import math

import torch
import torch.nn.functional as F


def timm_resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
    """Timm version pos embed resize function.

    copied from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    """  # noqa:E501
    ntok_new = posemb_new.shape[1]
    if num_tokens:
        posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0,
                                                                 num_tokens:]
        ntok_new -= num_tokens
    else:
        posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
    gs_old = int(math.sqrt(len(posemb_grid)))
    if not len(gs_new):  # backwards compatibility
        gs_new = [int(math.sqrt(ntok_new))] * 2
    assert len(gs_new) >= 2
    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old,
                                      -1).permute(0, 3, 1, 2)
    posemb_grid = F.interpolate(
        posemb_grid, size=gs_new, mode='bicubic', align_corners=False)
    posemb_grid = posemb_grid.permute(0, 2, 3,
                                      1).reshape(1, gs_new[0] * gs_new[1], -1)
    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
    return posemb
