chore: import upstream snapshot with attribution

tests/python/json_ffi/test_json_ffi_engine_image.py

@@ -0,0 +1,92 @@
+import base64
+from typing import Dict, List, Optional  # noqa: UP035
+
+import requests
+
+from mlc_llm.json_ffi import JSONFFIEngine
+from mlc_llm.testing import require_test_model
+
+
+def base64_encode_image(url: str) -> str:
+    response = requests.get(url)
+    response.raise_for_status()  # Ensure we got a successful response
+    image_data = base64.b64encode(response.content)
+    image_data_str = image_data.decode("utf-8")
+    data_url = f"data:image/jpeg;base64,{image_data_str}"
+    return data_url
+
+
+image_prompts = [
+    [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": f"{base64_encode_image('https://llava-vl.github.io/static/images/view.jpg')}",
+                },
+                {"type": "text", "text": "What does the image represent?"},
+            ],
+        }
+    ]
+]
+
+
+def run_chat_completion(
+    engine: JSONFFIEngine,
+    model: str,
+    prompts: List[List[Dict]] = image_prompts,  # noqa: UP006
+    tools: Optional[List[Dict]] = None,  # noqa: UP006
+):
+    num_requests = 1
+    max_tokens = 64
+    n = 1
+    output_texts: List[List[str]] = [["" for _ in range(n)] for _ in range(num_requests)]  # noqa: UP006
+
+    for rid in range(num_requests):
+        print(f"chat completion for request {rid}")
+        for response in engine.chat.completions.create(
+            messages=prompts[rid],
+            model=model,
+            max_tokens=max_tokens,
+            n=n,
+            request_id=str(rid),
+            tools=tools,
+        ):
+            for choice in response.choices:
+                assert choice.delta.role == "assistant"
+                assert isinstance(choice.delta.content[0], Dict)  # noqa: UP006
+                assert choice.delta.content[0]["type"] == "text"
+                output_texts[rid][choice.index] += choice.delta.content[0]["text"]
+
+    # Print output.
+    print("Chat completion all finished")
+    for req_id, outputs in enumerate(output_texts):
+        print(f"Prompt {req_id}: {prompts[req_id]}")
+        if len(outputs) == 1:
+            print(f"Output {req_id}:{outputs[0]}\n")
+        else:
+            for i, output in enumerate(outputs):
+                print(f"Output {req_id}({i}):{output}\n")
+
+
+@require_test_model("llava-1.5-7b-hf-q4f16_1-MLC")
+def test_chat_completion():
+    # Create engine.
+    engine = JSONFFIEngine(
+        model,  # noqa: F821
+        max_total_sequence_length=1024,
+    )
+
+    run_chat_completion(engine, model)  # noqa: F821
+
+    # Test malformed requests.
+    for response in engine._raw_chat_completion("malformed_string", n=1, request_id="123"):
+        assert len(response.choices) == 1
+        assert response.choices[0].finish_reason == "error"
+
+    engine.terminate()
+
+
+if __name__ == "__main__":
+    test_chat_completion()