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

import pytest

import vllm
from vllm.lora.request import LoRARequest
from vllm.platforms import current_platform

from ..utils import multi_gpu_test

MODEL_PATH = "openai/gpt-oss-20b"

PROMPT_TEMPLATE = """<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-10-29

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>user<|message|>I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.
"
##Instruction:
farm contains tables such as city, farm, farm_competition, competition_record. Table city has columns such as City_ID, Official_Name, Status, Area_km_2, Population, Census_Ranking. City_ID is the primary key.
Table farm has columns such as Farm_ID, Year, Total_Horses, Working_Horses, Total_Cattle, Oxen, Bulls, Cows, Pigs, Sheep_and_Goats. Farm_ID is the primary key.
Table farm_competition has columns such as Competition_ID, Year, Theme, Host_city_ID, Hosts. Competition_ID is the primary key.
Table competition_record has columns such as Competition_ID, Farm_ID, Rank. Competition_ID is the primary key.
The Host_city_ID of farm_competition is the foreign key of City_ID of city.
The Farm_ID of competition_record is the foreign key of Farm_ID of farm.
The Competition_ID of competition_record is the foreign key of Competition_ID of farm_competition.


###Input:
{context}

###Response:<|end|><|start|>assistant<|channel|>final<|message|>"""  # noqa: E501

EXPECTED_LORA_OUTPUT = [
    "SELECT avg(Working_Horses) FROM farm WHERE Total_Horses  >  5000",
    "SELECT max(Cows) ,  min(Cows) FROM farm",
    "SELECT max(Cows) ,  min(Cows) FROM farm",
]


def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int) -> None:
    prompts = [
        PROMPT_TEMPLATE.format(
            context="Give the average number of working horses on farms with more than 5000 total horses."  # noqa: E501
        ),  # noqa: E501
        PROMPT_TEMPLATE.format(
            context="What are the maximum and minimum number of cows across all farms."
        ),
        PROMPT_TEMPLATE.format(
            context="Return the maximum and minimum number of cows across all farms."
        ),
    ]
    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
    outputs = llm.generate(
        prompts,
        sampling_params,
        lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
    )
    # Print the outputs.
    generated_texts: list[str] = []
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text.strip()
        generated_texts.append(generated_text)
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
    for i in range(len(EXPECTED_LORA_OUTPUT)):
        assert generated_texts[i].startswith(EXPECTED_LORA_OUTPUT[i])


@pytest.mark.skipif(
    not current_platform.is_cuda(),
    reason=(
        "Mxfp4 LoRA on ROCm is blocked by a spawn compatibility issue. "
        "The fused_moe_lora Triton kernel crashes in spawned subprocesses, "
        "and vLLM forces spawn mode when HIP is initialized before "
        "multiprocessing. Fixing this requires either making the LoRA "
        "Triton kernel spawn-safe or pre-warming the kernel cache."
    ),
)
@pytest.mark.parametrize("mxfp4_use_marlin", [True, False])
@pytest.mark.parametrize("specialize_active_lora", [True, False])
def test_gpt_oss_lora(
    monkeypatch: pytest.MonkeyPatch,
    gptoss20b_lora_files,
    mxfp4_use_marlin,
    specialize_active_lora,
):
    with monkeypatch.context() as m:
        m.setenv("VLLM_MXFP4_USE_MARLIN", "1" if mxfp4_use_marlin else "0")
        llm = vllm.LLM(
            MODEL_PATH,
            max_model_len=1024,
            enable_lora=True,
            max_loras=4,
            max_lora_rank=8,
            max_num_seqs=2,
            max_num_batched_tokens=2048,
            specialize_active_lora=specialize_active_lora,
            compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
                cudagraph_specialize_lora=False,
            ),
        )

        generate_and_test(llm, gptoss20b_lora_files, lora_id=1)
        generate_and_test(llm, gptoss20b_lora_files, lora_id=2)


@multi_gpu_test(num_gpus=2)
@pytest.mark.parametrize("fully_sharded_loras", [False, True])
@pytest.mark.parametrize("mxfp4_use_marlin", [True, False])
def test_gpt_oss_lora_tp2(
    monkeypatch: pytest.MonkeyPatch,
    gptoss20b_lora_files,
    fully_sharded_loras,
    mxfp4_use_marlin,
):
    with monkeypatch.context() as m:
        m.setenv("VLLM_MXFP4_USE_MARLIN", "1" if mxfp4_use_marlin else "0")
        llm = vllm.LLM(
            MODEL_PATH,
            max_model_len=1024,
            enable_lora=True,
            max_loras=2,
            max_num_seqs=2,
            max_num_batched_tokens=2048,
            tensor_parallel_size=2,
            gpu_memory_utilization=0.8,
            fully_sharded_loras=fully_sharded_loras,
            compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
                cudagraph_specialize_lora=False,
            ),
        )

        generate_and_test(llm, gptoss20b_lora_files, lora_id=1)
        generate_and_test(llm, gptoss20b_lora_files, lora_id=2)
