test_chat_utils.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import warnings
from typing import Optional

import pytest
import os

from vllm.assets.image import ImageAsset
from vllm.config import ModelConfig
from vllm.entrypoints.chat_utils import (_try_extract_ast, load_chat_template,
                                         parse_chat_messages,
                                         parse_chat_messages_futures,
                                         resolve_chat_template_content_format,
                                         resolve_hf_chat_template)
from vllm.entrypoints.llm import apply_hf_chat_template
from vllm.multimodal import MultiModalDataDict
from vllm.multimodal.utils import encode_image_base64
from vllm.transformers_utils.tokenizer_group import TokenizerGroup
from ..utils import models_path_prefix

from ..models.registry import HF_EXAMPLE_MODELS
from ..utils import VLLM_PATH

EXAMPLES_DIR = VLLM_PATH / "examples"

PHI3V_MODEL_ID = os.path.join(models_path_prefix, "microsoft/Phi-3.5-vision-instruct")
ULTRAVOX_MODEL_ID = os.path.join(models_path_prefix, "fixie-ai/ultravox-v0_5-llama-3_2-1b")
QWEN2AUDIO_MODEL_ID = os.path.join(models_path_prefix, "Qwen/Qwen2-Audio-7B-Instruct")
QWEN2VL_MODEL_ID = os.path.join(models_path_prefix, "Qwen/Qwen2-VL-2B-Instruct")
QWEN25VL_MODEL_ID = os.path.join(models_path_prefix, "Qwen/Qwen2.5-VL-3B-Instruct")
MLLAMA_MODEL_ID = os.path.join(models_path_prefix, "meta-llama/Llama-3.2-11B-Vision-Instruct")
LLAMA_GUARD_MODEL_ID = os.path.join(models_path_prefix, "meta-llama/Llama-Guard-3-1B")
HERMES_MODEL_ID = os.path.join(models_path_prefix, "NousResearch/Hermes-3-Llama-3.1-8B")
MISTRAL_MODEL_ID = os.path.join(models_path_prefix, "mistralai/Mistral-Small-3.1-24B-Instruct-2503")

@pytest.fixture(scope="function")
def phi3v_model_config():
    return ModelConfig(PHI3V_MODEL_ID,
                       task="generate",
                       tokenizer=PHI3V_MODEL_ID,
                       tokenizer_mode="auto",
                       trust_remote_code=True,
                       dtype="auto",
                       seed=0,
                       limit_mm_per_prompt={
                           "image": 2,
                       })


@pytest.fixture(scope="module")
def phi3v_tokenizer():
    return TokenizerGroup(
        tokenizer_id=PHI3V_MODEL_ID,
        enable_lora=False,
        max_num_seqs=5,
        max_input_length=None,
    )


@pytest.fixture(scope="module")
def mllama_model_config():
    return ModelConfig(MLLAMA_MODEL_ID,
                       task="generate",
                       tokenizer=MLLAMA_MODEL_ID,
                       tokenizer_mode="auto",
                       trust_remote_code=True,
                       dtype="auto",
                       seed=0,
                       limit_mm_per_prompt={
                           "image": 2,
                       })


@pytest.fixture(scope="module")
def mllama_tokenizer():
    return TokenizerGroup(
        MLLAMA_MODEL_ID,
        enable_lora=False,
        max_num_seqs=5,
        max_input_length=None,
    )


@pytest.fixture(scope="function")
def mistral_model_config():
    return ModelConfig(MISTRAL_MODEL_ID,
                       task="generate",
                       tokenizer=MISTRAL_MODEL_ID,
                       tokenizer_mode="auto",
                       trust_remote_code=True,
                       dtype="auto",
                       seed=0,
                       limit_mm_per_prompt={
                           "image": 2,
                       })


@pytest.fixture(scope="module")
def mistral_tokenizer():
    return TokenizerGroup(
        tokenizer_id=MISTRAL_MODEL_ID,
        enable_lora=False,
        max_num_seqs=5,
        max_input_length=None,
    )


@pytest.fixture(scope="module")
def image_url():
    image = ImageAsset('cherry_blossom')
    base64 = encode_image_base64(image.pil_image)
    return f"data:image/jpeg;base64,{base64}"


def _assert_mm_data_is_image_input(
    mm_data: Optional[MultiModalDataDict],
    image_count: int,
) -> None:
    assert mm_data is not None
    assert set(mm_data.keys()) == {"image"}

    image_data = mm_data.get("image")
    assert image_data is not None

    assert isinstance(image_data, list) and len(image_data) == image_count


def test_parse_chat_messages_single_image(
    phi3v_model_config,
    phi3v_tokenizer,
    image_url,
):
    conversation, mm_data = parse_chat_messages(
        [{
            "role":
            "user",
            "content": [{
                "type": "image_url",
                "image_url": {
                    "url": image_url
                }
            }, {
                "type": "text",
                "text": "What's in the image?"
            }]
        }],
        phi3v_model_config,
        phi3v_tokenizer,
        content_format="string",
    )

    assert conversation == [{
        "role": "user",
        "content": "<|image_1|>\nWhat's in the image?"
    }]
    _assert_mm_data_is_image_input(mm_data, 1)


def test_parse_chat_messages_empty_system(
    mistral_model_config,
    mistral_tokenizer,
):
    # Test string format
    conversation, _ = parse_chat_messages(
        [{
            "role": "system",
            "content": ""
        }, {
            "role": "user",
            "content": [{
                "type": "text",
                "text": "Who are you?"
            }]
        }],
        mistral_model_config,
        mistral_tokenizer,
        content_format="string",
    )
    assert conversation == [{
        "role": "system",
        "content": ""
    }, {
        "role": "user",
        "content": "Who are you?"
    }]

    # Test openai format
    conversation, _ = parse_chat_messages(
        [{
            "role": "system",
            "content": ""
        }, {
            "role": "user",
            "content": [{
                "type": "text",
                "text": "Who are you?"
            }]
        }],
        mistral_model_config,
        mistral_tokenizer,
        content_format="openai",
    )
    assert conversation == [{
        "role": "system",
        "content": [{
            "type": "text",
            "text": ""
        }]
    }, {
        "role":
        "user",
        "content": [{
            "type": "text",
            "text": "Who are you?"
        }]
    }]


@pytest.mark.asyncio
async def test_parse_chat_messages_single_image_async(
    phi3v_model_config,
    phi3v_tokenizer,
    image_url,
):
    conversation, mm_future = parse_chat_messages_futures(
        [{
            "role":
            "user",
            "content": [{
                "type": "image_url",
                "image_url": {
                    "url": image_url
                }
            }, {
                "type": "text",
                "text": "What's in the image?"
            }]
        }],
        phi3v_model_config,
        phi3v_tokenizer,
        content_format="string",
    )

    assert conversation == [{
        "role": "user",
        "content": "<|image_1|>\nWhat's in the image?"
    }]
    _assert_mm_data_is_image_input(await mm_future, 1)


def test_parse_chat_messages_multiple_images(
    phi3v_model_config,
    phi3v_tokenizer,
    image_url,
):
    conversation, mm_data = parse_chat_messages(
        [{
            "role":
            "user",
            "content": [{
                "type": "image_url",
                "image_url": {
                    "url": image_url
                }
            }, {
                "type": "image_pil",
                "image_pil": ImageAsset('cherry_blossom').pil_image
            }, {
                "type": "text",
                "text": "What's in these images?"
            }]
        }],
        phi3v_model_config,
        phi3v_tokenizer,
        content_format="string",
    )

    assert conversation == [{
        "role":
        "user",
        "content":
        "<|image_1|>\n<|image_2|>\nWhat's in these images?"
    }]
    _assert_mm_data_is_image_input(mm_data, 2)


@pytest.mark.asyncio
async def test_parse_chat_messages_multiple_images_async(
    phi3v_model_config,
    phi3v_tokenizer,
    image_url,
):
    conversation, mm_future = parse_chat_messages_futures(
        [{
            "role":
            "user",
            "content": [{
                "type": "image_url",
                "image_url": {
                    "url": image_url
                }
            }, {
                "type": "image_pil",
                "image_pil": ImageAsset('cherry_blossom').pil_image
            }, {
                "type": "text",
                "text": "What's in these images?"
            }]
        }],
        phi3v_model_config,
        phi3v_tokenizer,
        content_format="string",
    )

    assert conversation == [{
        "role":
        "user",
        "content":
        "<|image_1|>\n<|image_2|>\nWhat's in these images?"
    }]
    _assert_mm_data_is_image_input(await mm_future, 2)


def test_parse_chat_messages_placeholder_already_in_prompt(
    phi3v_model_config,
    phi3v_tokenizer,
    image_url,
):
    conversation, mm_data = parse_chat_messages(
        [{
            "role":
            "user",
            "content": [{
                "type": "image_url",
                "image_url": {
                    "url": image_url
                }
            }, {
                "type": "image_url",
                "image_url": {
                    "url": image_url
                }
            }, {
                "type":
                "text",
                "text":
                "What's in <|image_1|> and how does it compare to <|image_2|>?"
            }]
        }],
        phi3v_model_config,
        phi3v_tokenizer,
        content_format="string",
    )
    assert conversation == [{
        "role":
        "user",
        "content":
        "What's in <|image_1|> and how does it compare to <|image_2|>?"
    }]
    _assert_mm_data_is_image_input(mm_data, 2)


def test_parse_chat_messages_placeholder_one_already_in_prompt(
    phi3v_model_config,
    phi3v_tokenizer,
    image_url,
):
    conversation, mm_data = parse_chat_messages(
        [{
            "role":
            "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": image_url
                    }
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": image_url
                    }
                },
                {
                    "type":
                    "text",
                    "text":
                    "What's in <|image_1|> and how does it compare to the other one?"  # noqa: E501
                }
            ]
        }],
        phi3v_model_config,
        phi3v_tokenizer,
        content_format="string",
    )

    assert conversation == [{
        "role":
        "user",
        "content":
        "<|image_2|>\nWhat's in <|image_1|> and how does it compare to the "
        "other one?"
    }]
    _assert_mm_data_is_image_input(mm_data, 2)


def test_parse_chat_messages_multiple_images_across_messages(
    phi3v_model_config,
    phi3v_tokenizer,
    image_url,
):
    conversation, mm_data = parse_chat_messages(
        [{
            "role":
            "user",
            "content": [{
                "type": "image_url",
                "image_url": {
                    "url": image_url
                }
            }, {
                "type": "text",
                "text": "What's in this image?"
            }]
        }, {
            "role": "assistant",
            "content": "Some stuff."
        }, {
            "role":
            "user",
            "content": [{
                "type": "image_url",
                "image_url": {
                    "url": image_url
                }
            }, {
                "type": "text",
                "text": "What about this one?"
            }]
        }],
        phi3v_model_config,
        phi3v_tokenizer,
        content_format="string",
    )

    assert conversation == [
        {
            "role": "user",
            "content": "<|image_1|>\nWhat's in this image?"
        },
        {
            "role": "assistant",
            "content": "Some stuff."
        },
        {
            "role": "user",
            "content": "<|image_2|>\nWhat about this one?"
        },
    ]
    _assert_mm_data_is_image_input(mm_data, 2)


def test_parse_chat_messages_context_text_format(
    phi3v_model_config,
    phi3v_tokenizer,
):
    conversation, mm_data = parse_chat_messages(
        [{
            "role": "user",
            "content": [{
                "type": "text",
                "text": "What's in this text?"
            }]
        }, {
            "role": "assistant",
            "content": "Some stuff."
        }, {
            "role": "user",
            "content": "What about this one?"
        }],
        phi3v_model_config,
        phi3v_tokenizer,
        content_format="openai",
    )

    assert conversation == [
        {
            "role": "user",
            "content": [{
                "type": "text",
                "text": "What's in this text?"
            }]
        },
        {
            "role": "assistant",
            "content": [{
                "type": "text",
                "text": "Some stuff."
            }]
        },
        {
            "role": "user",
            "content": [{
                "type": "text",
                "text": "What about this one?"
            }]
        },
    ]


def test_parse_chat_messages_rejects_too_many_images_in_one_message(
    phi3v_model_config,
    phi3v_tokenizer,
    image_url,
):
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore",
            message="coroutine 'async_get_and_parse_image' was never awaited")
        with pytest.raises(
                ValueError,
                match="At most 2 image\\(s\\) may be provided in one request\\."
        ):
            parse_chat_messages(
                [{
                    "role":
                    "user",
                    "content": [{
                        "type": "image_url",
                        "image_url": {
                            "url": image_url
                        }
                    }, {
                        "type": "image_url",
                        "image_url": {
                            "url": image_url
                        }
                    }, {
                        "type": "image_url",
                        "image_url": {
                            "url": image_url
                        }
                    }, {
                        "type": "text",
                        "text": "What's in these images?"
                    }]
                }],
                phi3v_model_config,
                phi3v_tokenizer,
                content_format="string",
            )


def test_parse_chat_messages_rejects_too_many_images_across_messages(
    phi3v_model_config,
    phi3v_tokenizer,
    image_url,
):
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore",
            message="coroutine 'async_get_and_parse_image' was never awaited")
        with pytest.raises(
                ValueError,
                match="At most 2 image\\(s\\) may be provided in one request\\."
        ):
            parse_chat_messages(
                [{
                    "role":
                    "user",
                    "content": [{
                        "type": "image_url",
                        "image_url": {
                            "url": image_url
                        }
                    }, {
                        "type": "text",
                        "text": "What's in this image?"
                    }]
                }, {
                    "role": "assistant",
                    "content": "Some stuff."
                }, {
                    "role":
                    "user",
                    "content": [{
                        "type": "image_url",
                        "image_url": {
                            "url": image_url
                        }
                    }, {
                        "type": "image_url",
                        "image_url": {
                            "url": image_url
                        }
                    }, {
                        "type": "text",
                        "text": "What about these two?"
                    }]
                }],
                phi3v_model_config,
                phi3v_tokenizer,
                content_format="string",
            )


def test_parse_chat_messages_multiple_images_uncommon_input(
    phi3v_model_config,
    phi3v_tokenizer,
    image_url,
):
    conversation, mm_data = parse_chat_messages(
        [{
            "role":
            "user",
            "content": [
                "What's in these images?", {
                    "image_url": image_url
                }, {
                    "image_url": image_url
                }
            ]
        }],
        phi3v_model_config,
        phi3v_tokenizer,
        content_format="string",
    )

    assert conversation == [{
        "role":
        "user",
        "content":
        "<|image_1|>\n<|image_2|>\nWhat's in these images?"
    }]
    _assert_mm_data_is_image_input(mm_data, 2)


### Mllama currently wraps images / texts as interleaved dictionaries
def test_mllama_single_image(
    mllama_model_config,
    mllama_tokenizer,
    image_url,
):
    """Ensures that a single image is parsed correctly mllama."""
    conversation, mm_data = parse_chat_messages(
        [{
            "role":
            "user",
            "content": [{
                'type': 'text',
                'text': 'The content of this image is:'
            }, {
                "image_url": image_url
            }]
        }],
        mllama_model_config,
        mllama_tokenizer,
        content_format="openai",
    )
    _assert_mm_data_is_image_input(mm_data, 1)
    assert conversation == [{
        'role':
        'user',
        'content': [{
            'type': 'text',
            'text': 'The content of this image is:'
        }, {
            'type': 'image'
        }]
    }]


def test_mllama_interleaved_images(
    mllama_model_config,
    mllama_tokenizer,
    image_url,
):
    """Ensures that multiple image are parsed as interleaved dicts."""
    conversation, mm_data = parse_chat_messages(
        [{
            "role":
            "user",
            "content": [
                {
                    'type': 'text',
                    'text': 'The content of the first image is:'
                },
                {
                    "image_url": image_url
                },
                {
                    'type': 'text',
                    'text': 'The content of the second image is:'
                },
                {
                    "image_url": image_url
                },
            ]
        }],
        mllama_model_config,
        mllama_tokenizer,
        content_format="openai",
    )
    _assert_mm_data_is_image_input(mm_data, 2)
    assert conversation == [{
        'role':
        'user',
        'content': [{
            'type': 'text',
            'text': 'The content of the first image is:'
        }, {
            'type': 'image'
        }, {
            'type': 'text',
            'text': 'The content of the second image is:'
        }, {
            'type': 'image'
        }]
    }]


@pytest.mark.parametrize("model", [MLLAMA_MODEL_ID])
def test_multimodal_image_parsing_matches_hf(model, image_url):
    """Checks end to end hf alignment for multimodal [image] parsing."""

    def get_conversation(is_hf: bool):
        img_part = {"type": "image_url", "image_url": {"url": image_url}}
        if is_hf:
            img_part = {'type': 'image'}
        return [{
            'role':
            'user',
            'content': [
                {
                    'type': 'text',
                    'text': 'The content of the first image is:'
                },
                img_part,
                {
                    'type': 'text',
                    'text': 'The content of the second image is:'
                },
                img_part,
                {
                    'type': 'text',
                    'text': 'What animal is in the first image?'
                },
            ]
        }]

    # Build a config for the model
    model_config = ModelConfig(model,
                               task="generate",
                               tokenizer=model,
                               tokenizer_mode="auto",
                               trust_remote_code=True,
                               dtype="auto",
                               seed=0,
                               limit_mm_per_prompt={
                                   "image": 2,
                               })

    # Build the tokenizer group and grab the underlying tokenizer
    tokenizer_group = TokenizerGroup(
        model,
        enable_lora=False,
        max_num_seqs=5,
        max_input_length=None,
        trust_remote_code=model_config.trust_remote_code,
    )
    tokenizer = tokenizer_group.tokenizer

    # Build and parse a conversation with {"type": "image"} using the tokenizer
    hf_conversation = get_conversation(is_hf=True)
    hf_result = tokenizer.apply_chat_template(
        hf_conversation,
        tokenize=False,
        add_generation_prompt=True,
    )

    # Now parse with vLLMs chat utils & apply the template
    vllm_conversation = get_conversation(is_hf=False)
    conversation, _ = parse_chat_messages(
        vllm_conversation,
        model_config,
        tokenizer_group,
        content_format="openai",
    )

    vllm_result = apply_hf_chat_template(
        tokenizer=tokenizer,
        conversation=conversation,
        chat_template=None,
        model_config=model_config,
        tools=None,
        add_generation_prompt=True,
    )

    assert hf_result == vllm_result


@pytest.mark.parametrize(
    "model",
    [
        QWEN2VL_MODEL_ID,  # tokenizer.chat_template is of type str
        HERMES_MODEL_ID,  # tokenizer.chat_template is of type dict
    ])
@pytest.mark.parametrize("use_tools", [True, False])
def test_resolve_hf_chat_template(sample_json_schema, model, use_tools):
    """checks that chat_template is a dict type for HF models."""
    model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
    model_info.check_available_online(on_fail="skip")

    model_config = ModelConfig(
        model,
        tokenizer=model_info.tokenizer or model,
        tokenizer_mode=model_info.tokenizer_mode,
        trust_remote_code=model_info.trust_remote_code,
        hf_overrides=model_info.hf_overrides,
    )

    # Build the tokenizer group and grab the underlying tokenizer
    tokenizer_group = TokenizerGroup(
        model,
        enable_lora=False,
        max_num_seqs=5,
        max_input_length=None,
        trust_remote_code=model_config.trust_remote_code,
    )
    tokenizer = tokenizer_group.tokenizer

    tools = [{
        "type": "function",
        "function": {
            "name": "dummy_function_name",
            "description": "This is a dummy function",
            "parameters": sample_json_schema
        }
    }] if use_tools else None

    # Test detecting the tokenizer's chat_template
    chat_template = resolve_hf_chat_template(
        tokenizer,
        chat_template=None,
        tools=tools,
        model_config=model_config,
    )
    assert isinstance(chat_template, str)


# NOTE: Qwen2-Audio default chat template is specially defined inside
# processor class instead of using `tokenizer_config.json`
# yapf: disable
@pytest.mark.parametrize(
    ("model", "expected_format"),
    [(PHI3V_MODEL_ID, "string"),
     (QWEN2VL_MODEL_ID, "openai"),
     (QWEN25VL_MODEL_ID, "openai"),
     (ULTRAVOX_MODEL_ID, "string"),
     (QWEN2AUDIO_MODEL_ID, "openai"),
     (MLLAMA_MODEL_ID, "openai"),
     (LLAMA_GUARD_MODEL_ID, "openai")],
)
# yapf: enable
def test_resolve_content_format_hf_defined(model, expected_format):
    model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
    model_info.check_available_online(on_fail="skip")

    model_config = ModelConfig(
        model,
        tokenizer=model_info.tokenizer or model,
        tokenizer_mode=model_info.tokenizer_mode,
        trust_remote_code=model_info.trust_remote_code,
        hf_overrides=model_info.hf_overrides,
    )

    tokenizer_group = TokenizerGroup(
        model,
        enable_lora=False,
        max_num_seqs=5,
        max_input_length=None,
        trust_remote_code=model_config.trust_remote_code,
    )
    tokenizer = tokenizer_group.tokenizer

    # Test detecting the tokenizer's chat_template
    chat_template = resolve_hf_chat_template(
        tokenizer,
        chat_template=None,
        tools=None,
        model_config=model_config,
    )
    assert isinstance(chat_template, str)

    print("[TEXT]")
    print(chat_template)
    print("[AST]")
    print(_try_extract_ast(chat_template))

    resolved_format = resolve_chat_template_content_format(
        None,  # Test detecting the tokenizer's chat_template
        None,
        "auto",
        tokenizer,
        model_config=model_config,
    )

    assert resolved_format == expected_format


# yapf: disable
@pytest.mark.parametrize(
    ("model", "expected_format"),
    [("Salesforce/blip2-opt-2.7b", "string"),
     ("facebook/chameleon-7b", "string"),
     ("deepseek-ai/deepseek-vl2-tiny", "string"),
     ("microsoft/Florence-2-base", "string"),
     ("adept/fuyu-8b", "string"),
     ("google/paligemma-3b-mix-224", "string"),
     ("Qwen/Qwen-VL", "string"),
     ("Qwen/Qwen-VL-Chat", "string")],
)
# yapf: enable
def test_resolve_content_format_fallbacks(model, expected_format):
    model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
    model_info.check_available_online(on_fail="skip")

    model_config = ModelConfig(
        model,
        tokenizer=model_info.tokenizer or model,
        tokenizer_mode=model_info.tokenizer_mode,
        trust_remote_code=model_info.trust_remote_code,
        hf_overrides=model_info.hf_overrides,
    )

    tokenizer_group = TokenizerGroup(
        model_config.tokenizer,
        enable_lora=False,
        max_num_seqs=5,
        max_input_length=None,
        trust_remote_code=model_config.trust_remote_code,
    )
    tokenizer = tokenizer_group.tokenizer

    # Test detecting the tokenizer's chat_template
    chat_template = resolve_hf_chat_template(
        tokenizer,
        chat_template=None,
        tools=None,
        model_config=model_config,
    )
    assert isinstance(chat_template, str)

    print("[TEXT]")
    print(chat_template)
    print("[AST]")
    print(_try_extract_ast(chat_template))

    resolved_format = resolve_chat_template_content_format(
        None,  # Test detecting the tokenizer's chat_template
        None,
        "auto",
        tokenizer,
        model_config=model_config,
    )

    assert resolved_format == expected_format


# yapf: disable
@pytest.mark.parametrize(
    ("template_path", "expected_format"),
    [("template_alpaca.jinja", "string"),
     ("template_baichuan.jinja", "string"),
     ("template_chatglm.jinja", "string"),
     ("template_chatglm2.jinja", "string"),
     ("template_chatml.jinja", "string"),
     ("template_dse_qwen2_vl.jinja", "openai"),
     ("template_falcon_180b.jinja", "string"),
     ("template_falcon.jinja", "string"),
     ("template_inkbot.jinja", "string"),
     ("template_teleflm.jinja", "string"),
     ("template_vlm2vec.jinja", "openai"),
     ("tool_chat_template_granite_20b_fc.jinja", "string"),
     ("tool_chat_template_hermes.jinja", "string"),
     ("tool_chat_template_internlm2_tool.jinja", "string"),
     ("tool_chat_template_llama3.1_json.jinja", "openai"),
     ("tool_chat_template_llama3.2_json.jinja", "openai"),
     ("tool_chat_template_mistral_parallel.jinja", "string"),
     ("tool_chat_template_mistral.jinja", "string")],
)
# yapf: enable
def test_resolve_content_format_examples(template_path, expected_format):
    model_config = ModelConfig(
        PHI3V_MODEL_ID,  # Dummy
        tokenizer=PHI3V_MODEL_ID,  # Dummy
        trust_remote_code=True,
    )

    tokenizer_group = TokenizerGroup(
        PHI3V_MODEL_ID,  # Dummy
        enable_lora=False,
        max_num_seqs=5,
        max_input_length=None,
        trust_remote_code=model_config.trust_remote_code,
    )
    dummy_tokenizer = tokenizer_group.tokenizer
    dummy_tokenizer.chat_template = None

    chat_template = load_chat_template(EXAMPLES_DIR / template_path)
    assert isinstance(chat_template, str)

    print("[TEXT]")
    print(chat_template)
    print("[AST]")
    print(_try_extract_ast(chat_template))

    resolved_format = resolve_chat_template_content_format(
        chat_template,
        None,
        "auto",
        dummy_tokenizer,
        model_config=model_config,
    )

    assert resolved_format == expected_format