# 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

MODELS = [
    "vllm-ascend/DeepSeek-R1-0528-W8A8",
]

MODES = ["mtp2", "mtp3"]

prompts = [
    "San Francisco is a",
]

api_keyword_args = {
    "max_tokens": 10,
}

aisbench_gsm8k = [{
    "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
}]

aisbench_aime = [{
    "case_type": "accuracy",
    "dataset_path": "vllm-ascend/aime2024",
    "request_conf": "vllm_api_general_chat",
    "dataset_conf": "aime2024/aime2024_gen_0_shot_chat_prompt",
    "max_out_len": 32768,
    "batch_size": 32,
    "baseline": 86.67,
    "threshold": 7
}]


@pytest.mark.asyncio
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("mode", MODES)
async def test_models(model: str, mode: str) -> None:
    port = get_open_port()
    env_dict = {
        "OMP_NUM_THREADS": "100",
        "OMP_PROC_BIND": "false",
        "HCCL_BUFFSIZE": "1024",
        "VLLM_RPC_TIMEOUT": "3600000",
        "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS": "3600000"
    }
    speculative_config = {"num_speculative_tokens": 2, "method": "mtp"}
    compilation_config = {
        "cudagraph_capture_sizes": [56],
        "cudagraph_mode": "FULL_DECODE_ONLY"
    }
    server_args = [
        "--quantization",
        "ascend",
        "--seed",
        "1024",
        "--no-enable-prefix-caching",
        "--data-parallel-size",
        "2",
        "--tensor-parallel-size",
        "8",
        "--enable-expert-parallel",
        "--port",
        str(port),
        "--max-model-len",
        "40960",
        "--max-num-seqs",
        "14",
        "--trust-remote-code",
    ]
    if mode == "mtp2":
        server_args.extend(["--max-num-batched-tokens", "4096"])
        server_args.extend(
            ["--speculative-config",
             json.dumps(speculative_config)])
        server_args.extend(["--gpu-memory-utilization", "0.92"])
        aisbench_cases = aisbench_gsm8k
    if mode == "mtp3":
        env_dict["HCCL_OP_EXPANSION_MODE"] = "AIV"
        server_args.extend(["--max-num-batched-tokens", "2048"])
        speculative_config["num_speculative_tokens"] = 3
        server_args.extend(
            ["--speculative-config",
             json.dumps(speculative_config)])
        server_args.extend(["--gpu-memory-utilization", "0.9"])
        server_args.extend(
            ["--compilation-config",
             json.dumps(compilation_config)])
        aisbench_cases = aisbench_aime
    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)
