# SPDX-License-Identifier: Apache-2.0
"""Compare the short outputs of HF and vLLM when using greedy sampling.

Run `pytest tests/basic_correctness/test_basic_correctness.py`.
"""
import os
import weakref

import pytest

from vllm import LLM
from vllm.platforms import current_platform

from ..conftest import VllmRunner
from ..models.utils import check_outputs_equal
from ..utils import multi_gpu_test
import os
from ..utils import models_path_prefix

MODELS = [
    os.path.join(models_path_prefix, "google/gemma-2-2b-it"),
    os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B-Instruct"),
]

TARGET_TEST_SUITE = os.environ.get("TARGET_TEST_SUITE", "L4")


@pytest.fixture(autouse=True)
def v1(run_with_both_engines):
    # Simple autouse wrapper to run both engines for each test
    # This can be promoted up to conftest.py to run for every
    # test in a package
    pass


def test_vllm_gc_ed():
    """Verify vllm instance is GC'ed when it is deleted"""
    llm = LLM(os.path.join(models_path_prefix, "distilbert/distilgpt2"))
    weak_llm = weakref.ref(llm)
    del llm
    # If there's any circular reference to vllm, this fails
    # because llm instance is not GC'ed.
    assert weak_llm() is None


@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("backend", ["FLASH_ATTN"])
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [5])
@pytest.mark.parametrize("enforce_eager", [False])
def test_models(
    monkeypatch: pytest.MonkeyPatch,
    hf_runner,
    model: str,
    backend: str,
    dtype: str,
    max_tokens: int,
    enforce_eager: bool,
) -> None:

    if backend == "FLASHINFER" and current_platform.is_rocm():
        pytest.skip("Flashinfer does not support ROCm/HIP.")

    if backend in ("XFORMERS",
                   "FLASHINFER") and model == os.path.join(models_path_prefix, "google/gemma-2-2b-it"):
        pytest.skip(
            f"{backend} does not support gemma2 with full context length.")

    with monkeypatch.context() as m:
        m.setenv("VLLM_ATTENTION_BACKEND", backend)

        # 5042 tokens for gemma2
        # gemma2 has alternating sliding window size of 4096
        # we need a prompt with more than 4096 tokens to test the sliding window
        prompt = "The following numbers of the sequence " + ", ".join(
            str(i) for i in range(1024)) + " are:"
        example_prompts = [prompt]

        with hf_runner(model, dtype=dtype) as hf_model:
            hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)

        with VllmRunner(model,
                        max_model_len=8192,
                        dtype=dtype,
                        enforce_eager=enforce_eager,
                        gpu_memory_utilization=0.7) as vllm_model:
            vllm_outputs = vllm_model.generate_greedy(example_prompts,
                                                      max_tokens)

        check_outputs_equal(
            outputs_0_lst=hf_outputs,
            outputs_1_lst=vllm_outputs,
            name_0="hf",
            name_1="vllm",
        )


# @multi_gpu_test(num_gpus=2)
# @pytest.mark.parametrize(
#     "model, distributed_executor_backend, attention_backend, "
#     "test_suite", [
#         ("distilbert/distilgpt2", "ray", "", "L4"),
#         ("distilbert/distilgpt2", "mp", "", "L4"),
#         ("meta-llama/Llama-3.2-1B-Instruct", "ray", "", "L4"),
#         ("meta-llama/Llama-3.2-1B-Instruct", "mp", "", "L4"),
#         ("distilbert/distilgpt2", "ray", "", "A100"),
#         ("distilbert/distilgpt2", "mp", "", "A100"),
#         ("distilbert/distilgpt2", "mp", "FLASHINFER", "A100"),
#         ("meta-llama/Meta-Llama-3-8B", "ray", "FLASHINFER", "A100"),
#     ])
# def test_models_distributed(
#     monkeypatch: pytest.MonkeyPatch,
#     hf_runner,
#     vllm_runner,
#     example_prompts,
#     model: str,
#     distributed_executor_backend: str,
#     attention_backend: str,
#     test_suite: str,
# ) -> None:

#     if test_suite != TARGET_TEST_SUITE:
#         pytest.skip(f"Skip test for {test_suite}")

#     with monkeypatch.context() as monkeypatch_context:
#         if model == "meta-llama/Llama-3.2-1B-Instruct" and distributed_executor_backend == "ray" and attention_backend == "" and test_suite == "L4":  # noqa
#             # test Ray Compiled Graph
#             monkeypatch_context.setenv("VLLM_USE_RAY_SPMD_WORKER", "1")
#             monkeypatch_context.setenv("VLLM_USE_RAY_COMPILED_DAG", "1")

#         if attention_backend:
#             monkeypatch_context.setenv(
#                 "VLLM_ATTENTION_BACKEND",
#                 attention_backend,
#             )

#         dtype = "half"
#         max_tokens = 5

#         # NOTE: take care of the order. run vLLM first, and then run HF.
#         # vLLM needs a fresh new process without cuda initialization.
#         # if we run HF first, the cuda initialization will be done and it
#         # will hurt multiprocessing backend with fork method
#         # (the default method).
#         with vllm_runner(
#                 model,
#                 dtype=dtype,
#                 tensor_parallel_size=2,
#                 distributed_executor_backend=distributed_executor_backend,
#         ) as vllm_model:
#             vllm_outputs = vllm_model.generate_greedy(example_prompts,
#                                                       max_tokens)

#         with hf_runner(model, dtype=dtype) as hf_model:
#             hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)

#         check_outputs_equal(
#             outputs_0_lst=hf_outputs,
#             outputs_1_lst=vllm_outputs,
#             name_0="hf",
#             name_1="vllm",
#         )
