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

import weakref

import pytest
import torch

from tests.models.utils import softmax
from vllm import LLM, PoolingParams
from vllm.distributed import cleanup_dist_env_and_memory

MODEL_NAME = "jason9693/Qwen2.5-1.5B-apeach"

prompts = ["The chef prepared a delicious meal."]


@pytest.fixture(scope="module")
def llm():
    # pytest caches the fixture so we use weakref.proxy to
    # enable garbage collection
    llm = LLM(
        model=MODEL_NAME,
        max_num_batched_tokens=32768,
        tensor_parallel_size=1,
        gpu_memory_utilization=0.75,
        enforce_eager=True,
        seed=0,
    )

    yield weakref.proxy(llm)

    del llm

    cleanup_dist_env_and_memory()


@pytest.mark.skip_global_cleanup
def test_pooling_params(llm: LLM):
    def get_outputs(activation):
        outputs = llm.classify(
            prompts, pooling_params=PoolingParams(activation=activation), use_tqdm=False
        )
        return torch.tensor([x.outputs.probs for x in outputs])

    default = get_outputs(activation=None)
    w_activation = get_outputs(activation=True)
    wo_activation = get_outputs(activation=False)

    assert torch.allclose(default, w_activation, atol=1e-2), (
        "Default should use activation."
    )
    assert not torch.allclose(w_activation, wo_activation, atol=1e-2), (
        "wo_activation should not use activation."
    )
    assert torch.allclose(softmax(wo_activation), w_activation, atol=1e-2), (
        "w_activation should be close to activation(wo_activation)."
    )


@pytest.mark.skip_global_cleanup
def test_encode_api(llm: LLM):
    # chunked prefill does not support all pooling
    err_msg = "pooling_task must be one of.+"
    with pytest.raises(ValueError, match=err_msg):
        llm.encode(prompts, pooling_task="token_classify", use_tqdm=False)


def test_score_api(llm: LLM):
    err_msg = "Score API is only enabled for num_labels == 1."
    with pytest.raises(ValueError, match=err_msg):
        llm.score("ping", "pong", use_tqdm=False)
