import time

import torch

import torch.nn as nn

from torch.autograd import Variable

import torch.optim as optim

import torchvision

from torchvision import datasets, models

from torchvision import transforms as T

from torch.utils.data import DataLoader, Dataset

import numpy as np

import matplotlib.pyplot as plt

import os

import time

import pandas as pd

from skimage import io, transform

import matplotlib.image as mpimg

from PIL import Image

from sklearn.metrics import roc_auc_score

import torch.nn.functional as F

import scipy

import random

import pickle

import scipy.io as sio

import itertools

from scipy.ndimage.interpolation import shift

import copy

import warnings

warnings.filterwarnings("ignore")

plt.ion()

from utils import *

def evaluate_pp(model,prediction_models, dataloader, data_size, batch_size, phase, dice_loss = dice_loss,\

                smooth = False, filter_size = 3, print_all = False, certainity_map = False):

    model.eval()

    running_loss = 0

    running_dice_score_class_0 = 0

    running_dice_score_class_1 = 0

    running_dice_score_class_2 = 0

    dc_sr = {0:[],1:[],2:[]}

    acc_sr = {0:[],1:[],2:[]}

    phase = phase

    for i in prediction_models:

        for param in i.parameters():

            param.requires_grad = False

    for i,data in enumerate(dataloader[phase]):

        input, segF,segP, segT,_ = data

        input = Variable(input).cuda()

        input_pp = []

        for j in prediction_models:

            output = j(input)

            preds_m = predict_pp(output)

            input_pp.append(preds_m)

        input_pp = torch.cat(input_pp,dim = 1)

        output_pp = model(input_pp)

        true = Variable(segments(segF, segP, segT)).cuda()

        loss = dice_loss(true,output_pp)

        running_loss += loss.data[0] * batch_size

        dice_score_batch = dice_score(true,output_pp, smooth= smooth, filter_size=filter_size)

        running_dice_score_class_0 += dice_score_batch[0] * batch_size

        running_dice_score_class_1 += dice_score_batch[1] * batch_size

        running_dice_score_class_2 += dice_score_batch[2] * batch_size

        dc_dict, acc_dict = dice_score_list(true,output_pp)

        if certainity_map:

            cm = make_certainity_maps(output_pp)

        for k in range(3):

            dc_sr[k].append(dc_dict[k])

            acc_sr[k].append(acc_dict[k])

        preds = predict(output_pp,smooth = smooth, filter_size=filter_size)

        if i == 11 or i == 4 or print_all:

            for k in range(batch_size):

                if certainity_map:

                    image_to_mask(input[k,1,:,:].data.cpu().numpy(),\

                              true[k,0,:,:].data.cpu().numpy(),\

                              true[k,1,:,:].data.cpu().numpy(),\

                              true[k,2,:,:].data.cpu().numpy(),\

                             preds[0][k,:,:].cpu().numpy(),\

                             preds[1][k,:,:].cpu().numpy(),\

                             preds[2][k,:,:].cpu().numpy(),\

                            cm[k].data.cpu().numpy())

                else:

                    image_to_mask(input[k,1,:,:].data.cpu().numpy(),\

                              true[k,0,:,:].data.cpu().numpy(),\

                              true[k,1,:,:].data.cpu().numpy(),\

                              true[k,2,:,:].data.cpu().numpy(),\

                             preds[0][k,:,:].cpu().numpy(),\

                             preds[1][k,:,:].cpu().numpy(),\

                             preds[2][k,:,:].cpu().numpy())

    loss = running_loss/data_sizes[phase] 

    dice_score_0 = running_dice_score_class_0/data_sizes[phase]

    dice_score_1 = running_dice_score_class_1/data_sizes[phase]

    dice_score_2 = running_dice_score_class_2/data_sizes[phase]

    for i in range(3):

        dc_sr[i] = list(itertools.chain(*dc_sr[i]))

        acc_sr[i] = list(itertools.chain(*acc_sr[i]))

    print('{} loss: {:.4f}, Dice Score (class 0): {:.4f}, Dice Score (class 1): {:.4f},Dice Score (class 2): {:.4f}'.format(phase,loss, dice_score_0, dice_score_1, dice_score_2))

    return loss, dice_score_0, dice_score_1, dice_score_2, dc_sr, acc_sr