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

import os
import pytest

from vllm import LLM
from vllm.distributed import cleanup_dist_env_and_memory


from utils import models_path_prefix
from ..openai.test_vision import TEST_IMAGE_ASSETS


@pytest.fixture(scope="function")
def text_llm():
    # pytest caches the fixture so we use weakref.proxy to
    # enable garbage collection
    llm = LLM(model=os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B-Instruct"),
              enforce_eager=True,
              seed=0)

    yield weakref.proxy(llm)

    del llm

    cleanup_dist_env_and_memory()


def test_chat(text_llm):
    prompt1 = "Explain the concept of entropy."
    messages = [
        {
            "role": "system",
            "content": "You are a helpful assistant"
        },
        {
            "role": "user",
            "content": prompt1
        },
    ]
    outputs = text_llm.chat(messages)
    assert len(outputs) == 1


def test_multi_chat(text_llm):
    prompt1 = "Explain the concept of entropy."
    prompt2 = "Explain what among us is."

    conversation1 = [
        {
            "role": "system",
            "content": "You are a helpful assistant"
        },
        {
            "role": "user",
            "content": prompt1
        },
    ]

    conversation2 = [
        {
            "role": "system",
            "content": "You are a helpful assistant"
        },
        {
            "role": "user",
            "content": prompt2
        },
    ]

    messages = [conversation1, conversation2]

    outputs = text_llm.chat(messages)
    assert len(outputs) == 2


@pytest.fixture(scope="function")
def vision_llm():
    # pytest caches the fixture so we use weakref.proxy to
    # enable garbage collection
    llm = LLM(
        model=os.path.join(models_path_prefix, "microsoft/Phi-3.5-vision-instruct"),
        max_model_len=4096,
        max_num_seqs=5,
        enforce_eager=True,
        trust_remote_code=True,
        limit_mm_per_prompt={"image": 2},
        seed=0,
    )

    yield weakref.proxy(llm)

    del llm

    cleanup_dist_env_and_memory()


@pytest.mark.parametrize("image_urls",
                         [[TEST_IMAGE_ASSETS[0], TEST_IMAGE_ASSETS[1]]],
                         indirect=True)
def test_chat_multi_image(vision_llm, image_urls: list[str]):
    messages = [{
        "role":
        "user",
        "content": [
            *({
                "type": "image_url",
                "image_url": {
                    "url": image_url
                }
            } for image_url in image_urls),
            {
                "type": "text",
                "text": "What's in this image?"
            },
        ],
    }]
    outputs = vision_llm.chat(messages)
    assert len(outputs) >= 0


def test_llm_chat_tokenization_no_double_bos(text_llm):
    """
    LLM.chat() should not add special tokens when using chat templates.
    Check we get a single BOS token for llama chat.
    """
    messages = [
        {
            "role": "system",
            "content": "You are a helpful assistant"
        },
        {
            "role": "user",
            "content": "Hello!"
        },
    ]
    outputs = text_llm.chat(messages)
    assert len(outputs) == 1

    prompt_token_ids = outputs[0].prompt_token_ids
    assert prompt_token_ids is not None

    bos_token = text_llm.get_tokenizer().bos_token_id

    # Ensure we have a single BOS
    assert prompt_token_ids[0] == bos_token
    assert prompt_token_ids[1] != bos_token, "Double BOS"


@pytest.fixture(scope="function")
def thinking_llm():
    # pytest caches the fixture so we use weakref.proxy to
    # enable garbage collection
    llm = LLM(
        model="Qwen/Qwen3-0.6B",
        max_model_len=4096,
        enforce_eager=True,
        seed=0,
    )

    yield weakref.proxy(llm)

    del llm

    cleanup_dist_env_and_memory()


@pytest.mark.parametrize("enable_thinking", [True, False])
def test_chat_extra_kwargs(thinking_llm, enable_thinking):
    messages = [
        {
            "role": "system",
            "content": "You are a helpful assistant"
        },
        {
            "role": "user",
            "content": "What is 1+1?"
        },
    ]

    outputs = thinking_llm.chat(
        messages,
        chat_template_kwargs={"enable_thinking": enable_thinking},
    )
    assert len(outputs) == 1

    prompt_token_ids = outputs[0].prompt_token_ids
    assert prompt_token_ids is not None

    think_id = thinking_llm.get_tokenizer().get_vocab()["<think>"]

    if enable_thinking:
        assert think_id not in prompt_token_ids
    else:
        # The chat template includes dummy thinking process
        assert think_id in prompt_token_ids
