Why confusion matrix shows different results from random undersampling class distribution?

Question

I have an imbalanced dataset that consists of 17 numerical features and 3 classes for output. I applied random undersampling and obtained the following confusion matrix with undersampling.

My question; when random undersampling show 33 numbers for each class why the confusion matrix shows more than 33?

#Raw Data Distribution

layers_counts=y.value_counts()
layers_counts

#Output

 2 498
 1 116
 0  39

 from imblearn.under_sampling import RandomUnderSampler
 rus=RandomUnderSampler(sampling_strategy="not minority")
 X_rus, y_rus = rus.fit_resample(Xtrain, ytrain)
 y_rus.value_counts()

 #Output

 0  33
 1  33
 2  33
 
 from sklearn.linear_model import LogisticRegression
 classifier=LogisticRegression()
 classifier.fit(X_rus, y_rus)
 
 ypred=classifier.predict(Xtest)
 
 from sklearn.metrics import confusion_matrix
 cm = confusion_matrix (y_test,ypred)
 cm_df2 = pd.DataFrame(cm,
                 index = ['VCS','VSG','VG'], 
                 columns = ['VCS','VSG','VG'])
 plt.figure(figsize=(8,6))
 sns.heatmap(cm_df2, annot=True)
 plt.title('Confusion Matrix')
 plt.ylabel('Actal Values')
 plt.xlabel('Predicted Values')
 plt.show()

When the rus provided 33 numbers for each class the confusion matrix is shown in the following, but I think it should be matched with 33? I am confused about that point, could you help me to understand?