import torch
import numpy as np

import torch.nn.functional as F
import torch.nn as nn

from torch_geometric.data import Data, DataLoader
import matplotlib.pyplot as plt
from utilities import *
from nn_conv import NNConv, NNConv_old

from timeit import default_timer
import scipy.io


class KernelNN3(torch.nn.Module):
    def __init__(self, width_node, width_kernel, depth, ker_in, in_width=1, out_width=1):
        super(KernelNN3, self).__init__()
        self.depth = depth

        self.fc1 = torch.nn.Linear(in_width, width_node)

        kernel = DenseNet([ker_in, width_kernel // 2, width_kernel, width_node**2], torch.nn.ReLU)
        self.conv1 = NNConv_old(width_node, width_node, kernel, aggr='mean')

        self.fc2 = torch.nn.Linear(width_node, 1)

    def forward(self, data):
        x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
        x = self.fc1(x)
        for k in range(self.depth):
            x = F.relu(self.conv1(x, edge_index, edge_attr))

        x = self.fc2(x)
        return x


TRAIN_PATH = 'data/piececonst_r241_N1024_smooth1.mat'
TEST_PATH = 'data/piececonst_r241_N1024_smooth2.mat'

for m in (100, 200, 400):
    for radius_train in (0.05, 0.15, 0.4):
        r = 2
        s = int(((241 - 1)/r) + 1)
        n = s**2
        # m = 200
        k = 5

        # radius_train = 0.15
        radius_test = radius_train
        print('resolution', s)


        ntrain = 100
        ntest = 100

        batch_size = 10
        batch_size2 = 10

        if radius_train == 0.4 and m==400:
            batch_size = 2
            batch_size2 = 2
        if radius_train == 0.4 and m == 200:
            batch_size = 5
            batch_size2 = 5
        # else:

        width = 64
        ker_width = 1000
        depth = 6
        edge_features = 6
        node_features = 6

        epochs = 200
        learning_rate = 0.0001
        scheduler_step = 50
        scheduler_gamma = 0.5

        path = 'UAI6_s'+str(s)+'_m'+ str(m)+'_radius'+ str(radius_train)
        path_model = 'model/'+ path
        path_train_err = 'results/'+ path + 'train.txt'
        path_test_err = 'results/'+ path + 'test.txt'
        path_image = 'results/'+ path


        t1 = default_timer()


        reader = MatReader(TRAIN_PATH)
        train_a = reader.read_field('coeff')[:ntrain,::r,::r].reshape(ntrain,-1)
        train_a_smooth = reader.read_field('Kcoeff')[:ntrain,::r,::r].reshape(ntrain,-1)
        train_a_gradx = reader.read_field('Kcoeff_x')[:ntrain,::r,::r].reshape(ntrain,-1)
        train_a_grady = reader.read_field('Kcoeff_y')[:ntrain,::r,::r].reshape(ntrain,-1)
        train_u = reader.read_field('sol')[:ntrain,::r,::r].reshape(ntrain,-1)

        reader.load_file(TEST_PATH)
        test_a = reader.read_field('coeff')[:ntest,::r,::r].reshape(ntest,-1)
        test_a_smooth = reader.read_field('Kcoeff')[:ntest,::r,::r].reshape(ntest,-1)
        test_a_gradx = reader.read_field('Kcoeff_x')[:ntest,::r,::r].reshape(ntest,-1)
        test_a_grady = reader.read_field('Kcoeff_y')[:ntest,::r,::r].reshape(ntest,-1)
        test_u = reader.read_field('sol')[:ntest,::r,::r].reshape(ntest,-1)


        a_normalizer = GaussianNormalizer(train_a)
        train_a = a_normalizer.encode(train_a)
        test_a = a_normalizer.encode(test_a)
        as_normalizer = GaussianNormalizer(train_a_smooth)
        train_a_smooth = as_normalizer.encode(train_a_smooth)
        test_a_smooth = as_normalizer.encode(test_a_smooth)
        agx_normalizer = GaussianNormalizer(train_a_gradx)
        train_a_gradx = agx_normalizer.encode(train_a_gradx)
        test_a_gradx = agx_normalizer.encode(test_a_gradx)
        agy_normalizer = GaussianNormalizer(train_a_grady)
        train_a_grady = agy_normalizer.encode(train_a_grady)
        test_a_grady = agy_normalizer.encode(test_a_grady)

        u_normalizer = UnitGaussianNormalizer(train_u)
        train_u = u_normalizer.encode(train_u)
        # test_u = y_normalizer.encode(test_u)



        meshgenerator = RandomMeshGenerator([[0,1],[0,1]],[s,s], sample_size=m)
        data_train = []
        for j in range(ntrain):
            for i in range(k):
                idx = meshgenerator.sample()
                grid = meshgenerator.get_grid()
                edge_index = meshgenerator.ball_connectivity(radius_train)
                edge_attr = meshgenerator.attributes(theta=train_a[j,:])
                #data_train.append(Data(x=init_point.clone().view(-1,1), y=train_y[j,:], edge_index=edge_index, edge_attr=edge_attr))
                data_train.append(Data(x=torch.cat([grid, train_a[j, idx].reshape(-1, 1),
                                                    train_a_smooth[j, idx].reshape(-1, 1), train_a_gradx[j, idx].reshape(-1, 1),
                                                    train_a_grady[j, idx].reshape(-1, 1)
                                                    ], dim=1),
                                       y=train_u[j, idx], edge_index=edge_index, edge_attr=edge_attr, sample_idx=idx
                                       ))


        meshgenerator = RandomMeshGenerator([[0,1],[0,1]],[s,s], sample_size=m)
        data_test = []
        for j in range(ntest):
            idx = meshgenerator.sample()
            grid = meshgenerator.get_grid()
            edge_index = meshgenerator.ball_connectivity(radius_test)
            edge_attr = meshgenerator.attributes(theta=test_a[j,:])
            data_test.append(Data(x=torch.cat([grid, test_a[j, idx].reshape(-1, 1),
                                               test_a_smooth[j, idx].reshape(-1, 1), test_a_gradx[j, idx].reshape(-1, 1),
                                               test_a_grady[j, idx].reshape(-1, 1)
                                               ], dim=1),
                                  y=test_u[j, idx], edge_index=edge_index, edge_attr=edge_attr, sample_idx=idx
                                  ))
        #
        train_loader = DataLoader(data_train, batch_size=batch_size, shuffle=True)
        test_loader = DataLoader(data_test, batch_size=batch_size2, shuffle=False)

        t2 = default_timer()

        print('preprocessing finished, time used:', t2-t1)
        device = torch.device('cuda')

        model = KernelNN3(width, ker_width,depth,edge_features,in_width=node_features).cuda()
        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)
        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=scheduler_step, gamma=scheduler_gamma)

        myloss = LpLoss(size_average=False)
        u_normalizer.cuda()
        ttrain = np.zeros((epochs, ))
        ttest = np.zeros((epochs,))
        model.train()
        for ep in range(epochs):
            t1 = default_timer()
            train_mse = 0.0
            train_l2 = 0.0
            for batch in train_loader:
                batch = batch.to(device)

                optimizer.zero_grad()
                out = model(batch)
                mse = F.mse_loss(out.view(-1, 1), batch.y.view(-1,1))
                mse.backward()

                l2 = myloss(
                    u_normalizer.decode(out.view(batch_size, -1), sample_idx=batch.sample_idx.view(batch_size, -1)),
                    u_normalizer.decode(batch.y.view(batch_size, -1), sample_idx=batch.sample_idx.view(batch_size, -1)))
                optimizer.step()
                train_mse += mse.item()
                train_l2 += l2.item()

            scheduler.step()
            t2 = default_timer()

            model.eval()
            test_l2 = 0.0
            with torch.no_grad():
                for batch in test_loader:
                    batch = batch.to(device)
                    out = model(batch)
                    out = u_normalizer.decode(out.view(batch_size2,-1), sample_idx=batch.sample_idx.view(batch_size2,-1))
                    test_l2 += myloss(out, batch.y.view(batch_size2, -1)).item()
                    # test_l2 += myloss(out.view(batch_size2,-1), y_normalizer.encode(batch.y.view(batch_size2, -1))).item()

            ttrain[ep] = train_l2/(ntrain * k)
            ttest[ep] = test_l2/ntest

            print(m, radius_train, ep, t2-t1, train_mse/len(train_loader), train_l2/(ntrain * k), test_l2/ntest)

        np.savetxt(path_train_err, ttrain)
        np.savetxt(path_test_err, ttest)
        torch.save(model, path_model)

        plt.figure()
        # plt.plot(ttrain, label='train loss')
        plt.plot(ttest, label='test loss')
        plt.legend(loc='upper right')
        plt.show()