Why is stratifiedkfold generating the same splits in spite of using different random_state values?

I am trying to generate different stratified splits of my data set using stratifiedkfold split and random_state parameter. However, when I use different random_state values, I still get the same splits. My understanding is that by using different random_state values, you will be able to generate different splits. Please let me know what I am doing incorrectly. Here is the code.

import numpy as np
X_train=np.ones(10)
Y_train=np.ones(10)

from sklearn.model_selection import StratifiedKFold
skf = StratifiedKFold(n_splits=5,random_state=0)
skf1 = StratifiedKFold(n_splits=5,random_state=100)


trn1=[]
cv1=[]
for train, cv in skf.split(X_train, Y_train):
    trn1=trn1+[train]
    cv1=cv1+[cv]

trn2=[]
cv2=[]
for train, cv in skf1.split(X_train, Y_train):
    trn2=trn2+[train]
    cv2=cv2+[cv]


for c in list(range(0,5)):
    print('Fold:'+str(c+1))
    print(trn1[c])
    print(trn2[c])
    print(cv1[c])
    print(cv2[c])

Here is the output

Fold:1
[2 3 4 5 6 7 8 9]
[2 3 4 5 6 7 8 9]
[0 1]
[0 1]
Fold:2
[0 1 4 5 6 7 8 9]
[0 1 4 5 6 7 8 9]
[2 3]
[2 3]
Fold:3
[0 1 2 3 6 7 8 9]
[0 1 2 3 6 7 8 9]
[4 5]
[4 5]
Fold:4
[0 1 2 3 4 5 8 9]
[0 1 2 3 4 5 8 9]
[6 7]
[6 7]
Fold:5
[0 1 2 3 4 5 6 7]
[0 1 2 3 4 5 6 7]
[8 9]
[8 9]