"""Tests for multi-op tl.composite() with epilogue scopes.

Public-surface tests only: we exercise tl.composite() and inspect the
resulting CompositeCmd. Validation, plan generation, and scheduling are
covered implicitly — they're internal to tl_context / pe_scheduler / tiling.
"""
from __future__ import annotations

import pytest

from kernbench.common.pe_commands import (
    EPILOGUE_OPS,
    CompositeCmd,
    Scope,
    TensorHandle,
)
from kernbench.triton_emu.tl_context import TLContext


def _h(idx: int, shape=(32, 32)) -> TensorHandle:
    nbytes = 1
    for d in shape:
        nbytes *= d
    return TensorHandle(
        id=f"h{idx}", addr=0x1000 + idx * 0x100,
        shape=shape, dtype="f16", nbytes=nbytes * 2,
    )


def test_composite_epilogue_roundtrip():
    """tl.composite() with mixed-scope epilogue produces a CompositeCmd whose
    ops tuple preserves order, kinds, and default scopes."""
    tl = TLContext()
    a, b = _h(0), _h(1)
    bias = _h(2, shape=(32,))
    scale = _h(3, shape=(2,))

    tl.composite(
        op="gemm", a=a, b=b, out_ptr=0x2000,
        epilogue=[
            {"op": "bias",    "bias":  bias},   # default OUTPUT_TILE
            {"op": "dequant", "scale": scale},  # default K_TILE
            {"op": "relu"},                     # default OUTPUT_TILE
            {"op": "scale",   "factor": 0.5},
        ],
    )
    cmd = tl._commands[-1]
    assert isinstance(cmd, CompositeCmd)

    kinds_scopes = [(o.kind, o.scope) for o in cmd.ops]
    assert kinds_scopes == [
        ("gemm",    Scope.OUTPUT_TILE),
        ("bias",    Scope.OUTPUT_TILE),
        ("dequant", Scope.K_TILE),
        ("relu",    Scope.OUTPUT_TILE),
        ("scale",   Scope.OUTPUT_TILE),
    ]

    # Single-op call (no epilogue) keeps the legacy code path: ops stays empty.
    tl2 = TLContext()
    tl2.composite(op="gemm", a=a, b=b, out_ptr=0x2000)
    cmd2 = tl2._commands[-1]
    assert isinstance(cmd2, CompositeCmd)
    assert cmd2.ops == ()


@pytest.mark.parametrize("bad,match", [
    ([{"op": "biass"}],                     "unknown op 'biass'"),
    ([{"op": "bias"}],                      "missing required field"),
    (["relu"],                              "must be a dict"),
])
def test_composite_epilogue_rejects_bad_input(bad, match):
    tl = TLContext()
    with pytest.raises(ValueError, match=match):
        tl.composite(op="gemm", a=_h(0), b=_h(1), out_ptr=0x2000,
                     epilogue=bad)


def test_epilogue_registry_contract():
    """EPILOGUE_OPS is the registry tl.composite validates against."""
    for kind, (required, scope) in EPILOGUE_OPS.items():
        assert isinstance(kind, str) and kind
        assert isinstance(required, tuple)
        assert isinstance(scope, Scope)


def test_composite_epilogue_e2e():
    """Drive a GEMM + bias + relu composite through the simulator and check
    op_log: exactly one MATH(bias) and one MATH(relu) record, ordered after
    GEMM and before STORE for the single (m,n) output tile."""
    from pathlib import Path

    from kernbench.runtime_api.bench_runner import run_bench
    from kernbench.runtime_api.types import resolve_device
    from kernbench.sim_engine.engine import GraphEngine
    from kernbench.topology.builder import resolve_topology

    topo_path = Path(__file__).parent.parent / "topology.yaml"
    topo = resolve_topology(str(topo_path))
    device = resolve_device(None)

    def _kernel(a_ptr, b_ptr, bias_ptr, out_ptr, tl):
        a = tl.ref(int(a_ptr), shape=(32, 32), dtype="f16")
        b = tl.ref(int(b_ptr), shape=(32, 32), dtype="f16")
        bias = tl.load(int(bias_ptr), shape=(32,), dtype="f16")
        h = tl.composite(
            op="gemm", a=a, b=b, out_ptr=int(out_ptr),
            epilogue=[{"op": "bias", "bias": bias}, {"op": "relu"}],
        )
        tl.wait(h)

    def _bench(torch):
        from kernbench.policy.placement.dp import DPPolicy
        dp = DPPolicy(cube="replicate", pe="replicate",
                      num_cubes=1, num_pes=1)
        a = torch.empty((32, 32), dtype="f16", dp=dp, name="a")
        b = torch.empty((32, 32), dtype="f16", dp=dp, name="b")
        bias = torch.empty((32,), dtype="f16", dp=dp, name="bias")
        out = torch.empty((32, 32), dtype="f16", dp=dp, name="out")
        torch.launch("composite_epi", _kernel, a, b, bias, out)

    result = run_bench(
        topology=topo, bench_fn=_bench, device=device,
        engine_factory=lambda t, d: GraphEngine(
            getattr(t, "topology_obj", t), enable_data=True,
        ),
    )
    assert result.completion.ok

    math = [r for r in result.engine.op_log
            if r.params.get("stage_type") == "MATH"]
    assert [r.params.get("op_kind") for r in math] == ["bias", "relu"]

    gemm = [r for r in result.engine.op_log
            if r.params.get("stage_type") == "GEMM"]
    store = [r for r in result.engine.op_log
             if r.params.get("stage_type") == "STORE"]
    assert gemm and store
    assert gemm[0].t_end <= math[0].t_start + 1e-6
    assert math[-1].t_end <= store[0].t_start + 1e-6