编写一个Python程序，使用循环神经网络（RNN）生成一些新的文本。
以下是一个示例输入文本段落：
```
The quick brown fox jumps over the lazy dog. The lazy dog is not amused.
```
以下是一个基本的 Python RNN 文本生成示例，它将使用您提供的输入文本段落：

```python
import tensorflow as tf
import numpy as np

# 设置随机种子以便于调试
tf.random.set_seed(111)

# 输入文本段落
text = 'The quick brown fox jumps over the lazy dog. The lazy dog is not amused.'

# 创建字符映射
chars = sorted(set(text))
char_to_index = {ch:i for i, ch in enumerate(chars)}
index_to_char = {i:ch for i, ch in enumerate(chars)}

# 准备训练数据
input_seq = []
output_seq = []

for i in range(0, len(text) - seq_length, step):
    input_seq.append(text[i:i + seq_length])
    output_seq.append(text[i + seq_length])
    
# 将输入序列和输出序列向量化
X = np.zeros((num_seqs, seq_length, len(chars)), dtype=np.float32)
Y = np.zeros((num_seqs, len(chars)), dtype=np.float32)

for i, seq in enumerate(input_seq):
    for t, char in enumerate(seq):
        X[i, t, char_to_index[char]] = 1
    Y[i, char_to_index[output_seq[i]]] = 1

# 定义模型
model = tf.keras.Sequential([
    tf.keras.layers.LSTM(128, input_shape=(seq_length, len(chars))),
    tf.keras.layers.Dense(len(chars), activation='softmax')
])

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy')

# 训练模型
model.fit(X, Y, batch_size=128, epochs=num_epochs)

# 生成文本
start_index = 0
generated_text = ''
sequence = text[start_index:start_index + seq_length]

for i in range(500):
    # 将序列向量化
    x_pred = np.zeros((1, seq_length, len(chars)))
    for t, char in enumerate(sequence):
        x_pred[0, t, char_to_index[char]] = 1.
        
    # 预测下一个字符
    preds = model.predict(x_pred, verbose=0)[0]
    next_index = np.argmax(preds)
    next_char = index_to_char[next_index]
    
    # 添加预测字符到生成的文本中
    generated_text += next_char
    
    # 更新序列，去除第一个字符，添加预测字符
    sequence = sequence[1:] + next_char
    
print(generated_text)
```