# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import NamedTuple

import pytest
import torch
from packaging.version import Version
from transformers import __version__ as TRANSFORMERS_VERSION

from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.platforms import current_platform
from vllm.transformers_utils.config import get_config
from vllm.utils.torch_utils import set_random_seed

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def generate_test_data(
    num_tokens: int,
    num_q_heads: int,
    num_kv_heads: int,
    head_size: int,
    max_position_embeddings: int,
    dtype: torch.dtype,
    device: torch.device,
):
    """Generate test data for given configuration."""
    set_random_seed(42)
    # Create 2D positions (3, num_tokens) for multimodal case
    positions = torch.randint(
        0, max_position_embeddings // 4, (3, num_tokens), device=device
    )

    # Create query and key tensors
    query = torch.randn(num_tokens, num_q_heads * head_size, dtype=dtype, device=device)
    key = torch.randn(num_tokens, num_kv_heads * head_size, dtype=dtype, device=device)

    return positions, query, key


class MRoPETestInfo(NamedTuple):
    model_name: str
    # https://github.com/pytorch/pytorch/blob/main/torch/testing/_comparison.py#L1317
    atol: float = 1e-2
    rtol: float = 1.6e-2
    marks: list[pytest.MarkDecorator] = []


TRANSFORMERS_BASE_VERSION = Version(TRANSFORMERS_VERSION).base_version

MODELS_TO_TEST = [
    MRoPETestInfo(model_name="zai-org/GLM-4.1V-9B-Thinking"),
    MRoPETestInfo(model_name="Qwen/Qwen2-VL-7B-Instruct"),
    MRoPETestInfo(model_name="Qwen/Qwen2-VL-72B-Instruct"),
    MRoPETestInfo(model_name="Qwen/Qwen2.5-VL-72B-Instruct"),
    MRoPETestInfo(
        model_name="Qwen/Qwen3-VL-4B-Instruct",
        marks=[
            pytest.mark.skipif(
                Version(TRANSFORMERS_BASE_VERSION) < Version("4.57.0"),
                reason="Qwen3-VL only available after Transformers v4.57",
            )
        ],
    ),
    MRoPETestInfo(
        model_name="Qwen/Qwen3-VL-30B-A3B-Instruct",
        marks=[
            pytest.mark.skipif(
                Version(TRANSFORMERS_BASE_VERSION) < Version("4.57.0"),
                reason="Qwen3-VL only available after Transformers v4.57",
            )
        ],
    ),
]

num_tokens_list = [11, 8192]


@pytest.mark.skipif(
    not current_platform.is_cuda_alike(), reason="Skipping CUDA/ROCm only tests."
)
@pytest.mark.parametrize(
    "model_info, model_name",
    [
        pytest.param(test_config, test_config.model_name, marks=test_config.marks)
        for test_config in MODELS_TO_TEST
    ],
)
@pytest.mark.parametrize("tp_size", [1, 2])
@pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("num_tokens", num_tokens_list)
def test_mrope(
    default_vllm_config,
    model_name: str,
    model_info: MRoPETestInfo,
    tp_size: int,
    dtype: torch.dtype,
    num_tokens: int,
):
    atol = model_info.atol
    rtol = model_info.rtol

    config = get_config(model_name, False).get_text_config()

    # get the model config
    total_num_kv_heads = config.num_key_value_heads
    total_num_heads = config.num_attention_heads
    num_heads = total_num_heads // tp_size
    num_kv_heads = max(1, total_num_kv_heads // tp_size)
    head_dim = (
        config.head_dim
        if hasattr(config, "head_dim")
        else config.hidden_size // total_num_heads
    )
    is_neox_style = True

    max_position = config.max_position_embeddings

    mrope_helper_class = get_rope(
        head_size=head_dim,
        max_position=max_position,
        is_neox_style=is_neox_style,
        rope_parameters=config.rope_parameters,
        dtype=dtype,
    ).to(device=device)

    # create q k v input tensors
    # create rotary pos emb input tensors
    positions, query, key = generate_test_data(
        num_tokens, num_heads, num_kv_heads, head_dim, max_position, dtype, device
    )

    query_native, key_native = mrope_helper_class.forward_native(
        positions,
        query.clone(),
        key.clone(),
    )

    query_cuda, key_cuda = mrope_helper_class.forward_cuda(
        positions,
        query.clone(),
        key.clone(),
    )

    torch.testing.assert_close(query_native, query_cuda, atol=atol, rtol=rtol)
    torch.testing.assert_close(key_native, key_cuda, atol=atol, rtol=rtol)


@pytest.mark.skipif(
    not current_platform.is_cuda_alike(), reason="Skipping CUDA/ROCm only tests."
)
@pytest.mark.parametrize(
    "model_info, model_name",
    [
        pytest.param(test_config, test_config.model_name, marks=test_config.marks)
        for test_config in MODELS_TO_TEST
    ],
)
@pytest.mark.parametrize("tp_size", [1, 2])
@pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("num_tokens", num_tokens_list)
def test_mrope_torch_compile_tracing(
    default_vllm_config,
    model_name: str,
    model_info: MRoPETestInfo,
    tp_size: int,
    dtype: torch.dtype,
    num_tokens: int,
):
    atol = model_info.atol
    rtol = model_info.rtol

    config = get_config(model_name, False).get_text_config()

    # get the model config
    total_num_kv_heads = config.num_key_value_heads
    total_num_heads = config.num_attention_heads
    num_heads = total_num_heads // tp_size
    num_kv_heads = max(1, total_num_kv_heads // tp_size)
    head_dim = (
        config.head_dim
        if hasattr(config, "head_dim")
        else config.hidden_size // total_num_heads
    )
    is_neox_style = True
    max_position = config.max_position_embeddings

    mrope_helper_class = get_rope(
        head_size=head_dim,
        max_position=max_position,
        is_neox_style=is_neox_style,
        rope_parameters=config.rope_parameters,
        dtype=dtype,
    ).to(device=device)

    # Generate test data
    positions, query, key = generate_test_data(
        num_tokens, num_heads, num_kv_heads, head_dim, max_position, dtype, device
    )

    # Create a wrapper that makes the in-place function appear functional
    def functional_forward_cuda(pos, q, k):
        """Wrapper that converts in-place operation to functional style

        CUDA Graph does not support in-place operations.
        This wrapper creates working copies of the
        input tensors and modifies them.
        """
        q_work = q.clone()  # Create working copies
        k_work = k.clone()
        # Your in-place function modifies q_work and k_work
        mrope_helper_class.forward_cuda(pos, q_work, k_work)
        return q_work, k_work  # Return the modified tensors

    # Get reference results
    query_native, key_native = mrope_helper_class.forward_native(
        positions,
        query.clone(),
        key.clone(),
    )

    try:
        compiled_forward_cuda = torch.compile(
            functional_forward_cuda,
            fullgraph=True,
            backend="inductor",
            mode="reduce-overhead",
            dynamic=False,
        )

        # Run compiled version
        query_compiled_cuda, key_compiled_cuda = compiled_forward_cuda(
            positions,
            query,
            key,
        )

        # Run original version for comparison
        query_cuda = query.clone()
        key_cuda = key.clone()
        mrope_helper_class.forward_cuda(positions, query_cuda, key_cuda)

        # Verify results
        torch.testing.assert_close(
            query_compiled_cuda, query_cuda, atol=atol, rtol=rtol
        )
        torch.testing.assert_close(key_compiled_cuda, key_cuda, atol=atol, rtol=rtol)
        torch.testing.assert_close(
            query_compiled_cuda, query_native, atol=atol, rtol=rtol
        )
        torch.testing.assert_close(key_compiled_cuda, key_native, atol=atol, rtol=rtol)

        print("✓ forward_cuda successfully traced with torch.compile inductor")

    except Exception as e:
        pytest.fail(f"forward_cuda failed to trace with torch.compile inductor: {e}")
