import torch
from fairseq.models.transformer import TransformerModel

# 数据准备
source_data = "path/to/source.txt"
target_data = "path/to/target.txt"

# 数据预处理
preprocessed_source = "path/to/preprocessed_source.txt"
preprocessed_target = "path/to/preprocessed_target.txt"

# 进行数据预处理的代码，将原始文本分词、标记化等处理，并保存到preprocessed_source和preprocessed_target中

# 构建词汇表
vocab_source = "path/to/vocab_source.txt"
vocab_target = "path/to/vocab_target.txt"

# 构建词汇表的代码，根据preprocessed_source和preprocessed_target构建词汇表，并保存到vocab_source和vocab_target中

# 训练模型
model = TransformerModel.from_pretrained(
    model_name_or_path="transformer.wmt19.en-de",
    checkpoint_file="model.pt",
    data_name_or_path=".",
    source_lang="en",
    target_lang="de"
)

# 定义训练数据集
train_dataset = torch.utils.data.Dataset()

# 加载训练数据集的代码，根据preprocessed_source和preprocessed_target加载训练数据集

# 训练模型的代码
model.train()

# 初始化训练参数、优化器等

for epoch in range(num_epochs):
    for batch in train_dataset:
        # 数据前向传播、计算损失
        loss = model.forward(batch)
        
        # 损失反向传播、参数更新
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

# 应用模型
def translate_text(source_text):
    tokens = model.encode(source_text)
    translation = model.generate(tokens)
    translation_text = model.decode(translation)
    return translation_text

# 使用示例
source_text = "Hello, how are you?"
translation = translate_text(source_text)
print(translation)