"""Config-driven multi-device allreduce test application.

Reads ``ccl.yaml`` + ``topology.yaml``, dynamically loads the kernel
module from ``ccl.yaml → module``, and picks the inter-SIP exchange
pattern from ``topology.yaml → system.sips.topology``.

Run directly::

    python -m pytest tests/allreduce_app.py -v -s
"""
from __future__ import annotations

import importlib
import math
from pathlib import Path
from typing import Any

import numpy as np

from kernbench.ccl.install import load_ccl_config, resolve_algorithm_config
from kernbench.ccl.sfr_config import configure_sfr_intercube_multisip
from kernbench.policy.placement.dp import DPPolicy


def _sip_topo_dims(sip_topo: str, n_sips: int) -> tuple[int, int]:
    if sip_topo == "ring_1d":
        return (0, 0)
    side = int(round(math.sqrt(n_sips)))
    if side * side != n_sips:
        raise ValueError(
            f"SIP topology '{sip_topo}' requires square n_sips, got {n_sips}"
        )
    return (side, side)


def run_allreduce(
    ctx: Any,
    engine: Any,
    spec: dict,
    *,
    algorithm: str | None = None,
    ccl_yaml: str | None = None,
) -> dict:
    """Config-driven allreduce: read yaml, load kernel, run.

    Everything is resolved from config — no hardcoded kernel imports.
    """
    cfg_all = load_ccl_config(ccl_yaml)
    cfg = resolve_algorithm_config(cfg_all, algorithm)

    # Dynamic import from ccl.yaml → module
    algo_module = importlib.import_module(cfg["module"])
    kernel_fn = algo_module.kernel
    topo_name_to_kind = algo_module.TOPO_NAME_TO_KIND

    n_elem = int(cfg.get("n_elem", 8))
    n_sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))
    sip_topo = str(
        spec.get("system", {}).get("sips", {}).get("topology", "ring_1d")
    )

    cm = spec["sip"]["cube_mesh"]
    cube_w = int(cm["w"])
    cube_h = int(cm["h"])
    n_cubes = cube_w * cube_h

    sip_topo_kind = topo_name_to_kind.get(sip_topo, 0)
    sip_topo_w, sip_topo_h = _sip_topo_dims(sip_topo, n_sips)

    algo_name = cfg.get("algorithm", "allreduce")
    print(f"\n{'=' * 60}")
    print(f"algorithm:        {algo_name}")
    print(f"module:           {cfg['module']}")
    print(f"sip_topology:     {sip_topo}")
    print(f"kernel:           {kernel_fn.__name__}")
    print(f"n_sips:           {n_sips}")
    print(f"n_cubes:          {n_cubes}")
    print(f"n_elem:           {n_elem}")
    print(f"{'=' * 60}")

    configure_sfr_intercube_multisip(engine, spec, cfg)

    dp = DPPolicy(
        cube="row_wise", pe="replicate",
        num_pes=1, num_cubes=n_cubes,
    )

    tensors = []
    for sip in range(n_sips):
        ctx.ahbm.set_device(sip)
        t = ctx.zeros(
            (n_cubes, n_elem), dtype="f16", dp=dp,
            name=f"sip{sip}",
        )
        t.copy_(ctx.from_numpy(
            np.full((n_cubes, n_elem), float(sip + 1), dtype=np.float16)
        ))
        tensors.append(t)

    for sip in range(n_sips):
        arr = tensors[sip].numpy()
        print(f"[SIP {sip}] input  cube0[:4] = {arr[0][:4].tolist()}  "
              f"cube{n_cubes - 1}[:4] = {arr[-1][:4].tolist()}")

    t_start = engine._env.now

    all_pending = []
    for sip_rank, t in enumerate(tensors):
        pending = ctx.launch(
            algo_name, kernel_fn, t,
            n_elem, cube_w, cube_h, n_sips, sip_rank,
            sip_topo_kind, sip_topo_w, sip_topo_h,
            _defer_wait=True,
        )
        all_pending.extend(pending)

    for h, sip_id, meta in all_pending:
        ctx.wait(h, _meta=meta)

    t_end = engine._env.now
    latency_ns = t_end - t_start
    print(f"\n[{algo_name} ws={n_sips}] sim latency = "
          f"{latency_ns:.1f} ns ({latency_ns / 1000:.3f} us)")

    for key, (_, trace) in engine._results.items():
        if not isinstance(trace, dict):
            continue
        total = trace.get("total_ns", 0.0)
        pe_exec = trace.get("pe_exec_ns", 0.0) or 0.0
        network = total - pe_exec
        print(f"  [{key}] total={total:.1f} ns  "
              f"pe_exec={pe_exec:.1f} ns  network={network:.1f} ns")

    expected = float(n_cubes * sum(range(1, n_sips + 1)))

    print()
    for sip in range(n_sips):
        arr = tensors[sip].numpy()
        print(f"[SIP {sip}] output cube0[:4]  = {arr[0][:4].tolist()}")
        print(f"[SIP {sip}] output cube{n_cubes - 1}[:4] = {arr[-1][:4].tolist()}")

    ok_cubes = 0
    for sip in range(n_sips):
        arr = tensors[sip].numpy()
        for cube_id in range(n_cubes):
            assert np.allclose(
                arr[cube_id], expected, rtol=1e-1, atol=1e-1,
            ), (
                f"SIP{sip} cube {cube_id}: "
                f"got {arr[cube_id][:4]}, expected {expected}"
            )
            ok_cubes += 1

    print(f"\n  {algo_name} (ws={n_sips}): {ok_cubes} OK")

    return {
        "expected": expected,
        "latency_ns": latency_ns,
        "ok_cubes": ok_cubes,
    }


# ── pytest entry point ───────────────────────────────────────────────

import pytest
import yaml

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

TOPOLOGY_PATH = Path(__file__).parent.parent / "topology.yaml"

CONFIGS = [
    pytest.param("intercube_allreduce", "ring_1d", 2, id="ring_2sip"),
    pytest.param("intercube_allreduce", "torus_2d", 4, id="torus_4sip"),
    pytest.param("intercube_allreduce", "mesh_2d_no_wrap", 4, id="mesh_4sip"),
]


def _write_temp_configs(tmp_path, sip_topology, n_sips, algorithm):
    """Write temp topology.yaml and ccl.yaml with the given overrides."""
    with open(TOPOLOGY_PATH) as f:
        topo_cfg = yaml.safe_load(f)
    topo_cfg["system"]["sips"]["count"] = n_sips
    topo_cfg["system"]["sips"]["topology"] = sip_topology
    topo_path = tmp_path / "topology.yaml"
    with open(topo_path, "w") as f:
        yaml.dump(topo_cfg, f, default_flow_style=False)

    ccl_path = Path(__file__).parent.parent / "ccl.yaml"
    with open(ccl_path) as f:
        ccl_cfg = yaml.safe_load(f)
    ccl_cfg["defaults"]["algorithm"] = algorithm
    tmp_ccl = tmp_path / "ccl.yaml"
    with open(tmp_ccl, "w") as f:
        yaml.dump(ccl_cfg, f, default_flow_style=False)

    return str(topo_path), str(tmp_ccl)


@pytest.mark.parametrize("algorithm,sip_topology,n_sips", CONFIGS)
def test_allreduce(tmp_path, algorithm, sip_topology, n_sips):
    topo_path, ccl_path = _write_temp_configs(
        tmp_path, sip_topology, n_sips, algorithm,
    )
    topo = resolve_topology(topo_path)
    engine = GraphEngine(topo.topology_obj, enable_data=True)
    spec = topo.topology_obj.spec

    with RuntimeContext(
        engine=engine,
        target_device=DeviceSelector("all"),
        correlation_id=f"test_{algorithm}_{sip_topology}",
        spec=spec,
    ) as ctx:
        result = run_allreduce(
            ctx, engine, spec,
            algorithm=algorithm, ccl_yaml=ccl_path,
        )
        assert result["ok_cubes"] > 0