# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Shape contract for ``QwenGatedDeltaNetAttention._output_projection``.

Every ``forward_*`` already allocates ``core_attn_out`` and ``z`` as
``(N, H, D)``. The helper now norms those tensors as-is and flattens once
for ``out_proj``; it used to be rank-agnostic via ``reshape(z_shape_og)``.
"""

from __future__ import annotations

import types

import pytest
import torch

from vllm.model_executor.layers.layernorm import RMSNormGated
from vllm.model_executor.layers.mamba.gdn.qwen_gdn_linear_attn import (
    QwenGatedDeltaNetAttention,
)


@pytest.mark.parametrize("dtype", [torch.float32])
@torch.inference_mode()
def test_output_projection_norms_per_head_and_flattens(
    default_vllm_config,
    dtype: torch.dtype,
) -> None:
    num_tokens, num_heads, head_dim = 3, 4, 8
    hidden = num_heads * head_dim

    layer = types.SimpleNamespace()
    layer.norm = RMSNormGated(
        head_dim,
        eps=1e-5,
        group_size=None,
        norm_before_gate=True,
        device="cpu",
        dtype=dtype,
    )
    layer.out_proj = lambda x: (x, None)
    layer._output_projection = types.MethodType(
        QwenGatedDeltaNetAttention._output_projection, layer
    )

    core_attn_out = torch.randn(num_tokens, num_heads, head_dim, dtype=dtype)
    z = torch.randn(num_tokens, num_heads, head_dim, dtype=dtype)
    out = layer._output_projection(core_attn_out, z)

    assert core_attn_out.shape == (num_tokens, num_heads, head_dim)
    assert z.shape == (num_tokens, num_heads, head_dim)
    assert out.shape == (num_tokens, hidden)
    assert out.dtype == dtype