import torch
import torch.nn as nn

from textCNN_data import textCNN_data, textCNN_param, dataLoader_param
from torch.utils.data import DataLoader
from multihead_attention import my_model
import os
from torch.nn import functional as F

os.environ["CUDA_VISIBLE_DEVICES"] = "0"


def validation(model, val_dataLoader, device):
    model.eval()
    total = 0
    correct = 0
    with torch.no_grad():
        for i, (clas, sentences) in enumerate(val_dataLoader):
            try:
                # sentences = sentences.type(torch.LongTensor).to(device)
                # clas = clas.type(torch.LongTensor).to(device)
                out = model(
                    sentences.to(
                        device))  # out: batch size 64 x sentences length 20 x word dimension 4(after my_linear)
                # out = F.relu(out.squeeze(-3))
                # out = F.max_pool1d(out, out.size(2)).squeeze(2)
                # softmax = nn.Softmax(dim=1)

                pred = torch.argmax(out, dim=1)  # 64x4 -> 64x1

                correct += (pred == clas.to(device)).sum()
                total += clas.size()[0]
            except IndexError as e:
                print(i)
                print('clas', clas)
                print('clas size', clas.size())
                print('sentence', sentences)
                print('sentences size', sentences.size())
                print(e)
                print(e.__traceback__)
                exit()

    acc = correct / total
    return acc


# seed = 66666666
# torch.cuda.manual_seed(seed)
# torch.manual_seed(seed)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
print(torch.cuda.get_device_name())
if torch.cuda.is_available():
    device = 'cuda:0'
else:
    device = 'cpu'

# device = 'cpu'


# init dataset
print('init dataset...')
trainDataFile = 'traindata_vec.txt'
valDataFile = 'devdata_vec.txt'
train_dataset = textCNN_data(trainDataFile)
train_dataLoader = DataLoader(train_dataset,
                              batch_size=dataLoader_param['batch_size'],
                              shuffle=True)

val_dataset = textCNN_data(valDataFile)
val_dataLoader = DataLoader(val_dataset,
                            batch_size=dataLoader_param['batch_size'],
                            # batch size 64
                            shuffle=False)

if __name__ == "__main__":
    # 设置随机种子，保证结果可复现

    # init net
    print('init net...')
    model = my_model()
    model.to(device)
    print(model)
    optimizer = torch.optim.Adam(model.parameters(), lr=0.0005)
    criterion = nn.CrossEntropyLoss()

    print("training...")

    best_dev_acc = 0
    # embed.train()
    for epoch in range(100):
        model.train()
        for i, (clas, sentences) in enumerate(train_dataLoader):
            # sentences: batch size 64 x sentence length 20 x embed dimension 128
            # 一个字是个128维vector 一句话是个 20x128的2D tensor 一个batch有64句话是个 64x20x128的3D tensor

            out = model(sentences.to(
                device))  # out: batch size 64 x word vector 4 (after my_linear)
            try:
                loss = criterion(out, clas.to(device))
            except:
                print(out.size(), out)
                print(clas.size(), clas)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            if (i + 1) % 10 == 0:
                print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
        model.eval()
        dev_acc = validation(model=model, val_dataLoader=val_dataLoader,
                             device=device)

        if best_dev_acc < dev_acc:
            best_dev_acc = dev_acc
            print("save model...")
            torch.save(model.state_dict(), "model.bin")
            print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
        print("best dev acc %.4f  dev acc %.4f" % (best_dev_acc, dev_acc))