input: Write a python function to load a CSV file, vectorize the text data, convert target labels to numerical values, split the data into train and test sets, train a Multinomial Naive Bayes model, and print the classification report.
  ```python
  import pandas as pd
  import numpy as np
  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
  
  def process_and_classify(file_path):
      # Load the data
      data = pd.read_csv(file_path)
  
      # Vectorize the text
      cv = CountVectorizer()
      X = cv.fit_transform(data["text"])
  
      # Convert target labels to numerical values
      y = np.array([1 if label == "category1" else 2 if label == "category2" else 3 for label in data["category"]])
  
      # Split into train and test data
      X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
  
      # Train the model
      nb = MultinomialNB()
      nb.fit(X_train, y_train)
  
      # Test the model
      y_pred = nb.predict(X_test)
  
      # Print out the results
      report = classification_report(y_test, y_pred)
      print(report)
      return report
  
  # Test cases
  assert process_and_classify("test_data1.csv") is not None
  assert process_and_classify("test_data2.csv") is not None
output: def process_and_classify(file_path):
      # Load the data
      data = pd.read_csv(file_path)
  
      # Vectorize the text
      cv = CountVectorizer()
      X = cv.fit_transform(data["text"])
  
      # Convert target labels to numerical values
      y = np.array([1 if label == "category1" else 2 if label == "category2" else 3 for label in data["category"]])
  
      # Split into train and test data
      X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
  
      # Train the model
      nb = MultinomialNB()
      nb.fit(X_train, y_train)
  
      # Test the model
      y_pred = nb.predict(X_test)
  
      # Print out the results
      report = classification_report(y_test, y_pred)
      print(report)
      return report
  
  # Test cases
  assert process_and_classify("test_data1.csv") is not None
  assert process_and_classify("test_data2.csv") is not None