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

import warnings

import os
import pytest
import torch.cuda

from vllm.model_executor.models import (is_pooling_model,
                                        is_text_generation_model,
                                        supports_multimodal)
from vllm.model_executor.models.adapters import (as_embedding_model,
                                                 as_reward_model,
                                                 as_seq_cls_model)
from vllm.model_executor.models.registry import (_MULTIMODAL_MODELS,
                                                 _SPECULATIVE_DECODING_MODELS,
                                                 _TEXT_GENERATION_MODELS,
                                                 ModelRegistry)
from vllm.platforms import current_platform

from ..utils import create_new_process_for_each_test
from .registry import HF_EXAMPLE_MODELS

models_path_prefix = os.getenv('VLLM_OPTEST_MODELS_PATH') or os.getenv("OPTEST_MODELS_PATH")


@pytest.mark.parametrize("model_arch", ModelRegistry.get_supported_archs())
def test_registry_imports(model_arch):
    # Skip if transformers version is incompatible
    model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
    model_info.check_transformers_version(on_fail="skip")
    # Ensure all model classes can be imported successfully
    model_cls = ModelRegistry._try_load_model_cls(model_arch)
    assert model_cls is not None

    if model_arch in _SPECULATIVE_DECODING_MODELS:
        return  # Ignore these models which do not have a unified format

    if (model_arch in _TEXT_GENERATION_MODELS
            or model_arch in _MULTIMODAL_MODELS):
        assert is_text_generation_model(model_cls)

    # All vLLM models should be convertible to a pooling model
    assert is_pooling_model(as_seq_cls_model(model_cls))
    assert is_pooling_model(as_embedding_model(model_cls))
    assert is_pooling_model(as_reward_model(model_cls))

    if model_arch in _MULTIMODAL_MODELS:
        assert supports_multimodal(model_cls)


@create_new_process_for_each_test()
@pytest.mark.parametrize("model_arch,is_mm,init_cuda,is_ce", [
    (os.path.join(models_path_prefix, "LlamaForCausalLM"), False, False, False),
    (os.path.join(models_path_prefix, "LlavaForConditionalGeneration"), True, True, False),
    (os.path.join(models_path_prefix, "BertForSequenceClassification"), False, False, True),
    (os.path.join(models_path_prefix, "RobertaForSequenceClassification"), False, False, True),
    (os.path.join(models_path_prefix, "XLMRobertaForSequenceClassification"), False, False, True),
])
def test_registry_model_property(model_arch, is_mm, init_cuda, is_ce):
    model_info = ModelRegistry._try_inspect_model_cls(model_arch)
    assert model_info is not None

    assert model_info.supports_multimodal is is_mm
    assert model_info.supports_cross_encoding is is_ce

    if init_cuda and current_platform.is_cuda_alike():
        assert not torch.cuda.is_initialized()

        ModelRegistry._try_load_model_cls(model_arch)
        if not torch.cuda.is_initialized():
            warnings.warn(
                "This model no longer initializes CUDA on import. "
                "Please test using a different one.",
                stacklevel=2)


@create_new_process_for_each_test()
@pytest.mark.parametrize(
    "model_arch,is_pp,init_cuda",
    [
        # TODO(woosuk): Re-enable this once the MLP Speculator is supported
        # in V1.
        # ("MLPSpeculatorPreTrainedModel", False, False),
        (os.path.join(models_path_prefix, "DeepseekV2ForCausalLM"), True, False),
        (os.path.join(models_path_prefix, "Qwen2VLForConditionalGeneration"), True, True),
    ])
def test_registry_is_pp(model_arch, is_pp, init_cuda):
    model_info = ModelRegistry._try_inspect_model_cls(model_arch)
    assert model_info is not None

    assert model_info.supports_pp is is_pp

    if init_cuda and current_platform.is_cuda_alike():
        assert not torch.cuda.is_initialized()

        ModelRegistry._try_load_model_cls(model_arch)
        if not torch.cuda.is_initialized():
            warnings.warn(
                "This model no longer initializes CUDA on import. "
                "Please test using a different one.",
                stacklevel=2)


def test_hf_registry_coverage():
    untested_archs = set(ModelRegistry.get_supported_archs() -
                         HF_EXAMPLE_MODELS.get_supported_archs())

    # Llama4ForCausalLM does not have a standalone model
    untested_archs.discard("Llama4ForCausalLM")

    assert not untested_archs, (
        "Please add the following architectures to "
        f"`tests/models/registry.py`: {untested_archs}")