# 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.v1.engine.llm_engine import LLMEngine as LLMEngineV1

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

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")


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, "distilbert/distilgpt2"))
    else:
        llm = LLM(os.path.join(models_path_prefix, "distilbert/distilgpt2"), 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", ["FLASH_ATTN"])
@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(
    monkeypatch: pytest.MonkeyPatch,
    hf_runner,
    model: str,
    backend: str,
    max_tokens: int,
    enforce_eager: bool,
    async_scheduling: bool,
    model_executor: str,
    enable_prompt_embeds: bool,
) -> None:
    if backend == "XFORMERS" 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) 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,
            ) 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,
                    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", [
#         ("distilbert/distilgpt2", "ray", "", "L4", {}),
#         ("distilbert/distilgpt2", "mp", "", "L4", {}),
#         ("distilbert/distilgpt2", "ray", "", "L4", {
#             "VLLM_SLEEP_WHEN_IDLE": "1"
#         }),
#         ("distilbert/distilgpt2", "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", {}),
#         ("distilbert/distilgpt2", "ray", "", "A100", {}),
#         ("distilbert/distilgpt2", "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":  # noqa
#             if enable_prompt_embeds:
#                 pytest.skip(
#                     "enable_prompt_embeds does not work with ray compiled dag."
#                 )
#             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,
#             )

#         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).
#         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,
#         ) 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)


def test_failed_model_execution(vllm_runner, monkeypatch) -> None:

    from vllm.envs import VLLM_USE_V1

    if not VLLM_USE_V1:
        pytest.skip("Skipping V0 test, dump input not supported")

    # 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, LLMEngineV1):
            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)
