input: Write a python function to create and train an RNN model to generate text based on input sentences using TensorFlow and Keras.
  assert isinstance(create_and_train_model(["Snow was steadily falling, blanketing the ground in white."]), str)
  assert isinstance(create_and_train_model(["A gentle wind was blowing in from the nearby mountains."]), str)
  assert isinstance(create_and_train_model(["The sky was a foggy grey, with no sign of the sun."]), str)
output: import tensorflow as tf
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Embedding, SimpleRNN, Dense
  from tensorflow.keras.preprocessing.text import Tokenizer
  from tensorflow.keras.preprocessing.sequence import pad_sequences
  import numpy as np
  
  def create_and_train_model(sentences):
      # Tokenize the sentences
      tokenizer = Tokenizer()
      tokenizer.fit_on_texts(sentences)
      total_words = len(tokenizer.word_index) + 1
  
      input_sequences = []
      for sentence in sentences:
          token_list = tokenizer.texts_to_sequences([sentence])[0]
          for i in range(1, len(token_list)):
              n_gram_sequence = token_list[:i+1]
              input_sequences.append(n_gram_sequence)
  
      # Pad sequences
      max_sequence_len = max([len(x) for x in input_sequences])
      input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))
  
      # Create predictors and label
      X, y = input_sequences[:,:-1], input_sequences[:,-1]
      y = tf.keras.utils.to_categorical(y, num_classes=total_words)
  
      # Define the model
      model = Sequential()
      model.add(Embedding(total_words, 10, input_length=max_sequence_len-1))
      model.add(SimpleRNN(100))
      model.add(Dense(total_words, activation='softmax'))
  
      # Compile the model
      model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      # Train the model
      model.fit(X, y, epochs=100, verbose=0)
  
      # Generate text
      def generate_text(seed_text, next_words):
          for _ in range(next_words):
              token_list = tokenizer.texts_to_sequences([seed_text])[0]
              token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
              predicted = np.argmax(model.predict(token_list, verbose=0), axis=-1)
              output_word = ""
              for word, index in tokenizer.word_index.items():
                  if index == predicted:
                      output_word = word
                      break
              seed_text += " " + output_word
          return seed_text
  
      return generate_text(sentences[0], 10)