# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
#
from typing import Any

import openai
import pytest
from vllm.utils.network_utils import get_open_port

from tests.e2e.conftest import RemoteOpenAIServer
from tools.vllm_bench import run_vllm_bench_case

MODELS = [
    "Qwen/Qwen3-8B",
]

prompts = [
    "San Francisco is a",
]

api_keyword_args = {
    "max_tokens": 10,
}

vllm_bench_cases = {
    "dataset-name": "random",
    "num_prompts": 500,
    "request_rate": 20,
    "random_input_len": 128,
    "max_concurrency": 40,
    "random_output_len": 100,
    "temperature": 0.0,
}

# NOTE: Any changes for the baseline throughput should be approved by team members.
# The origin baseline: 1600.0. For some uncertain reasons, the throughput is decreased to 1514.0
baseline_throughput = 1514.0  # baseline throughput for Qwen3-8B, measured with num_prompts=500


@pytest.mark.parametrize("model", MODELS)
@pytest.mark.asyncio
async def test_models(model: str) -> None:
    port = get_open_port()
    env_dict = {
        "TASK_QUEUE_ENABLE": "1",
        "HCCL_OP_EXPANSION_MODE": "AIV",
    }
    server_args = [
        "--async-scheduling",
        "--distributed-executor-backend",
        "mp",
        "--tensor-parallel-size",
        "1",
        "--port",
        str(port),
        "--max-model-len",
        "5500",
        "--max-num-batched-tokens",
        "40960",
        "--compilation-config",
        '{"cudagraph_mode": "FULL_DECODE_ONLY"}',
        "--additional-config",
        '{"pa_shape_list":[48,64,72,80],"weight_prefetch_config":{"enabled":true}}',
        "--block-size",
        "128",
        "--trust-remote-code",
        "--gpu-memory-utilization",
        "0.9",
    ]

    request_keyword_args: dict[str, Any] = {
        **api_keyword_args,
    }
    with RemoteOpenAIServer(model, server_args, server_port=port, env_dict=env_dict, auto_port=False) as server:
        client = server.get_async_client()
        batch = await client.completions.create(
            model=model,
            prompt=prompts,
            **request_keyword_args,
        )
        choices: list[openai.types.CompletionChoice] = batch.choices
        assert choices[0].text, "empty response"
        # vllm bench test
        run_vllm_bench_case(model, port, vllm_bench_cases, baseline_throughput)