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


import shutil
from collections.abc import Sequence

import pytest
import torch
from safetensors.torch import load_file, save_file

import vllm
from vllm.lora.request import LoRARequest

from ..utils import multi_gpu_test

MODEL_PATH = "allenai/OLMoE-1B-7B-0125-Instruct"

PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me. Do not return any additional explanation. Below is an instruction that describes a task, Write a response that appropriately completes the request.
"
##Instruction:
candidate_poll contains tables such as candidate, people. Table candidate has columns such as Candidate_ID, People_ID, Poll_Source, Date, Support_rate, Consider_rate, Oppose_rate, Unsure_rate. Candidate_ID is the primary key.
Table people has columns such as People_ID, Sex, Name, Date_of_Birth, Height, Weight. People_ID is the primary key.
The People_ID of candidate is the foreign key of People_ID of people.


###Input:
{context}

###Response:"""  # noqa: E501

EXPECTED_LORA_OUTPUT = [
    "SELECT count(*) FROM candidate",
    "SELECT count(*) FROM candidate",
    "SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1",  # noqa: E501
    "SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1",  # noqa: E501
]

EXPECTED_BASE_MODEL_OUTPUT = [
    "SELECT COUNT(Candidate_ID) FROM candidate",
    "SELECT COUNT(Candidate_ID) FROM candidate",
    "SELECT Candidate_ID, COUNT(*) as Total_Candidates\nFROM candidate\nINNER JOIN people ON candidate.People_ID = people.People_ID",  # noqa: E501
    # There are multiple acceptable responses
    (
        "SELECT Candidate_ID, Poll_Source FROM candidate WHERE People_ID IN (SELECT People_ID FROM people) ORDER BY COUNT(*) DESC LIMIT 1",  # noqa: E501
        "SELECT Candidate_ID, Poll_Source FROM candidate WHERE COUNT(People_ID) = (SELECT COUNT(People_ID) FROM people) ORDER BY Candidate_ID DESC LIMIT 1",  # noqa: E501
    ),
]


def _output_matches(generated: str, accepted: str | Sequence[str]) -> bool:
    if isinstance(accepted, str):
        accepted = (accepted,)
    return any(generated.startswith(s) for s in accepted)


def generate_and_test(
    llm: vllm.LLM,
    lora_path: str,
    lora_id: list[int | None] | int | None,
    compare_lower: bool = False,
) -> None:
    prompts = [
        PROMPT_TEMPLATE.format(context="How many candidates are there?"),
        PROMPT_TEMPLATE.format(context="Count the number of candidates."),
        PROMPT_TEMPLATE.format(
            context="Which poll resource provided the most number of candidate information?"  # noqa: E501
        ),
        PROMPT_TEMPLATE.format(
            context="Return the poll resource associated with the most candidates."
        ),
    ]

    lora_request = None
    if isinstance(lora_id, int):
        lora_request = LoRARequest(str(lora_id), lora_id, lora_path)
    elif isinstance(lora_id, list):
        lora_request = [
            LoRARequest(str(i), i, lora_path) if i is not None else None
            for i in lora_id
        ]

    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
    outputs = llm.generate(prompts, sampling_params, lora_request=lora_request)
    # Print the outputs.
    generated_texts: list[str] = []
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text.strip()
        generated_texts.append(generated_text)
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

    for i in range(len(EXPECTED_LORA_OUTPUT)):
        req_lora_id = lora_id[i] if isinstance(lora_id, list) else lora_id
        generated_text = generated_texts[i]
        expected_output = (
            EXPECTED_LORA_OUTPUT[i]
            if req_lora_id is not None
            else EXPECTED_BASE_MODEL_OUTPUT[i]
        )

        if compare_lower:
            generated_text = generated_text.lower()
            if isinstance(expected_output, str):
                expected_output = (expected_output.lower(),)
            else:
                expected_output = tuple(s.lower() for s in expected_output)
        assert _output_matches(generated_text, expected_output), (
            f"Output {i}: {generated_text!r} does not match any of {expected_output!r}"
        )


def test_olmoe_lora(olmoe_lora_files):
    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
    # Otherwise, the lora-test will fail due to CUDA OOM.
    llm = vllm.LLM(
        MODEL_PATH,
        max_model_len=1024,
        enable_lora=True,
        max_loras=4,
        enforce_eager=True,
        trust_remote_code=True,
        enable_chunked_prefill=True,
    )

    generate_and_test(llm, olmoe_lora_files, lora_id=1)
    generate_and_test(llm, olmoe_lora_files, lora_id=2)


def test_olmoe_lora_mixed(olmoe_lora_files):
    llm = vllm.LLM(
        MODEL_PATH,
        max_model_len=1024,
        enable_lora=True,
        max_loras=4,
        enforce_eager=True,
        trust_remote_code=True,
        enable_chunked_prefill=True,
    )

    generate_and_test(llm, olmoe_lora_files, lora_id=[1, None, 3, None])


def test_olmoe_lora_mixed_random(olmoe_lora_files, tmp_path):
    # Create a dummy LoRA with random weights based on the real one
    random_lora_path = tmp_path / "random_lora"
    shutil.copytree(olmoe_lora_files, random_lora_path)

    weights_path = random_lora_path / "adapter_model.safetensors"
    weights = load_file(str(weights_path))
    random_weights = {k: torch.randn_like(v) for k, v in weights.items()}
    save_file(random_weights, str(weights_path))

    llm = vllm.LLM(
        MODEL_PATH,
        max_model_len=1024,
        enable_lora=True,
        max_loras=4,
        enforce_eager=True,
        trust_remote_code=True,
        enable_chunked_prefill=True,
    )

    prompts = [
        PROMPT_TEMPLATE.format(context="How many candidates are there?"),
        PROMPT_TEMPLATE.format(context="Count the number of candidates."),
    ]

    lora_requests = [
        LoRARequest("real", 1, olmoe_lora_files),
        LoRARequest("random", 2, str(random_lora_path)),
    ]

    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
    outputs = llm.generate(prompts, sampling_params, lora_request=lora_requests)
    assert outputs[0].outputs[0].text.strip().startswith(EXPECTED_LORA_OUTPUT[0])


@pytest.mark.parametrize("fully_sharded_loras", [False, True])
@multi_gpu_test(num_gpus=2)
def test_olmoe_lora_tp2(olmoe_lora_files, fully_sharded_loras):
    llm = vllm.LLM(
        MODEL_PATH,
        max_model_len=1024,
        enable_lora=True,
        max_loras=4,
        enforce_eager=True,
        trust_remote_code=True,
        enable_chunked_prefill=True,
        tensor_parallel_size=2,
        fully_sharded_loras=fully_sharded_loras,
    )

    generate_and_test(llm, olmoe_lora_files, lora_id=1)
    generate_and_test(llm, olmoe_lora_files, lora_id=2)


@pytest.mark.parametrize("fully_sharded_loras", [False, True])
@multi_gpu_test(num_gpus=4)
def test_olmoe_lora_tp4(olmoe_lora_files, fully_sharded_loras):
    llm = vllm.LLM(
        MODEL_PATH,
        max_model_len=1024,
        enable_lora=True,
        max_loras=4,
        enforce_eager=True,
        trust_remote_code=True,
        enable_chunked_prefill=True,
        tensor_parallel_size=4,
        fully_sharded_loras=fully_sharded_loras,
    )
    generate_and_test(
        llm, olmoe_lora_files, lora_id=1, compare_lower=fully_sharded_loras
    )
    generate_and_test(
        llm, olmoe_lora_files, lora_id=2, compare_lower=fully_sharded_loras
    )
