Different values of Precision, Recall and AUROC, Confusion Matrix vis-a-vis SKLEARN

[sraghavm]

import torchmetrics
from sklearn.metrics import accuracy_score,precision_score,recall_score,matthews_corrcoef,confusion_matrix,roc_auc_score
import pandas as pd
import torch
import numpy as np

preds = [1,0,0,1,0,1,1,0,1]
truth = [0,1,0,1,1,1,0,1,1]

acc = accuracy_score(preds,truth)
prec = precision_score(preds,truth)
cmtr1 = confusion_matrix(preds,truth)
rec = recall_score(preds,truth)
mcc = matthews_corrcoef(preds,truth)
acc1 = roc_auc_score(preds,truth)

print(acc,prec,rec,mcc,acc1)
print(cmtr1)
accp = torchmetrics.Accuracy(task='binary')(torch.tensor(preds),torch.tensor(truth))
precp = torchmetrics.Precision(task='binary',average='none', num_classes=2)(torch.tensor(preds),torch.tensor(truth))
cmtr = torchmetrics.ConfusionMatrix(task='binary')(torch.tensor(preds),torch.tensor(truth))
rec1 = torchmetrics.Recall(task='binary')(torch.tensor(preds),torch.tensor(truth))
mcc1 = torchmetrics.MatthewsCorrCoef(task='binary')(torch.tensor(preds),torch.tensor(truth))
acc2 = torchmetrics.AUROC(task='binary',average='weighted')(torch.tensor(preds),torch.tensor(truth))
print(accp,precp,rec1,mcc1,acc2)
print(np.array(cmtr))

Results :
0.4444444444444444 0.5 0.6 -0.15811388300841897 0.425
[[1 3]
[2 3]]
tensor(0.4444) tensor(0.6000) tensor(0.5000) tensor(-0.1581) tensor(0.4167)
[[1 2]
[3 3]]

Why are values of Confusion Matrix different