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 // 4, width_kernel // 2, width_kernel,  width_kernel, width_node ** 2], torch.nn.ReLU)
        # kernel = DenseNet([ker_in, width_kernel // 2, width_kernel, width_node**2], torch.nn.ReLU)
        # kernel = DenseNet([ker_in, 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 = self.conv1(x, edge_index, edge_attr)
            if k != self.depth-1:
                x = F.relu(x)

        x = self.fc2(x)
        return x


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

for ker_width in (256,):
    r = 1
    s = int(((241 - 1)/r) + 1)
    n = s**2
    m = 200
    k = 2

    radius_train = 0.25
    radius_test = 0.25
    print('resolution', s)


    ntrain = 100
    ntest = 100

    batch_size = 5
    batch_size2 = 5
    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 = 'UAI8_s'+str(s)+'_ker_width'+ str(ker_width)+'_depth5_'
    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(ker_width, 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()