Repository: huggingface/transformers
Pull request: [`VaultGemma`] Update expectations in integration tests

As per title

cc @Cyrilvallez @ArthurZucker 

Changed files:
filename: tests/models/vaultgemma/test_modeling_vaultgemma.py
patch: @@ -107,8 +107,8 @@ def tearDown(self):
     def test_model_bf16(self):
         model_id = "google/vaultgemma-1b"
         EXPECTED_TEXTS = [
-            "<bos>Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
-            "<pad><pad><bos>Hi today I'm going to be talking about the history of the United States. The United States of America",
+            "<bos>Hello I am doing a project on a 1990 240sx. I have a 1",
+            "<pad><pad><bos>Hi today I am going to show you how to make a simple 3D model of a 3D",
         ]
 
         model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, attn_implementation="eager").to(
@@ -128,13 +128,11 @@ def test_model_pipeline_bf16(self):
         model_id = "google/vaultgemma-1b"
         # EXPECTED_TEXTS should match the same non-pipeline test, minus the special tokens
         EXPECTED_TEXTS = [
-            "Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
-            "Hi today I'm going to be talking about the history of the United States. The United States of America",
+            "Hello I am doing a project on a 1990 240sx. I have a 1",
+            "Hi today I am going to show you how to make a simple 3D model of a 3D",
         ]
 
-        model = AutoModelForCausalLM.from_pretrained(
-            model_id, dtype=torch.bfloat16, attn_implementation="flex_attention"
-        ).to(torch_device)
+        model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16).to(torch_device)
         tokenizer = AutoTokenizer.from_pretrained(model_id)
         pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
@@ -158,18 +156,7 @@ def test_export_static_cache(self):
         tokenizer = AutoTokenizer.from_pretrained(model_id, pad_token="
", padding_side="right")
         EXPECTED_TEXT_COMPLETIONS = Expectations(
             {
-                ("xpu", 3): [
-                    "Hello I am doing a project for my school and I need to know how to make a program that will take a number"
-                ],
-                ("cuda", 7): [
-                    "Hello I am doing a project for my school and I need to know how to make a program that will take a number"
-                ],
-                ("cuda", 8): [
-                    "Hello I am doing a project for my class and I am having trouble with the code. I am trying to make a"
-                ],
-                ("rocm", (9, 5)): [
-                    "Hello I am doing a project for my school and I need to know how to make a program that will take a number"
-                ],
+                ("cuda", 8): ["Hello I am doing a project on a 1990 240sx. I have a 1"],
             }
         )
         EXPECTED_TEXT_COMPLETION = EXPECTED_TEXT_COMPLETIONS.get_expectation()
@@ -239,8 +226,8 @@ def test_generation_beyond_sliding_window(self, attn_implementation: str):
 
         model_id = "google/vaultgemma-1b"
         EXPECTED_COMPLETIONS = [
-            " the people, the food, the culture, the history, the music, the art, the architecture",
-            ", green, yellow, orange, purple, pink, brown, black, white, gray, silver",
+            " place pretty place pretty place. place pretty place pretty place. place pretty place pretty place. place pretty",
+            ", green, yellow, orange, purple, black, white, and gray.\n\nA list of",
         ]
 
         input_text = [
@@ -285,8 +272,8 @@ def test_generation_beyond_sliding_window_dynamic(self, attn_implementation: str
 
         model_id = "google/vaultgemma-1b"
         EXPECTED_COMPLETIONS = [
-            " the people, the food, the culture, the history, the music, the art, the architecture",
-            ", green, yellow, orange, purple, pink, brown, black, white, gray, silver",
+            " place pretty place pretty place. place pretty place pretty place. place pretty place pretty place. place pretty",
+            ", green, yellow, orange, purple, black, white, and gray.\n\nA list of",
         ]
 
         input_text = [