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tests/models/gemma4_assistant/test_modeling_gemma4_assistant.py

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+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers import is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...test_processing_common import url_to_local_path
+
+
+if is_torch_available():
+    from transformers import (
+        AutoModelForCausalLM,
+        Gemma4ForConditionalGeneration,
+        Gemma4Processor,
+    )
+
+
+@slow
+@require_torch
+@unittest.skip(reason="Update after release")  # TODO @vasqu
+class Gemma4IntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.model_name = "google/gemma-4-E2B-it"
+        self.assistant_name = "google/gemma-4-E2B-it-assistant"
+        self.processor = Gemma4Processor.from_pretrained(self.model_name)
+
+        self.url1 = url_to_local_path(
+            "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
+        )
+        self.url2 = url_to_local_path(
+            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
+        )
+        self.messages = [
+            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": self.url1},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def test_model_with_image(self):
+        model = Gemma4ForConditionalGeneration.from_pretrained(self.model_name, device_map=torch_device)
+        assistant = AutoModelForCausalLM.from_pretrained(self.assistant_name, device_map=torch_device)
+
+        inputs = self.processor.apply_chat_template(
+            self.messages,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            add_generation_prompt=True,
+        ).to(torch_device)
+
+        output = model.generate(**inputs, assistant_model=assistant, max_new_tokens=30, do_sample=False)
+        input_size = inputs.input_ids.shape[-1]
+        output_text = self.processor.batch_decode(output[:, input_size:], skip_special_tokens=True)
+
+        EXPECTED_TEXTS = Expectations(
+            {
+                ("cuda", 8): ['This image shows a **brown and white cow** standing on a **sandy beach** with the **ocean and a blue sky** in the background'],
+            }
+        )  # fmt: skip
+        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
+        self.assertEqual(output_text, EXPECTED_TEXT)
+
+    def test_model_text_only(self):
+        model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map=torch_device)
+        assistant = AutoModelForCausalLM.from_pretrained(self.assistant_name, device_map=torch_device)
+
+        inputs = self.processor.tokenizer.apply_chat_template(
+            [{"role": "user", "content": "Write a poem about Machine Learning."}],
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            add_generation_prompt=True,
+        ).to(torch_device)
+
+        output = model.generate(**inputs, assistant_model=assistant, max_new_tokens=30, do_sample=False)
+        input_size = inputs.input_ids.shape[-1]
+        output_text = self.processor.batch_decode(output[:, input_size:], skip_special_tokens=True)
+
+        EXPECTED_TEXTS = Expectations(
+            {
+                ("cuda", (8, 0)): ['## The Algorithmic Mind\n\nA whisper starts, a seed unseen,\nOf data vast, a vibrant sheen.\nA sea of numbers,'],
+                ("cuda", (8, 6)): ['## The Algorithmic Mind\n\nA tapestry of data, vast and deep,\nWhere silent numbers in their slumber sleep.\nA sea of text'],
+            }
+        )  # fmt: skip
+        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
+        self.assertEqual(output_text, EXPECTED_TEXT)