input: Write a function to generate a machine learning program in Python that classifies text data into 3 different categories using a Naive Bayes classifier. The function should load data from a CSV file, vectorize the text data, convert target labels to numerical values, split the data into training and testing sets, train the model, test the model, and print out a classification report. The function should be named `classify_text_data`.
  
  
  assert classify_text_data()=="              precision    recall  f1-score   support\n\n           1       0.89      0.92      0.90       100\n           2       0.85      0.83      0.84       105\n           3       0.91      0.90      0.91       102\n\navg / total       0.88      0.88      0.88       307\n"
  
output: def classify_text_data():
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
  
      data = pd.read_csv('data.csv')
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data['text'])
      y = data['category'].map({'A': 1, 'B': 2, 'C': 3})
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      model = MultinomialNB()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
  
      return classification_report(y_test, y_pred, target_names=['1', '2', '3'])