#
# 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

import pytest
import vllm  # noqa: F401
from vllm.assets.image import ImageAsset

import vllm_ascend  # noqa: F401
from tests.conftest import VllmRunner

MODELS = [
    "Qwen/Qwen2.5-0.5B-Instruct",
    "vllm-ascend/Qwen2.5-0.5B-Instruct-w8a8",
    "Qwen/Qwen3-0.6B-Base",
]
MULTIMODALITY_MODELS = ["Qwen/Qwen2.5-VL-3B-Instruct"]
os.environ["VLLM_USE_MODELSCOPE"] = "True"
os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"


@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half", "float16"])
@pytest.mark.parametrize("max_tokens", [5])
def test_models(model: str, dtype: str, max_tokens: int) -> None:
    # 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 VllmRunner(model,
                    max_model_len=8192,
                    dtype=dtype,
                    enforce_eager=False,
                    gpu_memory_utilization=0.7) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


@pytest.mark.parametrize("model", MULTIMODALITY_MODELS)
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "1",
                    reason="qwen2.5_vl is not supported on v1")
def test_multimodal(model, prompt_template, vllm_runner):
    image = ImageAsset("cherry_blossom") \
        .pil_image.convert("RGB")
    img_questions = [
        "What is the content of this image?",
        "Describe the content of this image in detail.",
        "What's in the image?",
        "Where is this image taken?",
    ]
    images = [image] * len(img_questions)
    prompts = prompt_template(img_questions)
    with vllm_runner(model,
                     max_model_len=4096,
                     mm_processor_kwargs={
                         "min_pixels": 28 * 28,
                         "max_pixels": 1280 * 28 * 28,
                         "fps": 1,
                     }) as vllm_model:
        vllm_model.generate_greedy(prompts=prompts,
                                   images=images,
                                   max_tokens=64)


if __name__ == "__main__":
    import pytest
    pytest.main([__file__])