Update AutoModel classes in summarization example (#12178)

- Convert use of deprecated AutoModelWithLMHead to AutoModelForSeq2SeqLM
- Add newly required `truncation=True` to `tokenizer.encode` with `max_length`

This silences all warnings.

docs/source/task_summary.rst

@@ -827,18 +827,18 @@ CNN / Daily Mail), it yields very good results.
 .. code-block::
 
     >>> ## PYTORCH CODE
-    >>> from transformers import AutoModelWithLMHead, AutoTokenizer
+    >>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-    >>> model = AutoModelWithLMHead.from_pretrained("t5-base")
+    >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
     >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
 
     >>> # T5 uses a max_length of 512 so we cut the article to 512 tokens.
-    >>> inputs = tokenizer.encode("summarize: " + ARTICLE, return_tensors="pt", max_length=512)
+    >>> inputs = tokenizer.encode("summarize: " + ARTICLE, return_tensors="pt", max_length=512, truncation=True)
     >>> outputs = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
     >>> ## TENSORFLOW CODE
-    >>> from transformers import TFAutoModelWithLMHead, AutoTokenizer
+    >>> from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer
 
-    >>> model = TFAutoModelWithLMHead.from_pretrained("t5-base")
+    >>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")
     >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
 
     >>> # T5 uses a max_length of 512 so we cut the article to 512 tokens.