# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from dataclasses import MISSING, Field, asdict, dataclass, field

import pytest
import os

from vllm.compilation.backends import VllmBackend
from vllm.config import (LoadConfig, ModelConfig, PoolerConfig, VllmConfig,
                         get_field)
from vllm.model_executor.layers.pooler import PoolingType
from vllm.platforms import current_platform
from utils import models_path_prefix


def test_compile_config_repr_succeeds():
    # setup: VllmBackend mutates the config object
    config = VllmConfig()
    backend = VllmBackend(config)
    backend.configure_post_pass()

    # test that repr(config) succeeds
    val = repr(config)
    assert 'VllmConfig' in val
    assert 'inductor_passes' in val


@dataclass
class _TestConfigFields:
    a: int
    b: dict = field(default_factory=dict)
    c: str = "default"


def test_get_field():
    with pytest.raises(ValueError):
        get_field(_TestConfigFields, "a")

    b = get_field(_TestConfigFields, "b")
    assert isinstance(b, Field)
    assert b.default is MISSING
    assert b.default_factory is dict

    c = get_field(_TestConfigFields, "c")
    assert isinstance(c, Field)
    assert c.default == "default"
    assert c.default_factory is MISSING


@pytest.mark.parametrize(
    ("model_id", "expected_runner_type", "expected_task"),
    [
        (os.path.join(models_path_prefix, "distilbert/distilgpt2"), "generate", "generate"),
        (os.path.join(models_path_prefix, "intfloat/multilingual-e5-small"), "pooling", "embed"),
        (os.path.join(models_path_prefix, "jason9693/Qwen2.5-1.5B-apeach"), "pooling", "classify"),
        (os.path.join(models_path_prefix, "cross-encoder/ms-marco-MiniLM-L-6-v2"), "pooling", "classify"),
        (os.path.join(models_path_prefix, "Qwen/Qwen2.5-Math-RM-72B"), "pooling", "reward"),
        (os.path.join(models_path_prefix, "openai/whisper-small"), "transcription", "transcription"),
    ],
)
def test_auto_task(model_id, expected_runner_type, expected_task):
    config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
    )

    assert config.runner_type == expected_runner_type
    assert config.task == expected_task


@pytest.mark.parametrize(
    ("model_id", "expected_runner_type", "expected_task"),
    [
        (os.path.join(models_path_prefix, "distilbert/distilgpt2"), "pooling", "embed"),
        (os.path.join(models_path_prefix, "intfloat/multilingual-e5-small"), "pooling", "embed"),
        (os.path.join(models_path_prefix, "jason9693/Qwen2.5-1.5B-apeach"), "pooling", "classify"),
        (os.path.join(models_path_prefix, "cross-encoder/ms-marco-MiniLM-L-6-v2"), "pooling", "classify"),
        (os.path.join(models_path_prefix, "Qwen/Qwen2.5-Math-RM-72B"), "pooling", "embed"),
        (os.path.join(models_path_prefix, "openai/whisper-small"), "pooling", "embed"),
    ],
)
def test_score_task(model_id, expected_runner_type, expected_task):
    config = ModelConfig(
        model_id,
        task="score",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
    )

    assert config.runner_type == expected_runner_type
    assert config.task == expected_task


@pytest.mark.parametrize(("model_id", "bad_task"), [
    (os.path.join(models_path_prefix, "Qwen/Qwen2.5-Math-RM-72B"), "generate"),
])
def test_incorrect_task(model_id, bad_task):
    with pytest.raises(ValueError, match=r"does not support the .* task"):
        ModelConfig(
            model_id,
            task=bad_task,
            tokenizer=model_id,
            tokenizer_mode="auto",
            trust_remote_code=False,
            seed=0,
            dtype="float16",
        )


MODEL_IDS_EXPECTED = [
    (os.path.join(models_path_prefix, "Qwen/Qwen1.5-7B"), 32768),
    (os.path.join(models_path_prefix, "mistralai/Mistral-7B-v0.1"), 4096),
    (os.path.join(models_path_prefix, "mistralai/Mistral-7B-Instruct-v0.2"), 32768),
]


@pytest.mark.parametrize("model_id_expected", MODEL_IDS_EXPECTED)
def test_disable_sliding_window(model_id_expected):
    model_id, expected = model_id_expected
    model_config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
        disable_sliding_window=True,
    )
    assert model_config.max_model_len == expected


def test_get_sliding_window():
    TEST_SLIDING_WINDOW = 4096
    # Test that the sliding window is correctly computed.
    # For Qwen1.5/Qwen2, get_sliding_window() should be None
    # when use_sliding_window is False.
    qwen2_model_config = ModelConfig(
        os.path.join(models_path_prefix, "Qwen/Qwen1.5-7B"),
        task="auto",
        tokenizer=os.path.join(models_path_prefix, "Qwen/Qwen1.5-7B"),
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
    )

    qwen2_model_config.hf_config.use_sliding_window = False
    qwen2_model_config.hf_config.sliding_window = TEST_SLIDING_WINDOW
    assert qwen2_model_config.get_sliding_window() is None

    qwen2_model_config.hf_config.use_sliding_window = True
    assert qwen2_model_config.get_sliding_window() == TEST_SLIDING_WINDOW

    mistral_model_config = ModelConfig(
        os.path.join(models_path_prefix, "mistralai/Mistral-7B-v0.1"),
        task="auto",
        tokenizer=os.path.join(models_path_prefix, "mistralai/Mistral-7B-v0.1"),
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
    )
    mistral_model_config.hf_config.sliding_window = None
    assert mistral_model_config.get_sliding_window() is None

    mistral_model_config.hf_config.sliding_window = TEST_SLIDING_WINDOW
    assert mistral_model_config.get_sliding_window() == TEST_SLIDING_WINDOW


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_get_pooling_config():
    model_id = os.path.join(models_path_prefix, "sentence-transformers/all-MiniLM-L12-v2")
    model_config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
    )

    pooling_config = model_config._init_pooler_config()
    assert pooling_config is not None

    assert pooling_config.normalize
    assert pooling_config.pooling_type == PoolingType.MEAN.name


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_get_pooling_config_from_args():
    model_id = os.path.join(models_path_prefix, "sentence-transformers/all-MiniLM-L12-v2")
    model_config = ModelConfig(model_id,
                               task="auto",
                               tokenizer=model_id,
                               tokenizer_mode="auto",
                               trust_remote_code=False,
                               seed=0,
                               dtype="float16",
                               revision=None)

    override_pooler_config = PoolerConfig(pooling_type='CLS', normalize=True)
    model_config.override_pooler_config = override_pooler_config

    pooling_config = model_config._init_pooler_config()
    assert pooling_config is not None
    assert asdict(pooling_config) == asdict(override_pooler_config)


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_get_bert_tokenization_sentence_transformer_config():
    bge_model_config = ModelConfig(
        model="BAAI/bge-base-en-v1.5",
        task="auto",
        tokenizer="BAAI/bge-base-en-v1.5",
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
    )

    bert_bge_model_config = bge_model_config._get_encoder_config()

    assert bert_bge_model_config["max_seq_length"] == 512
    assert bert_bge_model_config["do_lower_case"]


def test_rope_customization():
    TEST_ROPE_SCALING = {"rope_type": "dynamic", "factor": 2.0}
    TEST_ROPE_THETA = 16_000_000.0
    LONGCHAT_ROPE_SCALING = {"rope_type": "linear", "factor": 8.0}

    llama_model_config = ModelConfig(
        os.path.join(models_path_prefix, "meta-llama/Meta-Llama-3-8B-Instruct"),
        task="auto",
        tokenizer=os.path.join(models_path_prefix, "meta-llama/Meta-Llama-3-8B-Instruct"),
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
    )
    assert getattr(llama_model_config.hf_config, "rope_scaling", None) is None
    assert getattr(llama_model_config.hf_config, "rope_theta", None) == 500_000
    assert llama_model_config.max_model_len == 8192

    llama_model_config = ModelConfig(
        os.path.join(models_path_prefix, "meta-llama/Meta-Llama-3-8B-Instruct"),
        task="auto",
        tokenizer=os.path.join(models_path_prefix, "meta-llama/Meta-Llama-3-8B-Instruct"),
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
        hf_overrides={
            "rope_scaling": TEST_ROPE_SCALING,
            "rope_theta": TEST_ROPE_THETA,
        },
    )
    assert getattr(llama_model_config.hf_config, "rope_scaling",
                   None) == TEST_ROPE_SCALING
    assert getattr(llama_model_config.hf_config, "rope_theta",
                   None) == TEST_ROPE_THETA
    assert llama_model_config.max_model_len == 16384

    longchat_model_config = ModelConfig(
        os.path.join(models_path_prefix, "lmsys/longchat-13b-16k"),
        task="auto",
        tokenizer=os.path.join(models_path_prefix, "lmsys/longchat-13b-16k"),
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
    )
    # Check if LONGCHAT_ROPE_SCALING entries are in longchat_model_config
    assert all(
        longchat_model_config.hf_config.rope_scaling.get(key) == value
        for key, value in LONGCHAT_ROPE_SCALING.items())
    assert longchat_model_config.max_model_len == 16384

    longchat_model_config = ModelConfig(
        os.path.join(models_path_prefix, "lmsys/longchat-13b-16k"),
        task="auto",
        tokenizer=os.path.join(models_path_prefix, "lmsys/longchat-13b-16k"),
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
        hf_overrides={
            "rope_scaling": TEST_ROPE_SCALING,
        },
    )
    assert getattr(longchat_model_config.hf_config, "rope_scaling",
                   None) == TEST_ROPE_SCALING
    assert longchat_model_config.max_model_len == 4096


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Encoder Decoder models not supported on ROCm.")
@pytest.mark.parametrize(("model_id", "is_encoder_decoder"), [
    (os.path.join(models_path_prefix, "facebook/opt-125m"), False),
    (os.path.join(models_path_prefix, "facebook/bart-base"), True),
    (os.path.join(models_path_prefix, "meta-llama/Llama-3.2-1B-Instruct"), False),
    (os.path.join(models_path_prefix, "meta-llama/Llama-3.2-11B-Vision"), True),
])
def test_is_encoder_decoder(model_id, is_encoder_decoder):
    config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
    )

    assert config.is_encoder_decoder == is_encoder_decoder


@pytest.mark.parametrize(("model_id", "uses_mrope"), [
    (os.path.join(models_path_prefix, "facebook/opt-125m"), False),
    (os.path.join(models_path_prefix, "Qwen/Qwen2-VL-2B-Instruct"), True),
])
def test_uses_mrope(model_id, uses_mrope):
    config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        dtype="float16",
        seed=0,
    )

    assert config.uses_mrope == uses_mrope


def test_generation_config_loading():
    model_id = os.path.join(models_path_prefix, "Qwen/Qwen2.5-1.5B-Instruct")

    # When set generation_config to "vllm", the default generation config
    # will not be loaded.
    model_config = ModelConfig(model_id,
                               task="auto",
                               tokenizer=model_id,
                               tokenizer_mode="auto",
                               trust_remote_code=False,
                               seed=0,
                               dtype="float16",
                               generation_config="vllm")
    assert model_config.get_diff_sampling_param() == {}

    # When set generation_config to "auto", the default generation config
    # should be loaded.
    model_config = ModelConfig(model_id,
                               task="auto",
                               tokenizer=model_id,
                               tokenizer_mode="auto",
                               trust_remote_code=False,
                               seed=0,
                               dtype="float16",
                               generation_config="auto")

    correct_generation_config = {
        "repetition_penalty": 1.1,
        "temperature": 0.7,
        "top_p": 0.8,
        "top_k": 20,
    }

    assert model_config.get_diff_sampling_param() == correct_generation_config

    # The generation config could be overridden by the user.
    override_generation_config = {"temperature": 0.5, "top_k": 5}

    model_config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        generation_config="auto",
        override_generation_config=override_generation_config)

    override_result = correct_generation_config.copy()
    override_result.update(override_generation_config)

    assert model_config.get_diff_sampling_param() == override_result

    # When generation_config is set to "vllm" and override_generation_config
    # is set, the override_generation_config should be used directly.
    model_config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        generation_config="vllm",
        override_generation_config=override_generation_config)

    assert model_config.get_diff_sampling_param() == override_generation_config


@pytest.mark.parametrize("pt_load_map_location", [
    "cuda",
    {
        "": "cuda"
    },
])
def test_load_config_pt_load_map_location(pt_load_map_location):
    load_config = LoadConfig(pt_load_map_location=pt_load_map_location)
    config = VllmConfig(load_config=load_config)

    assert config.load_config.pt_load_map_location == pt_load_map_location


@pytest.mark.parametrize(
    ("model_id", "max_model_len", "expected_max_len", "should_raise"), [
        ("BAAI/bge-reranker-base", None, 512, False),
        ("BAAI/bge-reranker-base", 256, 256, False),
        ("BAAI/bge-reranker-base", 513, 512, True),
        ("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", None, 131072, False),
        ("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", 131073, 131072, True),
    ])
def test_get_and_verify_max_len(model_id, max_model_len, expected_max_len,
                                should_raise):
    """Test get_and_verify_max_len with different configurations."""
    model_config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="float16",
        revision=None,
    )

    if should_raise:
        with pytest.raises(ValueError):
            model_config.get_and_verify_max_len(max_model_len)
    else:
        actual_max_len = model_config.get_and_verify_max_len(max_model_len)
        assert actual_max_len == expected_max_len
