# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""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
from unittest.mock import Mock

import pytest
import torch

from vllm import LLM
from vllm.platforms import current_platform
from vllm.v1.engine.llm_engine import LLMEngine

from ..conftest import HfRunner, VllmRunner
from ..models.utils import check_outputs_equal
from ..utils import multi_gpu_test
from vllm.platforms import current_platform
from ..utils import models_path_prefix

ATTN_BACKEND = ["ROCM_ATTN"] if current_platform.is_rocm() else ["FLASH_ATTN"]

MODELS = [
    os.path.join(models_path_prefix, "hmellor/tiny-random-Gemma2ForCausalLM"),
    os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B-Instruct"),
]

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


def test_vllm_gc_ed():
    """Verify vllm instance is GC'ed when it is deleted"""
    if not current_platform.is_rocm():
        llm = LLM(os.path.join(models_path_prefix, "hmellor/tiny-random-LlamaForCausalLM"))
    else:
        llm = LLM(os.path.join(models_path_prefix, "hmellor/tiny-random-LlamaForCausalLM"), block_size=64)
    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


def _fix_prompt_embed_outputs(
    vllm_outputs: list[tuple[list[int], str]],
    hf_model: HfRunner,
    example_prompts: list[str],
) -> list[tuple[list[int], str]]:
    fixed_vllm_outputs = []
    for vllm_output, hf_input, prompt in zip(
        vllm_outputs, hf_model.get_inputs(example_prompts), example_prompts
    ):
        hf_input_ids = hf_input["input_ids"].tolist()[0]
        fixed_vllm_outputs.append(
            (
                hf_input_ids + vllm_output[0][len(hf_input_ids) :],
                prompt + vllm_output[1],
            )
        )
    return fixed_vllm_outputs


@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("backend", ATTN_BACKEND)
@pytest.mark.parametrize("max_tokens", [5])
@pytest.mark.parametrize("enforce_eager", [False])
@pytest.mark.parametrize("async_scheduling", [True, False])
@pytest.mark.parametrize("model_executor", ["uni", "mp"])
@pytest.mark.parametrize("enable_prompt_embeds", [True, False])
def test_models(
    hf_runner,
    model: str,
    backend: str,
    max_tokens: int,
    enforce_eager: bool,
    async_scheduling: bool,
    model_executor: str,
    enable_prompt_embeds: bool,
) -> None:
    # 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) as hf_model:
        hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
        if enable_prompt_embeds:
            with torch.no_grad():
                prompt_embeds = hf_model.get_prompt_embeddings(example_prompts)

    if not current_platform.is_rocm():
        with VllmRunner(
            model,
            max_model_len=8192,
            enforce_eager=enforce_eager,
            enable_prompt_embeds=enable_prompt_embeds,
            gpu_memory_utilization=0.7,
            async_scheduling=async_scheduling,
            distributed_executor_backend=model_executor,
            attention_config={"backend": backend},
        ) as vllm_model:
            if enable_prompt_embeds:
                vllm_outputs = vllm_model.generate_greedy(prompt_embeds, max_tokens)
                vllm_outputs = _fix_prompt_embed_outputs(
                    vllm_outputs, hf_model, example_prompts
                )
            else:
                vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
    else:
        with VllmRunner(
            model,
            max_model_len=8192,
            enforce_eager=enforce_eager,
            enable_prompt_embeds=enable_prompt_embeds,
            gpu_memory_utilization=0.7,
            async_scheduling=async_scheduling,
            distributed_executor_backend=model_executor,
            attention_config={"backend": backend},
            block_size=64,
        ) as vllm_model:
            if enable_prompt_embeds:
                vllm_outputs = vllm_model.generate_greedy(prompt_embeds, max_tokens)
                vllm_outputs = _fix_prompt_embed_outputs(
                    vllm_outputs, hf_model, example_prompts
                )
            else:
                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, extra_env",
#     [
#         ("facebook/opt-125m", "ray", "", "L4", {}),
#         ("facebook/opt-125m", "mp", "", "L4", {}),
#         ("facebook/opt-125m", "ray", "", "L4", {"VLLM_SLEEP_WHEN_IDLE": "1"}),
#         ("facebook/opt-125m", "mp", "", "L4", {"VLLM_SLEEP_WHEN_IDLE": "1"}),
#         ("meta-llama/Llama-3.2-1B-Instruct", "ray", "", "L4", {}),
#         ("meta-llama/Llama-3.2-1B-Instruct", "mp", "", "L4", {}),
#         ("facebook/opt-125m", "ray", "", "A100", {}),
#         ("facebook/opt-125m", "mp", "", "A100", {}),
#     ],
# )
# @pytest.mark.parametrize("enable_prompt_embeds", [True, False])
# 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,
#     extra_env: dict[str, str],
#     enable_prompt_embeds: bool,
# ) -> 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"
#             and enable_prompt_embeds
#         ):  # noqa
#             pytest.skip("enable_prompt_embeds does not work with ray compiled dag.")

#         for k, v in extra_env.items():
#             monkeypatch_context.setenv(k, v)

#         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).
#         attention_config = {"backend": attention_backend} if attention_backend else None
#         with vllm_runner(
#             model,
#             dtype=dtype,
#             tensor_parallel_size=2,
#             distributed_executor_backend=distributed_executor_backend,
#             enable_prompt_embeds=enable_prompt_embeds,
#             gpu_memory_utilization=0.7,
#             attention_config=attention_config,
#         ) as vllm_model:
#             if enable_prompt_embeds:
#                 with hf_runner(model, dtype=dtype) as hf_model:
#                     with torch.no_grad():
#                         prompt_embeds = hf_model.get_prompt_embeddings(example_prompts)
#                     vllm_outputs = vllm_model.generate_greedy(prompt_embeds, max_tokens)
#                     vllm_outputs = _fix_prompt_embed_outputs(
#                         vllm_outputs, hf_model, example_prompts
#                     )
#                     hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
#             else:
#                 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",
#     )


def test_failed_model_execution(vllm_runner, monkeypatch) -> None:
    # Needed to mock an error in the same process
    monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")

    with vllm_runner(os.path.join(models_path_prefix, "facebook/opt-125m"), enforce_eager=True) as vllm_model:
        if isinstance(vllm_model.llm.llm_engine, LLMEngine):
            v1_test_failed_model_execution(vllm_model)


def v1_test_failed_model_execution(vllm_model):
    engine = vllm_model.llm.llm_engine
    mocked_execute_model = Mock(side_effect=RuntimeError("Mocked Critical Error"))
    engine.engine_core.engine_core.model_executor.execute_model = mocked_execute_model

    with pytest.raises(RuntimeError) as exc_info:
        prompts = [
            "Hello, my name is",
            "The president of the United States is",
            "The capital of France is",
            "The future of AI is",
        ]
        vllm_model.generate_greedy(prompts, 200, use_tqdm=False)
    assert isinstance(exc_info.value, RuntimeError)
    assert "Mocked Critical Error" in str(exc_info.value)
