def get_2d_sincos_pos_embed_np(
    embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16
):
    """
    Creates 2D sinusoidal positional embeddings.

    Args:
        embed_dim (`int`):
            The embedding dimension.
        grid_size (`int`):
            The size of the grid height and width.
        cls_token (`bool`, defaults to `False`):
            Whether or not to add a classification token.
        extra_tokens (`int`, defaults to `0`):
            The number of extra tokens to add.
        interpolation_scale (`float`, defaults to `1.0`):
            The scale of the interpolation.

    Returns:
        pos_embed (`np.ndarray`):
            Shape is either `[grid_size * grid_size, embed_dim]` if not using cls_token, or `[1 + grid_size*grid_size,
            embed_dim]` if using cls_token
    """
    if isinstance(grid_size, int):
        grid_size = (grid_size, grid_size)

    grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale
    grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
    pos_embed = get_2d_sincos_pos_embed_from_grid_np(embed_dim, grid)
    if cls_token and extra_tokens > 0:
        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
    return pos_embed

    Creates 2D sinusoidal positional embeddings.

    Args:
        embed_dim (`int`):
            The embedding dimension.
        grid_size (`int`):
            The size of the grid height and width.
        cls_token (`bool`, defaults to `False`):
            Whether or not to add a classification token.
        extra_tokens (`int`, defaults to `0`):
            The number of extra tokens to add.
        interpolation_scale (`float`, defaults to `1.0`):
            The scale of the interpolation.

    Returns:
        pos_embed (`np.ndarray`):
            Shape is either `[grid_size * grid_size, embed_dim]` if not using cls_token, or `[1 + grid_size*grid_size,
            embed_dim]` if using cls_token
    
src/diffusers/models/embeddings.py