"""Phase 1 tests for ADR-0024 SIP-level TP launcher (MVP scope).

Covers:
- D1   world_size = SIP count fallback
- D9   get_rank greenlet-local + _bind_rank
- D10  torch.ahbm.set_device + torch.accelerator alias
- D11  tensor placement scoped to current device SIP
- D12/D13  run() spawns one greenlet per rank

Deferred to later ADR-0024 sub-phases:
- D2 engine-routed install
- D6 install_plan.py
- D7 epoch barrier (this phase uses simple submit+yield+wait)
- D8 validator registry
"""
from __future__ import annotations

import os
import textwrap

import pytest
from greenlet import greenlet

from kernbench.runtime_api.distributed import AhbmCCLBackend, DistributedContext


# ── Fixtures / helpers ────────────────────────────────────────────────


class _FakeCtx:
    """Minimal ctx double — only exposes what AhbmCCLBackend.__init__ uses.

    Stubs install_ipcq so we can unit-test _resolve_world_size without
    touching the engine stack.
    """

    def __init__(self, spec: dict) -> None:
        self.spec = spec
        self.install_calls: list[dict] = []

    def install_ipcq(self, **kwargs) -> dict:
        self.install_calls.append(dict(kwargs))
        return {}


def _write_minimal_ccl_yaml(tmp_path) -> str:
    """Write a ccl.yaml with NO world_size override — forces topology derivation."""
    body = textwrap.dedent("""\
        defaults:
          algorithm: ring_allreduce_tcm
          buffer_kind: tcm
          backpressure: sleep
          n_slots: 4
          slot_size: 4096
          vc_chunk_size: 256
          ipcq_credit_size_bytes: 16

        algorithms:
          ring_allreduce_tcm:
            module: kernbench.ccl.algorithms.ring_allreduce
            topology: ring_1d
            buffer_kind: tcm
            n_elem: 8
    """)
    yaml_path = tmp_path / "ccl.yaml"
    yaml_path.write_text(body)
    return str(tmp_path)


# ── D1: world_size = SIP count fallback ───────────────────────────────


def test_world_size_equals_sip_count(tmp_path, monkeypatch, spec):
    """With no override, backend derives world_size from SIP count only.

    Topology has 2 SIPs × 16 cubes × 8 PEs = 256 PEs. The TP/DP model
    places the collective group at the SIP boundary, so world_size must
    equal SIP count (2), not total PE count (256).
    """
    monkeypatch.chdir(_write_minimal_ccl_yaml(tmp_path))
    ctx = _FakeCtx(spec=spec)
    backend = AhbmCCLBackend(torch_ctx=ctx)
    expected = int(spec["system"]["sips"]["count"])
    assert backend.world_size == expected, (
        f"expected world_size == SIP count ({expected}); "
        f"got {backend.world_size} — still deriving sips × cubes × pes"
    )


# ── D9: get_rank greenlet-local + _bind_rank ──────────────────────────


def test_get_rank_is_greenlet_local(tmp_path, monkeypatch, spec):
    """Each greenlet sees its own rank via dist.get_rank().

    Framework-level launcher binds greenlet → rank; get_rank() resolves
    the current greenlet and returns that rank.
    """
    monkeypatch.chdir(_write_minimal_ccl_yaml(tmp_path))
    ctx = _FakeCtx(spec=spec)
    dc = DistributedContext()
    dc._ctx_ref = ctx
    dc.init_process_group(backend="ahbm")
    assert dc.get_world_size() == int(spec["system"]["sips"]["count"])

    assert hasattr(dc, "_bind_rank"), (
        "DistributedContext must expose _bind_rank(g, rank) hook"
    )

    seen: dict[int, int] = {}

    def _probe(rank: int) -> None:
        seen[rank] = dc.get_rank()

    g0 = greenlet(lambda: _probe(0))
    g1 = greenlet(lambda: _probe(1))
    dc._bind_rank(g0, 0)
    dc._bind_rank(g1, 1)
    g0.switch()
    g1.switch()

    assert seen == {0: 0, 1: 1}, (
        f"expected each greenlet to see its own rank; got {seen}"
    )


def test_get_rank_fallback_without_bind(tmp_path, monkeypatch, spec):
    """Unbound greenlet falls back to rank 0 (single-driver compat)."""
    monkeypatch.chdir(_write_minimal_ccl_yaml(tmp_path))
    ctx = _FakeCtx(spec=spec)
    dc = DistributedContext()
    dc._ctx_ref = ctx
    dc.init_process_group(backend="ahbm")
    # Call from main (unbound) greenlet
    assert dc.get_rank() == 0


# ── D10/D11: torch.ahbm.set_device + tensor scoping ───────────────────


def test_ahbm_set_device_binds_tensor_to_single_sip(topology):
    """``torch.ahbm.set_device(rank)`` + default-sip DPPolicy → tensor on SIP rank.

    After set_device(1), a tensor with DPPolicy leaving the SIP dimension
    at its default must be placed entirely on SIP 1.
    """
    from kernbench.policy.placement.dp import DPPolicy
    from kernbench.runtime_api.context import RuntimeContext
    from kernbench.runtime_api.types import DeviceSelector
    from kernbench.sim_engine.engine import GraphEngine

    engine = GraphEngine(topology.topology_obj, enable_data=True)
    with RuntimeContext(
        engine=engine,
        target_device=DeviceSelector("all"),
        correlation_id="test_ahbm_set_device",
        spec=topology.topology_obj.spec,
    ) as ctx:
        assert hasattr(ctx, "ahbm"), (
            "RuntimeContext must expose .ahbm namespace (ADR-0024 D10)"
        )
        ctx.ahbm.set_device(1)

        dp = DPPolicy(cube="column_wise", pe="column_wise")  # default sip
        tensor = ctx.zeros((1, 128), dtype="f16", dp=dp, name="probe")

        shard_sips = {s.sip for s in tensor._handle.shards}
        assert shard_sips == {1}, (
            f"after ahbm.set_device(1), all shards should live on SIP 1; "
            f"got sips={sorted(shard_sips)}"
        )


def test_accelerator_alias_mirrors_ahbm(topology):
    """torch.accelerator.set_device_index(r) is an alias for ahbm.set_device(r)
    (PyTorch 2.x device-agnostic surface)."""
    from kernbench.runtime_api.context import RuntimeContext
    from kernbench.runtime_api.types import DeviceSelector
    from kernbench.sim_engine.engine import GraphEngine

    engine = GraphEngine(topology.topology_obj, enable_data=True)
    with RuntimeContext(
        engine=engine,
        target_device=DeviceSelector("all"),
        correlation_id="test_accelerator_alias",
        spec=topology.topology_obj.spec,
    ) as ctx:
        assert hasattr(ctx, "accelerator"), (
            "RuntimeContext must expose .accelerator namespace (ADR-0024 D10)"
        )
        ctx.accelerator.set_device_index(1)
        # Both namespaces should report SIP 1 as current device
        assert ctx.ahbm.current_device() == 1
        assert ctx.accelerator.current_device_index() == 1


# ── D12/D13: run() spawns one worker per rank ─────────────────────────


def test_run_spawns_one_worker_per_rank(tmp_path, monkeypatch, spec):
    """The bench's ``run()`` invokes ``worker`` once per rank.

    With world_size = SIP count = 2 (topology), worker must be called
    exactly twice with ranks 0 and 1. Each call sees world_size=2.
    """
    project_root = os.path.abspath(
        os.path.join(os.path.dirname(__file__), "..")
    )
    monkeypatch.chdir(_write_minimal_ccl_yaml(tmp_path))

    import benches.ccl_allreduce as bench

    calls: list[tuple[int, int]] = []

    def _fake_worker(rank: int, world_size: int, torch) -> None:
        calls.append((rank, world_size))

    monkeypatch.setattr(bench, "worker", _fake_worker)

    from kernbench.runtime_api.context import RuntimeContext
    from kernbench.runtime_api.types import DeviceSelector
    from kernbench.sim_engine.engine import GraphEngine
    from kernbench.topology.builder import resolve_topology

    topo = resolve_topology(os.path.join(project_root, "topology.yaml"))
    engine = GraphEngine(topo.topology_obj, enable_data=True)
    with RuntimeContext(
        engine=engine,
        target_device=DeviceSelector("all"),
        correlation_id="test_run_spawns",
        spec=topo.topology_obj.spec,
    ) as ctx:
        bench.run(ctx)

    ranks = sorted(r for r, _ in calls)
    ws_values = {ws for _, ws in calls}
    expected_ws = int(spec["system"]["sips"]["count"])
    assert ranks == list(range(expected_ws)), (
        f"run() should invoke worker for ranks 0..{expected_ws - 1}; "
        f"saw ranks={ranks}"
    )
    assert ws_values == {expected_ws}, (
        f"each worker should see world_size={expected_ws}; saw {ws_values}"
    )