# 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.
#
import json
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.aisbench import run_aisbench_cases
from .test_deepseek_r1_0528_w8a8 import *

aisbench_cases = [{
    "case_type": "accuracy",
    "dataset_path": "vllm-ascend/gsm8k-lite",
    "request_conf": "vllm_api_general_chat",
    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_chat_prompt",
    "max_out_len": 32768,
    "batch_size": 32,
    "baseline": 95,
    "threshold": 5
}]


@pytest.mark.asyncio
@pytest.mark.parametrize("model", MODELS)
async def test_models_eplb(model: str) -> None:
    port, env_dict, additional_config, server_args = config()
    additional_config.update(
        {
            "eplb_config": {
                "dynamic_eplb": "true",
                "expert_heat_collection_interval": 1000,
                "algorithm_execution_interval": 50,
                "eplb_policy_type": 3,
            }
        }
    )
    env_dict.update(
        {
            "DYNAMIC_EPLB": "true",
        }
    )
    server_args.extend(["--additional-config", json.dumps(additional_config)])
    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"
        print(choices)
        # aisbench test
        run_aisbench_cases(model,
                           port,
                           aisbench_cases,
                           server_args=server_args)

