matplotlib: histogram of SMOTEd class distribution showing colored synthetic region

Say I have a binary imbalanced dataset like so:

from collections import Counter
from sklearn.datasets import make_classification
from matplotlib import pyplot as plt
from imblearn.over_sampling import SMOTE

# fake dataset
X, y = make_classification(n_samples=10000, n_features=2, n_redundant=0,
    n_clusters_per_class=1, weights=[0.99], flip_y=0, random_state=1)
# summarize class distribution
counter = Counter(y)

print(counter)
Counter({0: 9900, 1: 100})

Using SMOTE to oversample minority class:

oversample = SMOTE()
Xs, ys = oversample.fit_resample(X, y)

Now, to show a histogram of class distribution:

a. before oversample:

plt.hist(y)

b. after oversampled:

plt.hist(ys)

But I would like to show in the oversampled plot, portion of the minority class generated in a different color.

Expected output:

Similar to the figure below:

You can use plt.bar for a bar plot. By drawing two bar plots onto the same subplot, the first still is partially visible.

import matplotlib.pyplot as plt
import numpy as np

# simulate before oversampling
y = np.random.choice([0, 1], 1000, p=[.95, .05])
# simulate after oversampling
ys = np.append(y, np.ones(sum(y == 0) - sum(y == 1), dtype=int))

plt.bar([0, 1], height=[sum(ys == 0), sum(ys == 1)], color=['cornflowerblue', 'lime'])
plt.bar([0, 1], height=[sum(y == 0), sum(y == 1)], color='cornflowerblue')
plt.xticks([0, 1])
plt.show()