import torch
from torch import nn
import math
from PIL import Image, ImageDraw, ImageFont
import logging
import os
import pandas as pd
import csv
import pickle
import numpy as np

from torch.nn import BCELoss

from torch.nn import functional as F
import math
import numbers
from typing import List

def get_all_attention_64(attn_maps_down, attn_maps_mid , attn_maps_up, res = 16):
    result  = []
    
    for attn_map_integrated in attn_maps_up:
        if attn_map_integrated == []: continue
        attn_map = attn_map_integrated.squeeze(0)
        # print(attn_map.shape)
        b, i, j = attn_map.shape
        H = W = int(math.sqrt(i))
        # print(H)
        
        if H == res:
            item = attn_map.reshape(-1, res, res, attn_map.shape[-1] )
            item = item.permute(0, 3, 1, 2)
            item = F.interpolate(item, 64, mode='bilinear').permute(0, 2, 3, 1)
            result.append(item)
    for attn_map_integrated in attn_maps_mid:
        attn_map =  attn_map_integrated.squeeze(0)
        b, i, j = attn_map.shape
        H = W = int(math.sqrt(i))
        # print(H)
        if (H==8):
            item = attn_map.reshape(-1, 8, 8, attn_map.shape[-1] )
            item = item.permute(0, 3, 1, 2)
            item = F.interpolate(item, 64, mode='bilinear').permute(0, 2, 3, 1)
            result.append(item)

    for attn_map_integrated in attn_maps_down:
        if attn_map_integrated == []: continue
        attn_map = attn_map_integrated.squeeze(0)
        if attn_map == []: continue
        b, i, j = attn_map.shape
        H = W = int(math.sqrt(i))

        if H == res:
            item = attn_map.reshape(-1, res, res, attn_map.shape[-1] )
            item = item.permute(0, 3, 1, 2)
            item = F.interpolate(item, 64, mode='bilinear').permute(0, 2, 3, 1)
            result.append(item)
    # print('RES LENGTH', len(result))
    
    # for maps in result:
    #     print(maps.shape)
    result = torch.cat(result, dim=0)
    result = result.sum(0) / result.shape[0]
    return result


def compute_loco_v2(attn_maps_down, attn_maps_mid, attn_maps_up, bboxes, object_positions, smooth_attn=True, topk = 0.8):
    
    loss = 0.
    pad_loss = 0.
    total_fg_map = torch.zeros(size=(64, 64)).cuda()

    alpha = 0.2
    beta = 0.8

    object_number = len(bboxes)
    if object_number == 0:
        return torch.tensor(0).float().cuda() if torch.cuda.is_available() else torch.tensor(0).float()
    attn16 = get_all_attention_64(attn_maps_down[-1]+ attn_maps_down[-2], attn_maps_mid, attn_maps_up[0]+attn_maps_up[1], 16)
    all_attn = [attn16]
    max_loss = 0


    for attn_map in all_attn:

        sum_in = 0.
        sum_out = 0.

        i, j, k = attn_map.shape
        H = W = i 
        for obj_idx in range(object_number): 
            obj_loss = 0
            mask = torch.zeros(size=(H, W)).cuda() if torch.cuda.is_available() else torch.zeros(size=(H, W))
            for obj_box in bboxes[obj_idx]:

                x_min, y_min, x_max, y_max = int(obj_box[0] * W), \
                    int(obj_box[1] * H), int(obj_box[2] * W), int(obj_box[3] * H)
                mask[y_min: y_max, x_min: x_max] = 1 
                total_fg_map[y_min: y_max, x_min: x_max] = 1

            for obj_position in [object_positions[obj_idx]]: 
                
                ca_map_obj = attn_map[:, :, obj_position].sum(-1)

                ca_map_obj = ca_map_obj.reshape(H, W)
                norm_ca_map_obj = ca_map_obj / ca_map_obj.max()
                norm_ca_map_obj = norm_ca_map_obj.reshape(H, W)

                sum_in += (norm_ca_map_obj * mask).sum()
                sum_out += (norm_ca_map_obj * (1 - mask)).sum()

            loss += (obj_loss/len(object_positions[obj_idx]))

        sot_map = attn_map[:, :, 0].reshape(H, W)
        eot_map = attn_map[:, :, -1].reshape(H, W)

        norm_sot_map = (1 - sot_map) / (1 - sot_map).max()
        norm_eot_map = eot_map / eot_map.max()


        pad_map = beta * norm_sot_map + (1 - beta) * norm_eot_map

    total_fg_mask = total_fg_map
    fg_map = pad_map * total_fg_mask

    bce_loss = F.binary_cross_entropy(torch.sigmoid(pad_map.to(torch.float16).reshape(-1)), fg_map.to(torch.float16).reshape(-1))

    pad_loss += bce_loss

    loss += (1 - sum_in / (sum_in + sum_out)) ** 2

    return loss + alpha * pad_loss

def compute_ca_loss(attn_maps_mid, attn_maps_up, bboxes, object_positions):
    loss = 0
    object_number = len(bboxes)
    if object_number == 0:
        return torch.tensor(0).float().cuda()
    for attn_map_integrated in attn_maps_mid:
        attn_map = attn_map_integrated.chunk(2)[1]

        #
        b, i, j = attn_map.shape
        H = W = int(math.sqrt(i))
        for obj_idx in range(object_number):
            obj_loss = 0
            mask = torch.zeros(size=(H, W)).cuda()
            for obj_box in bboxes[obj_idx]:

                x_min, y_min, x_max, y_max = int(obj_box[0] * W), \
                    int(obj_box[1] * H), int(obj_box[2] * W), int(obj_box[3] * H)
                mask[y_min: y_max, x_min: x_max] = 1

            for obj_position in object_positions[obj_idx]:
                ca_map_obj = attn_map[:, :, obj_position].reshape(b, H, W)

                activation_value = (ca_map_obj * mask).reshape(b, -1).sum(dim=-1)/ca_map_obj.reshape(b, -1).sum(dim=-1)

                obj_loss += torch.mean((1 - activation_value) ** 2)
            loss += (obj_loss/len(object_positions[obj_idx]))

        # compute loss on padding tokens
        # activation_value = torch.zeros(size=(b, )).cuda()
        # for obj_idx in range(object_number):
        #     bbox = bboxes[obj_idx]
        #     ca_map_obj = attn_map[:, :, padding_start:].reshape(b, H, W, -1)
        #     activation_value += ca_map_obj[:, int(bbox[0] * H): int(bbox[1] * H),
        #                        int(bbox[2] * W): int(bbox[3] * W), :].reshape(b, -1).sum(dim=-1) / ca_map_obj.reshape(b, -1).sum(dim=-1)
        #
        # loss += torch.mean((1 - activation_value) ** 2)


    for attn_map_integrated in attn_maps_up[0]:
        attn_map = attn_map_integrated.chunk(2)[1]
        #
        b, i, j = attn_map.shape
        H = W = int(math.sqrt(i))

        for obj_idx in range(object_number):
            obj_loss = 0
            mask = torch.zeros(size=(H, W)).cuda()
            for obj_box in bboxes[obj_idx]:
                x_min, y_min, x_max, y_max = int(obj_box[0] * W), \
                    int(obj_box[1] * H), int(obj_box[2] * W), int(obj_box[3] * H)
                mask[y_min: y_max, x_min: x_max] = 1

            for obj_position in object_positions[obj_idx]:
                ca_map_obj = attn_map[:, :, obj_position].reshape(b, H, W)
                # ca_map_obj = attn_map[:, :, object_positions[obj_position]].reshape(b, H, W)

                activation_value = (ca_map_obj * mask).reshape(b, -1).sum(dim=-1) / ca_map_obj.reshape(b, -1).sum(
                    dim=-1)

                obj_loss += torch.mean((1 - activation_value) ** 2)
            loss += (obj_loss / len(object_positions[obj_idx]))

        # compute loss on padding tokens
        # activation_value = torch.zeros(size=(b, )).cuda()
        # for obj_idx in range(object_number):
        #     bbox = bboxes[obj_idx]
        #     ca_map_obj = attn_map[:, :,padding_start:].reshape(b, H, W, -1)
        #     activation_value += ca_map_obj[:, int(bbox[0] * H): int(bbox[1] * H),
        #                        int(bbox[2] * W): int(bbox[3] * W), :].reshape(b, -1).sum(dim=-1) / ca_map_obj.reshape(b, -1).sum(dim=-1)
        #
        # loss += torch.mean((1 - activation_value) ** 2)
    loss = loss / (object_number * (len(attn_maps_up[0]) + len(attn_maps_mid)))
    return loss