add qwen3
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346
vllm-v0.6.2/tests/entrypoints/openai/test_vision.py
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346
vllm-v0.6.2/tests/entrypoints/openai/test_vision.py
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from typing import Dict, List
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import openai
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import pytest
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import pytest_asyncio
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from vllm.multimodal.utils import encode_image_base64, fetch_image
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from ...utils import RemoteOpenAIServer
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MODEL_NAME = "microsoft/Phi-3.5-vision-instruct"
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MAXIMUM_IMAGES = 2
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# Test different image extensions (JPG/PNG) and formats (gray/RGB/RGBA)
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TEST_IMAGE_URLS = [
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"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
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"https://upload.wikimedia.org/wikipedia/commons/f/fa/Grayscale_8bits_palette_sample_image.png",
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"https://upload.wikimedia.org/wikipedia/commons/thumb/9/91/Venn_diagram_rgb.svg/1280px-Venn_diagram_rgb.svg.png",
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"https://upload.wikimedia.org/wikipedia/commons/0/0b/RGBA_comp.png",
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]
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@pytest.fixture(scope="module")
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def server():
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args = [
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"--task",
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"generate",
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"--dtype",
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"bfloat16",
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"--max-model-len",
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"2048",
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"--max-num-seqs",
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"5",
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"--enforce-eager",
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"--trust-remote-code",
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"--limit-mm-per-prompt",
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f"image={MAXIMUM_IMAGES}",
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]
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with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
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yield remote_server
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@pytest_asyncio.fixture
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async def client(server):
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async with server.get_async_client() as async_client:
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yield async_client
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@pytest.fixture(scope="session")
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def base64_encoded_image() -> Dict[str, str]:
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return {
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image_url: encode_image_base64(fetch_image(image_url))
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for image_url in TEST_IMAGE_URLS
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}
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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@pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
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async def test_single_chat_session_image(client: openai.AsyncOpenAI,
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model_name: str, image_url: str):
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messages = [{
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"role":
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"user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": image_url
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}
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},
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{
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"type": "text",
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"text": "What's in this image?"
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},
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],
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}]
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# test single completion
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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logprobs=True,
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top_logprobs=5)
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assert len(chat_completion.choices) == 1
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choice = chat_completion.choices[0]
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assert choice.finish_reason == "length"
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assert chat_completion.usage == openai.types.CompletionUsage(
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completion_tokens=10, prompt_tokens=772, total_tokens=782)
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message = choice.message
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message = chat_completion.choices[0].message
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assert message.content is not None and len(message.content) >= 10
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assert message.role == "assistant"
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messages.append({"role": "assistant", "content": message.content})
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# test multi-turn dialogue
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messages.append({"role": "user", "content": "express your result in json"})
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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)
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message = chat_completion.choices[0].message
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assert message.content is not None and len(message.content) >= 0
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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@pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
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async def test_single_chat_session_image_beamsearch(client: openai.AsyncOpenAI,
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model_name: str,
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image_url: str):
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messages = [{
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"role":
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"user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": image_url
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}
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},
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{
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"type": "text",
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"text": "What's in this image?"
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},
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],
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}]
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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n=2,
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max_completion_tokens=10,
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logprobs=True,
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top_logprobs=5,
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extra_body=dict(use_beam_search=True))
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assert len(chat_completion.choices) == 2
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assert chat_completion.choices[
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0].message.content != chat_completion.choices[1].message.content
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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@pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
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async def test_single_chat_session_image_base64encoded(
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client: openai.AsyncOpenAI, model_name: str, image_url: str,
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base64_encoded_image: Dict[str, str]):
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messages = [{
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"role":
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"user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url":
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f"data:image/jpeg;base64,{base64_encoded_image[image_url]}"
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}
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},
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{
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"type": "text",
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"text": "What's in this image?"
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},
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],
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}]
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# test single completion
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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logprobs=True,
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top_logprobs=5)
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assert len(chat_completion.choices) == 1
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choice = chat_completion.choices[0]
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assert choice.finish_reason == "length"
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assert chat_completion.usage == openai.types.CompletionUsage(
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completion_tokens=10, prompt_tokens=772, total_tokens=782)
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message = choice.message
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message = chat_completion.choices[0].message
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assert message.content is not None and len(message.content) >= 10
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assert message.role == "assistant"
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messages.append({"role": "assistant", "content": message.content})
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# test multi-turn dialogue
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messages.append({"role": "user", "content": "express your result in json"})
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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)
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message = chat_completion.choices[0].message
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assert message.content is not None and len(message.content) >= 0
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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@pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
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async def test_single_chat_session_image_base64encoded_beamsearch(
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client: openai.AsyncOpenAI, model_name: str, image_url: str,
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base64_encoded_image: Dict[str, str]):
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messages = [{
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"role":
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"user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url":
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f"data:image/jpeg;base64,{base64_encoded_image[image_url]}"
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}
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},
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{
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"type": "text",
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"text": "What's in this image?"
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},
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],
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}]
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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n=2,
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max_completion_tokens=10,
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extra_body=dict(use_beam_search=True))
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assert len(chat_completion.choices) == 2
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assert chat_completion.choices[
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0].message.content != chat_completion.choices[1].message.content
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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@pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
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async def test_chat_streaming_image(client: openai.AsyncOpenAI,
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model_name: str, image_url: str):
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messages = [{
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"role":
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"user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": image_url
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}
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},
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{
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"type": "text",
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"text": "What's in this image?"
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},
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],
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}]
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# test single completion
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.0,
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)
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output = chat_completion.choices[0].message.content
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stop_reason = chat_completion.choices[0].finish_reason
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# test streaming
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stream = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.0,
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stream=True,
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)
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chunks: List[str] = []
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finish_reason_count = 0
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async for chunk in stream:
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delta = chunk.choices[0].delta
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if delta.role:
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assert delta.role == "assistant"
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if delta.content:
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chunks.append(delta.content)
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if chunk.choices[0].finish_reason is not None:
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finish_reason_count += 1
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# finish reason should only return in last block
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assert finish_reason_count == 1
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assert chunk.choices[0].finish_reason == stop_reason
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assert delta.content
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assert "".join(chunks) == output
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_name", [MODEL_NAME])
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@pytest.mark.parametrize(
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"image_urls",
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[TEST_IMAGE_URLS[:i] for i in range(2, len(TEST_IMAGE_URLS))])
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async def test_multi_image_input(client: openai.AsyncOpenAI, model_name: str,
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image_urls: List[str]):
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messages = [{
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"role":
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"user",
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"content": [
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*({
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"type": "image_url",
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"image_url": {
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"url": image_url
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}
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} for image_url in image_urls),
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{
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"type": "text",
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"text": "What's in this image?"
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},
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],
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}]
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if len(image_urls) > MAXIMUM_IMAGES:
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with pytest.raises(openai.BadRequestError): # test multi-image input
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await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.0,
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)
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# the server should still work afterwards
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completion = await client.completions.create(
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model=model_name,
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prompt=[0, 0, 0, 0, 0],
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max_tokens=5,
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temperature=0.0,
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)
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completion = completion.choices[0].text
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assert completion is not None and len(completion) >= 0
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else:
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chat_completion = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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max_completion_tokens=10,
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temperature=0.0,
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)
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message = chat_completion.choices[0].message
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assert message.content is not None and len(message.content) >= 0
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