# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from transformers import AutoModelForSequenceClassification

from vllm.platforms import current_platform


@pytest.mark.parametrize(
    "model",
    [
        pytest.param(
            "jason9693/Qwen2.5-1.5B-apeach",
            marks=[
                pytest.mark.core_model,
                pytest.mark.cpu_model,
                pytest.mark.slow_test,
            ],
        ),
    ],
)
@pytest.mark.parametrize("dtype", ["half"] if current_platform.is_rocm() else ["float"])
def test_models(
    hf_runner,
    vllm_runner,
    example_prompts,
    model: str,
    dtype: str,
    monkeypatch,
) -> None:
    if current_platform.is_rocm():
        # ROCm Triton FA does not currently support sliding window attention
        # switch to use ROCm CK FA backend
        monkeypatch.setenv("VLLM_USE_TRITON_FLASH_ATTN", "False")

    with vllm_runner(model, max_model_len=512, dtype=dtype) as vllm_model:
        vllm_outputs = vllm_model.classify(example_prompts)

    with hf_runner(
        model, dtype=dtype, auto_cls=AutoModelForSequenceClassification
    ) as hf_model:
        hf_outputs = hf_model.classify(example_prompts)

    # check logits difference
    for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
        hf_output = torch.tensor(hf_output)
        vllm_output = torch.tensor(vllm_output)

        # the tolerance value of 1e-2 is selected based on the
        # half datatype tests in
        # tests/models/language/pooling/test_embedding.py
        assert torch.allclose(
            hf_output, vllm_output, 1e-3 if dtype == "float" else 1e-2
        )
