input: Write a python function to train a support vector classifier (SVC) and evaluate its accuracy. 
  assert train_and_evaluate_svc(X, y) >= 0 and train_and_evaluate_svc(X, y) <= 100
  assert isinstance(train_and_evaluate_svc(X, y), float)
output: def train_and_evaluate_svc(X, y):
      from sklearn.model_selection import train_test_split
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Create and train the SVC model
      model = SVC()
      model.fit(X_train, y_train)
      
      # Predict the labels for the test set
      y_pred = model.predict(X_test)
      
      # Calculate and return the accuracy
      accuracy = accuracy_score(y_test, y_pred) * 100  # Convert to percentage
      return accuracy