import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset, random_split
import os
import numpy as np
import matplotlib.pyplot as plt
import cv2
def load_data(pix, use_type='train'):
    datasets = list()
    file_list = [x for x in os.listdir(f"./out_mat/{pix}/{use_type}/") if x.endswith('.npy')][:3000]
    for file in file_list:
        file_img = np.load(f"./out_mat/{pix}/{use_type}/{file}")[:,:,:1]
        datasets.append(file_img)
    return np.asarray(datasets)
train_set = load_data(96, 'train')
val_set = load_data(96, 'valid')
test_set = load_data(96, 'test')

def load_mask(mask_rate):
    mask_files = os.listdir(f'./out_mat/96/mask/{mask_rate}')
    masks = list()
    for file in mask_files:
        d = cv2.imread(f'./out_mat/96/mask/{mask_rate}/{file}', cv2.IMREAD_GRAYSCALE)
        d = (d > 0) * 1
        masks.append(d)
    return np.asarray(masks)

masks = load_mask(20)

maxs = train_set.max(axis=0)
mins = train_set.min(axis=0)

len(train_set)

norm_train = (train_set - mins) / (maxs-mins)
del train_set
norm_valid = (val_set - mins) / (maxs-mins)
del val_set
norm_test = (test_set - mins) / (maxs-mins)
del test_set
norm_train.shape

trans_train = np.transpose(norm_train, (0, 3, 1, 2))
trans_val = np.transpose(norm_valid, (0, 3, 1, 2))
trans_test = np.transpose(norm_test, (0, 3, 1, 2))

# 可视化特定特征的函数
def visualize_feature(input_feature,masked_feature, output_feature, title):
    plt.figure(figsize=(12, 6))
    plt.subplot(1, 3, 1)
    plt.imshow(input_feature[0].cpu().numpy())
    plt.title(title + " Input")
    plt.subplot(1, 3, 2)
    plt.imshow(masked_feature[0].cpu().numpy())
    plt.title(title + " Masked")
    plt.subplot(1, 3, 3)
    plt.imshow(output_feature[0].detach().cpu().numpy())
    plt.title(title + " Recovery")
    plt.show()

# 设置随机种子以确保结果的可重复性
torch.manual_seed(0)
np.random.seed(0)

# 数据准备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
# 将numpy数组转换为PyTorch张量
tensor_train = torch.tensor(trans_train.astype(np.float32), device=device)
tensor_valid = torch.tensor(trans_val.astype(np.float32), device=device)
tensor_test = torch.tensor(trans_test.astype(np.float32), device=device)

# 创建一个数据集和数据加载器
train_set = TensorDataset(tensor_train, tensor_train)  # 输出和标签相同，因为我们是自编码器
val_set = TensorDataset(tensor_valid, tensor_valid)
test_set = TensorDataset(tensor_test, tensor_test)
batch_size = 64
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)

def mask_data(data, device, masks):
    mask_inds = np.random.choice(masks.shape[0], data.shape[0])
    mask = torch.from_numpy(masks[mask_inds]).to(device)
    tmp_first_channel = data[:, 0, :, :] * mask
    masked_data = torch.clone(data)
    masked_data[:, 0, :, :] = tmp_first_channel
    return masked_data

class SEBlock(nn.Module):
    def __init__(self, in_channels, reduced_dim):
        super(SEBlock, self).__init__()
        self.se = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(in_channels, reduced_dim, kernel_size=1),
            nn.ReLU(),
            nn.Conv2d(reduced_dim, in_channels, kernel_size=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        return x * self.se(x)

class Conv(nn.Sequential):
    def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, bias=False):
        super(Conv, self).__init__(
            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
                      dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2)
        )

class ConvBNReLU(nn.Sequential):
    def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d, bias=False):
        super(ConvBNReLU, self).__init__(
            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
                      dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),
            norm_layer(out_channels),
            nn.ReLU()
        )

class SeparableBNReLU(nn.Sequential):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, norm_layer=nn.BatchNorm2d):
        super(SeparableBNReLU, self).__init__(
            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation,
                      padding=((stride - 1) + dilation * (kernel_size - 1)) // 2, groups=in_channels, bias=False),
            norm_layer(out_channels),
            nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=False),
            nn.ReLU6()
        )

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample
        if in_channels != out_channels or stride != 1:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )

    def forward(self, x):
        identity = x
        if self.downsample is not None:
            identity = self.downsample(x)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += identity
        out = self.relu(out)
        return out

class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU6, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Conv2d(in_features, hidden_features, 1, 1, 0, bias=True)
        self.act = act_layer()
        self.fc2 = nn.Conv2d(hidden_features, out_features, 1, 1, 0, bias=True)
        self.drop = nn.Dropout(drop, inplace=True)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x

class MultiHeadAttentionBlock(nn.Module):
    def __init__(self, embed_dim, num_heads, dropout=0.1):
        super(MultiHeadAttentionBlock, self).__init__()
        self.attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)
        self.norm = nn.LayerNorm(embed_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        B, C, H, W = x.shape
        x = x.view(B, C, H * W).permute(2, 0, 1)  # (B, C, H, W) -> (HW, B, C)
        attn_output, _ = self.attention(x, x, x)
        attn_output = self.norm(attn_output)
        attn_output = self.dropout(attn_output)
        attn_output = attn_output.permute(1, 2, 0).view(B, C, H, W)
        return attn_output

class SpatialAttentionBlock(nn.Module):
    def __init__(self):
        super(SpatialAttentionBlock, self).__init__()
        self.conv = nn.Conv2d(2, 1, kernel_size=7, padding=3, bias=False)

    def forward(self, x): #(B, 64, H, W)
        avg_out = torch.mean(x, dim=1, keepdim=True) #(B, 1, H, W)
        max_out, _ = torch.max(x, dim=1, keepdim=True)#(B, 1, H, W)
        out = torch.cat([avg_out, max_out], dim=1)#(B, 2, H, W)
        out = torch.sigmoid(self.conv(out))#(B, 1, H, W)
        return x * out #(B, C, H, W)

class DecoderAttentionBlock(nn.Module):
    def __init__(self, in_channels):
        super(DecoderAttentionBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, in_channels // 2, kernel_size=1)
        self.conv2 = nn.Conv2d(in_channels // 2, in_channels, kernel_size=1)
        self.spatial_attention = SpatialAttentionBlock()

    def forward(self, x):
        # 通道注意力
        b, c, h, w = x.size()
        avg_pool = F.adaptive_avg_pool2d(x, 1)
        max_pool = F.adaptive_max_pool2d(x, 1)

        avg_out = self.conv1(avg_pool)
        max_out = self.conv1(max_pool)

        out = avg_out + max_out
        out = torch.sigmoid(self.conv2(out))

        # 添加空间注意力
        out = x * out
        out = self.spatial_attention(out)
        return out


class MaskedAutoencoder(nn.Module):
    def __init__(self):
        super(MaskedAutoencoder, self).__init__()
        self.encoder = nn.Sequential(
            Conv(1, 32, kernel_size=3, stride=2),
            nn.ReLU(),
            SEBlock(32,32),
            ConvBNReLU(32, 64, kernel_size=3, stride=2),
            ResidualBlock(64,64),
            SeparableBNReLU(64, 128, kernel_size=3, stride=2),
            MultiHeadAttentionBlock(embed_dim=128, num_heads=4),
            SEBlock(128, 128)
        )
        self.mlp = Mlp(in_features=128, hidden_features=256, out_features=128, act_layer=nn.ReLU6, drop=0.1)
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(128, 32, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            DecoderAttentionBlock(32),
            nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            DecoderAttentionBlock(16),
            nn.ReLU(),
            nn.ConvTranspose2d(16, 1, kernel_size=3, stride=2, padding=1, output_padding=1),  # 修改为 output_padding=1
            nn.Sigmoid()
        )

    def forward(self, x):
        encoded = self.encoder(x)
        print("Encoded size:", encoded.size())
        decoded = self.decoder(encoded)
        print("Encoded size:", decoded.size())
        return decoded

# 实例化模型、损失函数和优化器
model = MaskedAutoencoder()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练函数
def train_epoch(model, device, data_loader, criterion, optimizer):
    model.train()
    running_loss = 0.0
    for batch_idx, (data, _) in enumerate(data_loader):
        masked_data = mask_data(data, device, masks)
        optimizer.zero_grad()
        reconstructed = model(masked_data)
        loss = criterion(reconstructed, data)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / (batch_idx + 1)
# 评估函数
def evaluate(model, device, data_loader, criterion):
    model.eval()
    running_loss = 0.0
    with torch.no_grad():
        for batch_idx, (data, _) in enumerate(data_loader):
            data = data.to(device)
            masked_data = mask_data(data, device, masks)
            reconstructed = model(masked_data)
            if batch_idx == 8:
                rand_ind = np.random.randint(0, len(data))
                visualize_feature(data[rand_ind], masked_data[rand_ind], reconstructed[rand_ind], title='NO_2')
            loss = criterion(reconstructed, data)
            running_loss += loss.item()
    return running_loss / (batch_idx + 1)
# 测试函数
def test(model, device, data_loader):
    model.eval()
    with torch.no_grad():
        for batch_idx, (data, _) in enumerate(data_loader):
            data = data.to(device)
            masked_data = mask_data(data, device, masks)
            masked_ind = np.argwhere(masked_data[0][0]==0)
            reconstructed = model(masked_data)
            recon_no2 = reconstructed[0][0]
            ori_no2 = data[0][0]
    return
model = model.to(device)

num_epochs = 100
train_losses = list()
val_losses = list()
for epoch in range(num_epochs):
    train_loss = train_epoch(model, device, train_loader, criterion, optimizer)
    train_losses.append(train_loss)
    val_loss = evaluate(model, device, val_loader, criterion)
    val_losses.append(val_loss)
    print(f'Epoch {epoch+1}, Train Loss: {train_loss}, Val Loss: {val_loss}')

# 测试模型
test_loss = evaluate(model, device, test_loader, criterion)
print(f'Test Loss: {test_loss}')