input: Write a python function to calculate the accuracy of a Multinomial Naive Bayes classifier model on a given dataset.
  assert calculate_accuracy("emails.csv") > 0.9
  assert calculate_accuracy("another_emails_dataset.csv") > 0.85
  assert calculate_accuracy("yet_another_emails_dataset.csv") > 0.8
output: def calculate_accuracy(dataset_path):
      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 accuracy_score
  
      # Load the dataset
      data = pd.read_csv(dataset_path)
      
      # Assuming the dataset has 'text' and 'label' columns
      X = data['text']
      y = data['label']
      
      # Convert text data into numerical data
      vectorizer = CountVectorizer()
      X_vect = vectorizer.fit_transform(X)
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X_vect, y, test_size=0.2, random_state=42)
      
      # Train the Multinomial Naive Bayes classifier
      model = MultinomialNB()
      model.fit(X_train, y_train)
      
      # Predict the labels for the test set
      y_pred = model.predict(X_test)
      
      # Calculate the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy