# SPDX-License-Identifier: Apache-2.0

import pytest

from vllm.lora.models import LoRAModel
from vllm.lora.peft_helper import PEFTHelper
from vllm.lora.utils import get_adapter_absolute_path
from vllm.model_executor.models.llama import LlamaForCausalLM

# Provide absolute path and huggingface lora ids
lora_fixture_name = ["sql_lora_files", "sql_lora_huggingface_id"]
LLAMA_LORA_MODULES = [
    "qkv_proj", "o_proj", "gate_up_proj", "down_proj", "embed_tokens",
    "lm_head"
]


@pytest.mark.parametrize("lora_fixture_name", lora_fixture_name)
def test_load_checkpoints_from_huggingface(lora_fixture_name, request):
    lora_name = request.getfixturevalue(lora_fixture_name)
    packed_modules_mapping = LlamaForCausalLM.packed_modules_mapping
    embedding_modules = LlamaForCausalLM.embedding_modules
    embed_padding_modules = LlamaForCausalLM.embedding_padding_modules
    expected_lora_modules: list[str] = []
    for module in LLAMA_LORA_MODULES:
        if module in packed_modules_mapping:
            expected_lora_modules.extend(packed_modules_mapping[module])
        else:
            expected_lora_modules.append(module)

    lora_path = get_adapter_absolute_path(lora_name)

    # lora loading should work for either absolute path and huggingface id.
    peft_helper = PEFTHelper.from_local_dir(lora_path, 4096)
    lora_model = LoRAModel.from_local_checkpoint(
        lora_path,
        expected_lora_modules,
        peft_helper=peft_helper,
        lora_model_id=1,
        device="cpu",
        embedding_modules=embedding_modules,
        embedding_padding_modules=embed_padding_modules)

    # Assertions to ensure the model is loaded correctly
    assert lora_model is not None, "LoRAModel is not loaded correctly"
