input: Write a python function to train a Naive Bayes text classifier and return its accuracy.
  assert TextClassifier(x_train, y_train, x_test, y_test) == 0.9  # Assuming this as a hypothetical accuracy for test case
  assert TextClassifier(x_train_2, y_train_2, x_test_2, y_test_2) == 0.85  # Another hypothetical accuracy
  assert TextClassifier(x_train_3, y_train_3, x_test_3, y_test_3) >= 0.75  # Accuracy should be 0.75 or higher
output: from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.pipeline import make_pipeline
  from sklearn.metrics import accuracy_score
  
  def TextClassifier(x_train, y_train, x_test, y_test):
      model = make_pipeline(CountVectorizer(), MultinomialNB())
      model.fit(x_train, y_train)
      predictions = model.predict(x_test)
      return accuracy_score(y_test, predictions)