#
# 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.
# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
#
"""Compare the short outputs of HF and vLLM when using greedy sampling.

Run `pytest tests/test_offline_inference.py`.
"""
import os
from unittest.mock import patch

from modelscope import snapshot_download  # type: ignore
from vllm import SamplingParams
from vllm.model_executor.models.registry import ModelRegistry

from tests.conftest import VllmRunner

os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"


def test_models_distributed_QwQ():
    example_prompts = [
        "Hello, my name is",
    ]
    dtype = "half"
    max_tokens = 5
    with VllmRunner(
            "Qwen/QwQ-32B",
            dtype=dtype,
            tensor_parallel_size=4,
            distributed_executor_backend="mp",
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


def test_models_distributed_DeepSeek_multistream_moe():
    example_prompts = [
        "Hello, my name is",
    ]
    dtype = "half"
    max_tokens = 5
    with VllmRunner(
            "vllm-ascend/DeepSeek-V3-Pruning",
            dtype=dtype,
            tensor_parallel_size=4,
            distributed_executor_backend="mp",
            additional_config={
                "torchair_graph_config": {
                    "enabled": True,
                    "enable_multistream_moe": True,
                },
                "ascend_scheduler_config": {
                    "enabled": True,
                },
                "refresh": True,
            },
            enforce_eager=False,
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DBO": "1"})
def test_models_distributed_DeepSeek_dbo():
    example_prompts = ["The president of the United States is"] * 41
    dtype = "half"
    sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
    with VllmRunner(
            "deepseek-ai/DeepSeek-V2-Lite",
            dtype=dtype,
            tensor_parallel_size=4,
            distributed_executor_backend="mp",
    ) as vllm_model:
        model_arch = 'DeepseekV2ForCausalLM'
        registed_models = ModelRegistry.models
        assert registed_models[
            model_arch].module_name == "vllm_ascend.models.deepseek_dbo"
        assert registed_models[
            model_arch].class_name == "CustomDeepseekDBOForCausalLM"
        vllm_model.generate(example_prompts, sampling_params)


@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DBO": "1"})
def test_models_distributed_DeepSeekV3_dbo():
    example_prompts = ["The president of the United States is"] * 41
    dtype = "half"
    sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
    with VllmRunner(
            "vllm-ascend/DeepSeek-V3-Pruning",
            dtype=dtype,
            tensor_parallel_size=4,
            distributed_executor_backend="mp",
    ) as vllm_model:
        model_arch = 'DeepseekV3ForCausalLM'
        registed_models = ModelRegistry.models
        assert registed_models[
            model_arch].module_name == "vllm_ascend.models.deepseek_dbo"
        assert registed_models[
            model_arch].class_name == "CustomDeepseekDBOForCausalLM"
        vllm_model.generate(example_prompts, sampling_params)


def test_models_distributed_DeepSeek_W8A8():
    example_prompts = [
        "Hello, my name is",
    ]
    max_tokens = 5

    with VllmRunner(
            snapshot_download("vllm-ascend/DeepSeek-V2-Lite-W8A8"),
            max_model_len=8192,
            enforce_eager=True,
            dtype="auto",
            tensor_parallel_size=4,
            quantization="ascend",
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


def test_models_distributed_pangu():
    example_prompts = [
        "Hello, my name is",
    ]
    max_tokens = 5

    with VllmRunner(
            snapshot_download("vllm-ascend/pangu-pro-moe-pruing"),
            max_model_len=8192,
            enforce_eager=True,
            dtype="auto",
            tensor_parallel_size=4,
            distributed_executor_backend="mp",
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)