Add t5 to pipeline(task='summarization') (#3413)

* solve conflicts

* move warnings below

* incorporate changes

* add pad_to_max_length to pipelines

* add bug fix for T5 beam search

* add prefix patterns

* make style

* fix conflicts

* adapt pipelines for task specific parameters

* improve docstring

* remove unused patterns

src/transformers/modeling_tf_utils.py

@@ -999,10 +999,12 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
             # set eos token prob to zero if min_length is not reached
             if eos_token_id is not None and cur_len < min_length:
                 # create eos_token_id boolean mask
+                num_batch_hypotheses = batch_size * num_beams
+
                 is_token_logit_eos_token = tf.convert_to_tensor(
                     [True if token is eos_token_id else False for token in range(vocab_size)], dtype=tf.bool
                 )
-                eos_token_indices_mask = tf.broadcast_to(is_token_logit_eos_token, [batch_size, vocab_size])
+                eos_token_indices_mask = tf.broadcast_to(is_token_logit_eos_token, [num_batch_hypotheses, vocab_size])
 
                 scores = set_tensor_by_indices_to_value(scores, eos_token_indices_mask, -float("inf"))