input: Write a function to evaluate the accuracy of a machine-learning model using a Support Vector Classifier from the sklearn library. The function should fit the model with given training data and compute the accuracy score using the test data. If the training or test data is not provided, raise a ValueError with a corresponding message.
  assert evaluate_model_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test) == 0.95
  assert evaluate_model_accuracy(SVC(kernel='rbf', gamma='scale'), X_train, y_train, X_test, y_test) == 0.97
  assert evaluate_model_accuracy(SVC(kernel='linear'), None, y_train, X_test, y_test) == "ValueError: Training data not provided"
  assert evaluate_model_accuracy(SVC(kernel='linear'), X_train, y_train, None, y_test) == "ValueError: Test data not provided"
output: def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      if X_train is None or y_train is None:
          raise ValueError("Training data not provided")
      if X_test is None or y_test is None:
          raise ValueError("Test data not provided")
      model.fit(X_train, y_train)
      return model.score(X_test, y_test)