from typing import List, Optional, Tuple, Union

import torch
from torch import Tensor

from kornia.testing import KORNIA_CHECK, KORNIA_CHECK_SHAPE

# TODO: implement width of the line


def _draw_pixel(image: torch.Tensor, x: int, y: int, color: torch.Tensor) -> None:
    r"""Draws a pixel into an image.

    Args:
        image: the input image to where to draw the lines with shape :math`(C,H,W)`.
        x: the x coordinate of the pixel.
        y: the y coordinate of the pixel.
        color: the color of the pixel with :math`(C)` where :math`C` is the number of channels of the image.

    Return:
        Nothing is returned.
    """
    image[:, y, x] = color


def draw_line(image: torch.Tensor, p1: torch.Tensor, p2: torch.Tensor, color: torch.Tensor) -> torch.Tensor:
    r"""Draw a single line into an image.

    Args:
        image: the input image to where to draw the lines with shape :math`(C,H,W)`.
        p1: the start point [x y] of the line with shape (2).
        p2: the end point [x y] of the line with shape (2).
        color: the color of the line with shape :math`(C)` where :math`C` is the number of channels of the image.

    Return:
        the image with containing the line.

    Examples:
        >>> image = torch.zeros(1, 8, 8)
        >>> draw_line(image, torch.tensor([6, 4]), torch.tensor([1, 4]), torch.tensor([255]))
        tensor([[[  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],
                 [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],
                 [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],
                 [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],
                 [  0., 255., 255., 255., 255., 255., 255.,   0.],
                 [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],
                 [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],
                 [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.]]])
    """

    if (len(p1) != 2) or (len(p2) != 2):
        raise ValueError("p1 and p2 must have length 2.")

    if len(image.size()) != 3:
        raise ValueError("image must have 3 dimensions (C,H,W).")

    if color.size(0) != image.size(0):
        raise ValueError("color must have the same number of channels as the image.")

    if (p1[0] >= image.size(2)) or (p1[1] >= image.size(1) or (p1[0] < 0) or (p1[1] < 0)):
        raise ValueError("p1 is out of bounds.")

    if (p2[0] >= image.size(2)) or (p2[1] >= image.size(1) or (p2[0] < 0) or (p2[1] < 0)):
        raise ValueError("p2 is out of bounds.")

    # move p1 and p2 to the same device as the input image
    # move color to the same device and dtype as the input image
    p1 = p1.to(image.device).to(torch.int64)
    p2 = p2.to(image.device).to(torch.int64)
    color = color.to(image)

    # assign points
    x1, y1 = p1
    x2, y2 = p2

    # calcullate coefficients A,B,C of line
    # from equation Ax + By + C = 0
    A = y2 - y1
    B = x1 - x2
    C = x2 * y1 - x1 * y2

    # make sure A is positive to utilize the function properly
    if A < 0:
        A = -A
        B = -B
        C = -C

    # calculate the slope of the line
    # check for division by zero
    if B != 0:
        m = -A / B

    # make sure you start drawing in the right direction
    x1, x2 = min(x1, x2).long(), max(x1, x2).long()
    y1, y2 = min(y1, y2).long(), max(y1, y2).long()

    # line equation that determines the distance away from the line
    def line_equation(x, y):
        return A * x + B * y + C

    # vertical line
    if B == 0:
        image[:, y1 : y2 + 1, x1] = color
    # horizontal line
    elif A == 0:
        image[:, y1, x1 : x2 + 1] = color
    # slope between 0 and 1
    elif 0 < m < 1:
        for i in range(x1, x2 + 1):
            _draw_pixel(image, i, y1, color)
            if line_equation(i + 1, y1 + 0.5) > 0:
                y1 += 1
    # slope greater than or equal to 1
    elif m >= 1:
        for j in range(y1, y2 + 1):
            _draw_pixel(image, x1, j, color)
            if line_equation(x1 + 0.5, j + 1) < 0:
                x1 += 1
    # slope less then -1
    elif m <= -1:
        for j in range(y1, y2 + 1):
            _draw_pixel(image, x2, j, color)
            if line_equation(x2 - 0.5, j + 1) > 0:
                x2 -= 1
    # slope between -1 and 0
    elif -1 < m < 0:
        for i in range(x1, x2 + 1):
            _draw_pixel(image, i, y2, color)
            if line_equation(i + 1, y2 - 0.5) > 0:
                y2 -= 1

    return image


def draw_rectangle(
    image: torch.Tensor, rectangle: torch.Tensor, color: Optional[torch.Tensor] = None, fill: Optional[bool] = None
) -> torch.Tensor:
    r"""Draw N rectangles on a batch of image tensors.

    Args:
        image: is tensor of BxCxHxW.
        rectangle: represents number of rectangles to draw in BxNx4
            N is the number of boxes to draw per batch index[x1, y1, x2, y2]
            4 is in (top_left.x, top_left.y, bot_right.x, bot_right.y).
        color: a size 1, size 3, BxNx1, or BxNx3 tensor.
            If C is 3, and color is 1 channel it will be broadcasted.
        fill: is a flag used to fill the boxes with color if True.

    Returns:
        This operation modifies image inplace but also returns the drawn tensor for
        convenience with same shape the of the input BxCxHxW.

    Example:
        >>> img = torch.rand(2, 3, 10, 12)
        >>> rect = torch.tensor([[[0, 0, 4, 4]], [[4, 4, 10, 10]]])
        >>> out = draw_rectangle(img, rect)
    """

    batch, c, h, w = image.shape
    batch_rect, num_rectangle, num_points = rectangle.shape
    if batch != batch_rect:
        raise AssertionError("Image batch and rectangle batch must be equal")
    if num_points != 4:
        raise AssertionError("Number of points in rectangle must be 4")

    # clone rectangle, in case it's been expanded assignment from clipping causes problems
    rectangle = rectangle.long().clone()

    # clip rectangle to hxw bounds
    rectangle[:, :, 1::2] = torch.clamp(rectangle[:, :, 1::2], 0, h - 1)
    rectangle[:, :, ::2] = torch.clamp(rectangle[:, :, ::2], 0, w - 1)

    if color is None:
        color = torch.tensor([0.0] * c).expand(batch, num_rectangle, c)

    if fill is None:
        fill = False

    if len(color.shape) == 1:
        color = color.expand(batch, num_rectangle, c)
    b, n, color_channels = color.shape

    if color_channels == 1 and c == 3:
        color = color.expand(batch, num_rectangle, c)

    for b in range(batch):
        for n in range(num_rectangle):
            if fill:
                image[
                    b,
                    :,
                    int(rectangle[b, n, 1]) : int(rectangle[b, n, 3] + 1),
                    int(rectangle[b, n, 0]) : int(rectangle[b, n, 2] + 1),
                ] = color[b, n, :, None, None]
            else:
                image[b, :, int(rectangle[b, n, 1]) : int(rectangle[b, n, 3] + 1), rectangle[b, n, 0]] = color[
                    b, n, :, None
                ]
                image[b, :, int(rectangle[b, n, 1]) : int(rectangle[b, n, 3] + 1), rectangle[b, n, 2]] = color[
                    b, n, :, None
                ]
                image[b, :, rectangle[b, n, 1], int(rectangle[b, n, 0]) : int(rectangle[b, n, 2] + 1)] = color[
                    b, n, :, None
                ]
                image[b, :, rectangle[b, n, 3], int(rectangle[b, n, 0]) : int(rectangle[b, n, 2] + 1)] = color[
                    b, n, :, None
                ]

    return image


def _get_convex_edges(polygon: Tensor, h: int, w: int) -> Tuple[Tensor, Tensor]:
    r"""Gets the left and right edges of a polygon for each y-coordinate y \in [0, h)
    Args:
        polygons: represents polygons to draw in BxNx2
            N is the number of points
            2 is (x, y).
        h: bottom most coordinate (top coordinate is assumed to be 0)
        w: right most coordinate (left coordinate is assumed to be 0)
    Returns:
        The left and right edges of the polygon of shape (B,B).
    """

    dtype = polygon.dtype

    # Check if polygons are in loop closed format, if not -> make it so
    if not torch.allclose(polygon[..., -1, :], polygon[..., 0, :]):
        polygon = torch.cat((polygon, polygon[..., :1, :]), dim=-2)  # (B, N+1, 2)

    # Partition points into edges
    x_start, y_start = polygon[..., :-1, 0], polygon[..., :-1, 1]
    x_end, y_end = polygon[..., 1:, 0], polygon[..., 1:, 1]

    # Create scanlines, edge dx/dy, and produce x values
    ys = torch.arange(h, device=polygon.device, dtype=dtype)
    dx = ((x_end - x_start) / (y_end - y_start + 1e-12)).clamp(-w, w)
    xs = (ys[..., :, None] - y_start[..., None, :]) * dx[..., None, :] + x_start[..., None, :]

    # Only count edge in their active regions (i.e between the vertices)
    valid_edges = (y_start[..., None, :] <= ys[..., :, None]).logical_and(ys[..., :, None] <= y_end[..., None, :])
    valid_edges |= (y_start[..., None, :] >= ys[..., :, None]).logical_and(ys[..., :, None] >= y_end[..., None, :])
    x_left_edges = xs.clone()
    x_left_edges[~valid_edges] = w
    x_right_edges = xs.clone()
    x_right_edges[~valid_edges] = -1

    # Find smallest and largest x values for the valid edges
    x_left = x_left_edges.min(dim=-1).values
    x_right = x_right_edges.max(dim=-1).values
    return x_left, x_right


def _batch_polygons(polygons: List[Tensor]) -> Tensor:
    r"""Converts a List of variable length polygons into a fixed size tensor.

    Works by repeating the last element in the tensor.
    Args:
        polygon: List of variable length polygons of shape [N_1 x 2, N_2 x 2, ..., N_B x 2].
                    B is the batch size,
                    N_i is the number of points,
                    2 is (x, y).
    Returns:
        A fixed size tensor of shape (B, N, 2) where N = max_i(N_i)
    """
    B, N = len(polygons), len(max(polygons, key=len))
    batched_polygons = torch.zeros(B, N, 2, dtype=polygons[0].dtype, device=polygons[0].device)
    for b, p in enumerate(polygons):
        batched_polygons[b] = torch.cat((p, p[-1:].expand(N - len(p), 2))) if len(p) < N else p
    return batched_polygons


def draw_convex_polygon(images: Tensor, polygons: Union[Tensor, List[Tensor]], colors: Tensor) -> Tensor:
    r"""Draws convex polygons on a batch of image tensors.

    Args:
        images: is tensor of BxCxHxW.
        polygons: represents polygons as points, either BxNx2 or List of variable length polygons.
            N is the number of points.
            2 is (x, y).
        color: a B x 3 tensor or 3 tensor with color to fill in.

    Returns:
        This operation modifies image inplace but also returns the drawn tensor for
        convenience with same shape the of the input BxCxHxW.

    Note:
        This function assumes a coordinate system (0, h - 1), (0, w - 1) in the image, with (0, 0) being the center
        of the top-left pixel and (w - 1, h - 1) being the center of the bottom-right coordinate.

    Example:
        >>> img = torch.rand(1, 3, 12, 16)
        >>> poly = torch.tensor([[[4, 4], [12, 4], [12, 8], [4, 8]]])
        >>> color = torch.tensor([[0.5, 0.5, 0.5]])
        >>> out = draw_convex_polygon(img, poly, color)
    """
    # TODO: implement optional linetypes for smooth edges
    KORNIA_CHECK_SHAPE(images, ["B", "C", "H", "W"])
    b_i, c_i, h_i, w_i, device = *images.shape, images.device
    if isinstance(polygons, List):
        polygons = _batch_polygons(polygons)
    b_p, _, xy, device_p, dtype_p = *polygons.shape, polygons.device, polygons.dtype
    if len(colors.shape) == 1:
        colors = colors.expand(b_i, c_i)
    b_c, _, device_c = *colors.shape, colors.device
    KORNIA_CHECK(xy == 2, "Polygon vertices must be xy, i.e. 2-dimensional")
    KORNIA_CHECK(b_i == b_p == b_c, "Image, polygon, and color must have same batch dimension")
    KORNIA_CHECK(device == device_p == device_c, "Image, polygon, and color must have same device")

    x_left, x_right = _get_convex_edges(polygons, h_i, w_i)
    ws = torch.arange(w_i, device=device, dtype=dtype_p)[None, None, :]
    fill_region = (ws >= x_left[..., :, None]) & (ws <= x_right[..., :, None])
    images = (~fill_region[:, None]) * images + fill_region[:, None] * colors[..., None, None]
    return images