input: Write a function to evaluate the accuracy of a machine-learning model using a Support Vector Classifier (SVC) from the sklearn library. The function should take training and testing datasets as inputs, fit the model, make predictions, and return the accuracy of the model. If the input data is empty, the function should return a message indicating that the data is empty.
  assert evaluate_model_accuracy([], [], [], []) == "Data is empty."
  assert evaluate_model_accuracy(X_train, y_train, X_test, y_test) == 0.95
  assert evaluate_model_accuracy(np.array([[1, 2], [2, 3]]), np.array([0, 1]), np.array([[1, 2]]), np.array([0])) == 1.0
  assert evaluate_model_accuracy(np.array([[1, 2], [2, 3], [3, 4]]), np.array([0, 1, 0]), np.array([[1, 2], [3, 4]]), np.array([0, 0])) == 0.5
  assert evaluate_model_accuracy(X_train, y_train, X_test, np.array([])) == "Data is empty."
output: def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      if len(X_train) == 0 or len(y_train) == 0 or len(X_test) == 0 or len(y_test) == 0:
          return "Data is empty."
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      model = SVC()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)