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()

class PPnet(nn.Module):

    def __init__(self, in_channels, out_channels, int_chan = 16, num_layers = 4):

        super(PPnet, self).__init__()

        self.layers = nn.ModuleList()

        in_chan = in_channels

        for i in range(num_layers):

            if i == num_layers-1:

                self.layers.append(nn.Conv2d(int_chan,out_channels,3,padding = 1))

            else:

                self.layers.append(nn.Conv2d(in_chan, int_chan, 3, padding= 1))

                self.layers.append(nn.BatchNorm2d(int_chan))

                self.layers.append(nn.ReLU())

                in_chan = int_chan

        self.layers = nn.Sequential(*self.layers)

    def forward(self,x):

        return self.layers(x)

def predict_pp(scores):

    preds = F.softmax(scores)

    pred_class = torch.max(preds, dim = 1, keepdim = True)[1]

    class_0_pred_seg = (pred_class == 0).type(torch.cuda.FloatTensor)

    class_1_pred_seg = (pred_class == 1).type(torch.cuda.FloatTensor)

    class_2_pred_seg = (pred_class == 2).type(torch.cuda.FloatTensor)

    class_3_pred_seg = (pred_class == 3).type(torch.cuda.FloatTensor)

    prediction = torch.cat((class_0_pred_seg,class_1_pred_seg,class_2_pred_seg,class_3_pred_seg),dim = 1)

    return prediction

def smoothing_preds(preds,filter_size = 3):

    smooth_pred = torch.round(F.avg_pool1d(preds,filter_size))

    return smooth_pred

from sklearn.metrics import accuracy_score

def dice_score_list(true,scores):

    N,C,sh1,sh2 = true.size()

    prediction = predict_pp(scores)

    prediction = prediction.data.cpu().numpy()

    true = true.data.cpu().numpy()

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

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

    for i in range(N):

        for j in range(3):

            pred = prediction[i,j,:,:]

            tr = true[i,j,:,:]

            flat_pred = np.ravel(pred)

            flat_true = np.ravel(tr)

            acc_sr[j].append(accuracy_score(flat_true,flat_pred))

            temp_dice_F = (2*(np.sum(pred*tr)) + 1e-4)/(np.sum(pred + tr) + 1e-4)

            dc_sr[j].append(temp_dice_F)

    return dc_sr, acc_sr