Specify dtype=torch.bool to avoid xla error (#31191)

The StoppingCriteriaList allocates is_done without specifying dtype=torch.bool. On XLA this allocates a float tensor and causes a failure on the following line:

is_done = is_done | criteria(input_ids, scores, **kwargs)

by attempting to OR float with bool.

src/transformers/generation/stopping_criteria.py

@@ -502,7 +502,7 @@ class EosTokenCriteria(StoppingCriteria):
 class StoppingCriteriaList(list):
     @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.BoolTensor:
-        is_done = torch.full((input_ids.shape[0],), False, device=input_ids.device)
+        is_done = torch.full((input_ids.shape[0],), False, device=input_ids.device, dtype=torch.bool)
         for criteria in self:
             is_done = is_done | criteria(input_ids, scores, **kwargs)
         return is_done