ClasswiseWrapper silently drops values for invalid labels

Bug

ClasswiseWrapper converts a metric tensor into a dictionary with zip(self.labels, x). If the number of labels does not match the number of metric values, zip silently truncates the longer input. Duplicate labels also overwrite earlier dictionary entries. Both cases can make class metrics disappear without any error.

Reproduction

import torch

from torchmetrics.classification import MulticlassAccuracy
from torchmetrics.wrappers import ClasswiseWrapper

metric = ClasswiseWrapper(
    MulticlassAccuracy(num_classes=3, average=None),
    labels=["cat", "dog"],
)

result = metric(
    torch.tensor([0, 1, 2]),
    torch.tensor([0, 1, 2]),
)
print(result)

The wrapped metric produces three class values, but the result contains only two:

{"multiclassaccuracy_cat": tensor(1.), "multiclassaccuracy_dog": tensor(1.)}

Likewise, labels such as ["cat", "cat", "dog"] produce only two dictionary keys because the second cat overwrites the first.

Expected behavior

Custom labels should define a one-to-one mapping to the wrapped metric values. Mismatched label counts and duplicate labels should raise a clear ValueError instead of silently losing results.

Environment

• TorchMetrics: current master at e63e92a6
• PyTorch: 2.13.0
• Python: 3.12.13
• macOS

Start at the ClasswiseWrapper implementation and reproduce the mismatch and duplicate-label cases from the issue using the shown PyTorch metrics. Add coverage for both invalid inputs and verify that each raises a clear ValueError rather than silently dropping or overwriting metric values.