from typing import NamedTuple

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

from sglang.srt.layers.rotary_embedding import get_rope
from sglang.srt.utils import (
    cpu_has_amx_support,
    is_cpu,
    is_cuda,
    is_hip,
    is_npu,
    is_xpu,
)

_is_cuda = is_cuda()
_is_hip = is_hip()
_is_cpu = is_cpu()
_is_cpu_amx_available = cpu_has_amx_support()
_is_npu = is_npu()
_is_xpu = is_xpu()

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."""
    torch.manual_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
    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="Qwen/Qwen2-VL-7B-Instruct"),
    MRoPETestInfo(model_name="Qwen/Qwen2-VL-72B-Instruct"),
    MRoPETestInfo(model_name="Qwen/Qwen2.5-VL-72B-Instruct"),
]

num_tokens_list = [11, 8192]


@pytest.mark.skipif(not _is_cuda, 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(
    model_name: str,
    model_info: MRoPETestInfo,
    tp_size: int,
    dtype: torch.dtype,
    num_tokens: int,
):
    atol = model_info.atol
    rtol = model_info.rtol

    config = AutoConfig.from_pretrained(model_name)
    config = config.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

    rope_theta = config.rope_theta
    max_position = config.max_position_embeddings
    partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
    rotary_dim = int(head_dim * partial_rotary_factor)

    mrope_helper_class = get_rope(
        head_size=head_dim,
        rotary_dim=rotary_dim,
        max_position=max_position,
        base=rope_theta,
        is_neox_style=is_neox_style,
        rope_scaling=config.rope_scaling,
        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(
        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)