, 1, 4, 3]], dtype=float),
            (5, 4),
            np.array(
                [
                    # Box 0: row 0, cols 0..1
                    [
                        [True, True, False, False, False],
                        [False, False, False, False, False],
                        [False, False, False, False, False],
                        [False, False, False, False, False],
                    ],
                    # Box 1: rows 1..3, cols 2..4
                    [
                        [False, False, False, False, False],
                        [False, False, True, True, True],
                        [False, False, True, True, True],
                        [False, False, True, True, True],
                    ],
                ],
                dtype=bool,
            ),
        ),
    ],
)
def test_xyxy_to_mask(boxes: np.ndarray, resolution_wh, expected: np.ndarray) -> None:
    result = xyxy_to_mask(boxes, resolution_wh)
    assert result.dtype == np.bool_
    assert result.shape == expected.shape
    np.testing.assert_array_equal(result, expected)