import matplotlib.colors as mcolors
import torch


def preprocess_data(data, skip: bool = True):
    # Assuming 'data' is a tensor of shape [n_samples, 3]
    if not skip:
        # Compute argmin and argmax for each row
        argmin_values = torch.argmin(data, dim=1, keepdim=True).float()
        argmax_values = torch.argmax(data, dim=1, keepdim=True).float()

        # Normalize or scale argmin and argmax if necessary
        # For example, here I am just dividing by the number of features
        argmin_values /= data.shape[1] - 1
        argmax_values /= data.shape[1] - 1

        # Concatenate the argmin and argmax values to the original data
        new_data = torch.cat((data, argmin_values, argmax_values), dim=1)
    else:
        new_data = data
    return new_data


def extract_colors():
    # Extracting the list of xkcd colors as RGB triples
    xkcd_colors = mcolors.XKCD_COLORS
    rgb_values = [mcolors.to_rgb(color) for color in xkcd_colors.values()]

    # Extracting the list of xkcd color names
    xkcd_color_names = list(xkcd_colors.keys())

    # Convert the list of RGB triples to a PyTorch tensor
    rgb_tensor = torch.tensor(rgb_values, dtype=torch.float32)
    return rgb_tensor, xkcd_color_names


PURE_RGB = preprocess_data(
    torch.cat([torch.eye(3), torch.eye(3) + torch.eye(3)[:, [1, 2, 0]]], dim=0)
)
PURE_HSV = torch.tensor(
    [[0], [1 / 3], [2 / 3], [5 / 6], [1 / 6], [0.5]], dtype=torch.float32
)