input: Write a function to evaluate the accuracy of a machine-learning model using a Support Vector Classifier (SVC). The function should take training data (X_train, y_train) and testing data (X_test, y_test) as inputs, and return the accuracy of the model on the test data. If the input data is empty, return 0.0.
  assert evaluate_model_accuracy(X_train=[[1,2],[3,4]], y_train=[1,0], X_test=[[5,6],[7,8]], y_test=[1,0])==1.0
  assert evaluate_model_accuracy(X_train=[[1,1],[2,2],[3,3]], y_train=[0,1,0], X_test=[[1,1],[2,2]], y_test=[0,1])==0.5
  assert evaluate_model_accuracy(X_train=[], y_train=[], X_test=[], y_test=[])==0.0
output: def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      if not X_train or not X_test:
          return 0.0
      model = SVC()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)