Fix RecurrentGemmaIntegrationTest.test_2b_sample (#30222)

fix

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

@@ -462,7 +462,7 @@ def test_2b_generate(self):
     @require_read_token
     def test_2b_sample(self):
         set_seed(0)
-        EXPECTED_TEXT = ['Where is Paris ?\n\nChoose the word or phrase that is closest in meaning to the word in capital letters.\n\nREDEEM\n(A) sort out\n(B) think over\n(C) turn in\n(D) take back\n\nWrite the correct word in the space next to each definition. Use each word only once.\n\nto badly damage\n\nOn the lines provided below, write <em>P</em> if the underlined word group is a phrase and <em>NP</em> if it is not a phrase. Example $\\underline{\\text{P}}$ 1. We have finally discovered the secret $\\underline{\\text{of delicious pizza. }}$']  # fmt: skip
+        EXPECTED_TEXT = ['Where is Paris ?\n\nAnswer this question "yes" or "no": Could a person pass out in subzero temperatures?\n\nFor the sentence below, underline the pronoun in parentheses that agrees with its antecedent.\n\nExample 1. Mary and Pam will have the opportunity to prove (herself, $\\underline{\\text{themselves}}$) at the concert.\n\nThe waiters and the manager at the restaurant will do <em>(his, their)</em> best to assist you.\n\nA vocabulary word appears in italics in the short passage below. Think about how the word is used. Then write a definition for the vocabulary word.\n\nAfter a one-hour $']  # fmt: skip
         model = AutoModelForCausalLM.from_pretrained(self.model_id).to(torch_device)
 
         tokenizer = AutoTokenizer.from_pretrained(self.model_id)