如何使用 Python 堆实现序列到序列模型？

import heapq

# 假设我们的词典中有以下单词
vocab = ['<pad>', '<s>', '</s>', 'p', 'i', 'd', 'a', 'n', 'c', 'o', 'e', 'm', '.']

# 定义编码器模型
def encoder(input_seq):
    # TODO: 实现编码器模型
    return encoder_output

# 定义解码器模型
def decoder(input_seq, encoder_output, prev_hidden_state):
    # TODO: 实现解码器模型
    return logits, hidden_state

# 将输入序列转换为数字序列
input_seq = [vocab.index(c) for c in 'pidancode.com']

# 初始化Beam Search算法参数
beam_size = 5
max_len = 20
prev_states = [(0, [vocab.index('<s>')], None)]
final_states = []
heap = []

# 进行Beam Search
for t in range(max_len):
    for score_b, seq_b, hidden_state_b in prev_states:
        # 将序列传递给解码器模型，获取下一个时刻的预测
        logits, hidden_state = decoder(seq_b[-1], encoder_output, hidden_state_b)
        logits = logits.squeeze().numpy()
        # 计算未出现在当前序列中的单词的概率
        log_probs = [logits[j] if j in seq_b else float('-inf') for j in range(len(vocab))]
        # 使用堆，对所有扩展后的序列按照得分进行排序
        for j in range(len(vocab)):
            new_seq = seq_b + [j]
            new_score = score_b + log_probs[j]
            item = (new_score, new_seq, hidden_state)
            heapq.heappush(heap, item)

    # 取得分最高的前beam_size个序列，作为下一时刻的备选序列
    prev_states = [heapq.heappop(heap) for _ in range(beam_size)]
    # 如果某个序列以<s>结尾，并且长度不超过max_len，则将其添加到最终结果中
    final_states += [state for state in prev_states if state[1][-1] == vocab.index('</s>') and len(state[1]) <= max_len]
    # 如果已经找到beam_size个可行的序列，则退出Beam Search
    if len(final_states) == beam_size:
        break

# 输出Beam Search得到的最终结果
for score, seq, _ in sorted(final_states, key=lambda x: x[0], reverse=True):
    seq_str = ''.join(vocab[j] for j in seq[1:-1])
    print(f"{seq_str}: {score}")