# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
The code refers to https://github.com/facebookresearch/detr
Modified by Zhang Yanyi
"""
import math
import torch
from torch import nn
import cv2
import numpy as np

from models.transformer.util.misc import NestedTensor


class PositionEmbeddingSine_3D(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one
    used by the Attention is all you need paper, generalized to work on images.
    """
    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
        super().__init__()
        self.num_pos_feats_t = num_pos_feats/8*2
        self.num_pos_feats_s = num_pos_feats/8*3
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale

    def forward(self, tensor_list: NestedTensor):
        x = tensor_list.tensors

        mask = tensor_list.mask

        assert mask is not None
        #b, t, h, w = mask.shape
        #mask = mask.reshape(b, 1, h, w).repeat(1,4,1,1)
        not_mask = ~mask
        #print('mask1',mask[1, :,:,:])

        t_embed = not_mask.cumsum(1, dtype=torch.float32)
        y_embed = not_mask.cumsum(2, dtype=torch.float32)
        x_embed = not_mask.cumsum(3, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            t_embed = t_embed / (t_embed[:, -1:, :, :] + eps) * self.scale
            y_embed = y_embed / (y_embed[:, :, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, :, -1:] + eps) * self.scale


        t_dim_t = torch.arange(self.num_pos_feats_t, dtype=torch.float32, device=x.device)
        t_dim_t = self.temperature ** (2 * (t_dim_t // 2) / self.num_pos_feats_t)

        pos_t = t_embed[:, :, :, :, None] / t_dim_t

        s_dim_t = torch.arange(self.num_pos_feats_s, dtype=torch.float32, device=x.device)
        s_dim_t = self.temperature ** (2 * (s_dim_t // 2) / self.num_pos_feats_s)
        pos_x = x_embed[:, :, :, :, None] / s_dim_t
        pos_y = y_embed[:, :, :, :, None] / s_dim_t


        pos_x = torch.stack((pos_x[:, :, :, :, 0::2].sin(), pos_x[:, :, :, :, 1::2].cos()), dim=5).flatten(4)

        pos_y = torch.stack((pos_y[:, :, :, :, 0::2].sin(), pos_y[:, :, :, :, 1::2].cos()), dim=5).flatten(4)

        pos_t = torch.stack((pos_t[:, :, :, :, 0::2].sin(), pos_t[:, :, :, :, 1::2].cos()), dim=5).flatten(4)

        pos = torch.cat((pos_t, pos_y, pos_x), dim=4).permute(0, 4, 1, 2, 3)

        return pos

def build_position_encoding(hidden_dim):
    position_embedding = PositionEmbeddingSine_3D(hidden_dim, normalize=True)
    return position_embedding


