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+"""llm_conf.sdpa_backends / llm_conf.torch_compile on both copies of FunASRNano (the package
+class and the recipe's model.py, imported by path). CPU-only, no weights: tiny stand-ins."""
+
+import importlib.util
+import os
+import sys
+import types
+
+import pytest
+import torch
+import torch.nn as nn
+
+from funasr.models.fun_asr_nano import model as funasr_model
+from funasr.register import tables
+
+REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+RECIPE_DIR = os.path.join(REPO, "examples", "industrial_data_pretraining", "fun_asr_nano")
+
+
+class _TinyDecoder(nn.Module):
+    """Stands in for the HF decoder stack (``llm.model``); records every eager call."""
+
+    def __init__(self, dim):
+        super().__init__()
+        self.proj = nn.Linear(dim, dim)
+        self.eager_calls = []
+
+    def forward(self, input_ids=None, inputs_embeds=None, **kwargs):
+        if not torch.compiler.is_compiling():
+            cudnn = torch.backends.cuda.cudnn_sdp_enabled()
+            self.eager_calls.append({"batch": int(inputs_embeds.shape[0]), "cudnn": cudnn})
+        return self.proj(inputs_embeds)
+
+
+class _TinyLLM(nn.Module):
+    def __init__(self, dim=8, vocab=16):
+        super().__init__()
+        self.model, self.embed = _TinyDecoder(dim), nn.Embedding(vocab, dim)
+
+    def get_input_embeddings(self):
+        return self.embed
+
+
+class _TinyEncoder(nn.Module):
+    def __init__(self, input_size=80, **kwargs):
+        super().__init__()
+        self.lin = nn.Linear(input_size, 4)
+
+    def output_size(self):
+        return 4
+
+
+class _TinyAdaptor(nn.Module):
+    def __init__(self, encoder_dim=4, llm_dim=8, **kwargs):
+        super().__init__()
+        self.lin = nn.Linear(encoder_dim, llm_dim)
+
+
+def _load_recipe_model():
+    previous = tables.model_classes.get("FunASRNano")
+    if RECIPE_DIR not in sys.path:
+        sys.path.insert(0, RECIPE_DIR)  # the recipe imports ``ctc`` and ``tools.utils`` bare
+    path = os.path.join(RECIPE_DIR, "model.py")
+    spec = importlib.util.spec_from_file_location("fun_asr_nano_recipe_model", path)
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module  # tables.register() calls inspect.getfile on the class
+    try:
+        spec.loader.exec_module(module)
+    except ImportError as exc:
+        sys.modules.pop(spec.name, None)
+        pytest.skip(f"recipe model.py not importable here: {exc}")
+    finally:
+        tables.model_classes["FunASRNano"] = previous  # the package class, registered at import
+    return module.FunASRNano
+
+
+@pytest.fixture(scope="module", params=["funasr", "recipe"])
+def model_class(request):
+    return funasr_model.FunASRNano if request.param == "funasr" else _load_recipe_model()
+
+
+@pytest.fixture
+def build(monkeypatch, model_class):
+    """``build(llm_conf) -> FunASRNano`` on the CPU, with the transformers loaders stubbed."""
+    fake = types.ModuleType("transformers")
+    fake.AutoConfig = types.SimpleNamespace(from_pretrained=lambda path, **kw: {})
+    fake.AutoModelForCausalLM = types.SimpleNamespace(from_config=lambda config, **kw: _TinyLLM())
+    monkeypatch.setattr(funasr_model, "AutoConfig", fake.AutoConfig)  # bound at import time
+    monkeypatch.setattr(funasr_model, "AutoModelForCausalLM", fake.AutoModelForCausalLM)
+    monkeypatch.setitem(sys.modules, "transformers", fake)  # the recipe imports in __init__
+    monkeypatch.setitem(tables.encoder_classes, "TinyEnc", _TinyEncoder)
+    monkeypatch.setitem(tables.adaptor_classes, "TinyAdp", _TinyAdaptor)
+    kw = dict(audio_encoder="TinyEnc", audio_adaptor="TinyAdp", llm="tiny")
+    return lambda c: model_class(audio_encoder_conf={}, audio_adaptor_conf={}, llm_conf=c, **kw)
+
+
+def _run(model, batch):
+    device = next(model.llm.parameters()).device
+    return model.llm.model.forward(inputs_embeds=torch.zeros(batch, 3, 8, device=device))
+
+
+def test_default_leaves_sdpa_flag_and_forward_alone(build):
+    flag = torch.backends.cuda.cudnn_sdp_enabled()
+    model = build({})
+    assert torch.backends.cuda.cudnn_sdp_enabled() == flag
+    assert "forward" not in model.llm.model.__dict__  # still the class method
+    _run(model, 2)
+    assert model.llm.model.eager_calls[-1]["cudnn"] == flag
+
+
+def test_sdpa_backends_disable_cudnn_only_inside_forward(build):
+    flag = torch.backends.cuda.cudnn_sdp_enabled()
+    model = build({"sdpa_backends": ["flash", "efficient", "math"]})
+    assert torch.backends.cuda.cudnn_sdp_enabled() == flag  # construction changed nothing
+    _run(model, 2)
+    assert model.llm.model.eager_calls[-1]["cudnn"] is False
+    assert torch.backends.cuda.cudnn_sdp_enabled() == flag  # restored on return
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="needs a CUDA device")
+def test_torch_compile_wraps_cuda_llm_and_keeps_batch_one_eager(build):
+    model = build({"torch_compile": True}).cuda()  # built on the CPU and moved, as the trainer does
+    decoder = model.llm.model
+    assert "forward" in decoder.__dict__
+    _run(model, 1)
+    assert [c["batch"] for c in decoder.eager_calls] == [1]
+    out = _run(model, 2)  # compiled: the stand-in does not record under Dynamo
+    assert [c["batch"] for c in decoder.eager_calls] == [1]
+    torch.testing.assert_close(out, decoder.proj(torch.zeros(2, 3, 8, device=out.device)))
+
+
+def test_torch_compile_keeps_cpu_decoder_eager(build):
+    model = build({"torch_compile": True})
+    _run(model, 1)
+    _run(model, 4)
+    assert [c["batch"] for c in model.llm.model.eager_calls] == [1, 4]