"""AttnRes depth mixer: switch, no double-register, causality, two-phase match."""
from dataclasses import asdict

import pytest
import torch

from kda.layers.attn_res import BlockAttnResStack, FullAttnResStack, atomic_block_size
from kda.layers.kda_attn import KDAAttention
from kda.models.causal_lm import CausalLM
from kda.models.config import KDAConfig
from kda.models.k3_config import K3Config
from kda.training.toy import load_ckpt, save_ckpt


def _tiny_k3(**kwargs):
    defaults = dict(
        hidden_size=32,
        num_hidden_layers=4,
        num_heads=4,
        head_dim=8,
        chunk_size=4,
        vocab_size=64,
        moe_latent_size=16,
        moe_d_ff=16,
        n_routed=4,
        top_k=2,
        n_shared=1,
        kv_lora_rank=8,
        q_lora_rank=16,
        qk_nope_head_dim=8,
        v_head_dim=8,
    )
    defaults.update(kwargs)
    return K3Config(**defaults)


def test_default_attnres_is_off():
    cfg = K3Config(num_hidden_layers=2)
    assert cfg.attnres == "off"
    model = CausalLM(cfg)
    assert model.mixer is None
    assert isinstance(model.blocks[0].attn, KDAAttention)


def test_invalid_attnres_rejected():
    with pytest.raises(ValueError, match="attnres"):
        K3Config(attnres="yes")
    with pytest.raises(ValueError, match="attnres_block_size"):
        K3Config(attnres="block", attnres_block_size=0)


@pytest.mark.parametrize("mode, stack_cls", [("block", BlockAttnResStack), ("full", FullAttnResStack)])
def test_mixer_kind_and_atomic_count(mode, stack_cls):
    cfg = _tiny_k3(attnres=mode, attnres_block_size=2)
    model = CausalLM(cfg)
    assert model.attnres == mode
    assert isinstance(model.mixer, stack_cls)
    assert len(model.mixer.layers) == 2 * cfg.num_hidden_layers
    if mode == "block":
        assert model.mixer.block_size == 4  # 2 DecoderBlocks × attn|ffn


def test_auto_block_size_targets_eight_blocks():
    assert atomic_block_size(24, None) == 6  # 3 DecoderBlocks × 2
    assert atomic_block_size(4, None) == 2
    assert atomic_block_size(93, None) == 24  # 12 DecoderBlocks × 2, K3 S=12


def test_no_duplicate_parameter_ids():
    model = CausalLM(_tiny_k3(attnres="block"))
    ids = [id(p) for p in model.parameters()]
    assert len(ids) == len(set(ids))
    names = [n for n, _ in model.named_parameters()]
    assert len(names) == len(set(names))
    residual_names = [n for n in names if "residuals" in n or "final_residual" in n]
    assert residual_names
    block_names = [n for n in names if n.startswith("blocks.")]
    mixer_weight_names = [
        n for n in names if n.startswith("mixer.layers.") and "query" not in n and "norm" not in n
    ]
    assert block_names
    assert mixer_weight_names == []


def test_off_and_block_differ_at_same_seed():
    torch.manual_seed(0)
    off = CausalLM(_tiny_k3(attnres="off"))
    torch.manual_seed(0)
    on = CausalLM(_tiny_k3(attnres="block"))
    x = torch.tensor([[1, 2, 3, 4]])
    with torch.no_grad():
        assert not torch.allclose(off(x), on(x))


def test_block_two_phase_matches_naive():
    torch.manual_seed(4)
    model = CausalLM(_tiny_k3(attnres="block", attnres_block_size=2)).eval()
    x = torch.randint(0, 64, (2, 8))
    with torch.no_grad():
        emb = model.embedding(x)
        naive = model.mixer.forward_naive(emb)
        two_phase = model.mixer(emb)
    torch.testing.assert_close(naive, two_phase, atol=1e-5, rtol=1e-5)


@pytest.mark.parametrize("mode", ["block", "full"])
def test_attnres_is_still_causal(mode):
    torch.manual_seed(51)
    model = CausalLM(_tiny_k3(attnres=mode, num_hidden_layers=2)).eval()
    with torch.no_grad():
        a = model(torch.tensor([[1, 2, 3, 4]]))
        b = model(torch.tensor([[1, 2, 3, 9]]))
    torch.testing.assert_close(a[:, :3], b[:, :3], atol=1e-5, rtol=1e-5)


def test_kda_config_block_runs():
    cfg = KDAConfig(
        hidden_size=16,
        num_hidden_layers=2,
        num_heads=2,
        num_value_heads=2,
        head_dim=4,
        chunk_size=4,
        vocab_size=32,
        intermediate_size=32,
        attnres="block",
        attnres_block_size=1,
    )
    model = CausalLM(cfg)
    logits = model(torch.tensor([[1, 2, 3, 4]]))
    assert logits.shape == (1, 4, 32)


def test_attnres_ckpt_roundtrip(tmp_path):
    cfg = _tiny_k3(attnres="block", attnres_block_size=2)
    model = CausalLM(cfg)
    path = str(tmp_path / "attnres.pt")
    save_ckpt(model, cfg, path)
    loaded, loaded_cfg = load_ckpt(path)
    assert loaded_cfg.attnres == "block"
    assert loaded_cfg.attnres_block_size == 2
    torch.manual_seed(1)
    x = torch.randint(0, cfg.vocab_size, (2, 8))
    with torch.no_grad():
        torch.testing.assert_close(model(x), loaded(x), atol=1e-5, rtol=1e-5)


def test_old_ckpt_without_attnres_stays_off(tmp_path):
    cfg = _tiny_k3()
    payload = asdict(cfg)
    payload.pop("attnres")
    payload.pop("attnres_block_size")
    payload.pop("attnres_zero_init_queries")
    payload.pop("attnres_final_aggregate")
    model = CausalLM(cfg)
    path = str(tmp_path / "legacy.pt")
    torch.save({"model_state": model.state_dict(), "config": payload}, path)
    _, loaded_cfg = load_ckpt(path)
    assert loaded_cfg.attnres == "off"
    assert loaded_cfg.attnres_block_size is None


def test_attnres_block_overfits_single_batch():
    torch.manual_seed(30)
    cfg = _tiny_k3(attnres="block", num_hidden_layers=2, attnres_block_size=1)
    model = CausalLM(cfg)
    x = torch.randint(0, cfg.vocab_size, (2, 8))
    optim = torch.optim.AdamW(model.parameters(), lr=3e-3)
    final = None
    for _ in range(200):
        optim.zero_grad()
        loss = model(x, labels=x)
        loss.backward()
        optim.step()
        final = loss.item()
    assert final < 0.5, f"final loss {final:.4f} >= 0.5"