"""Regression tests for post-training merges (tiny-model-posttrain).

Covers the two measured 2026-08-13 merge bugs on the 50M 16k line:
  - base pretrain checkpoints carry mtp_heads.* keys that folded
    post-training checkpoints lack (must intersect keys, never KeyError)
  - trim_delta flattened its mask before indexing the tensor (IndexError)
"""

import torch

from train.ties_merge import ties_merge, trim_delta


def test_trim_delta_preserves_top_fraction_shape():
    delta = torch.randn(8, 5)
    out = trim_delta(delta, keep=0.2)
    assert out.shape == delta.shape
    nonzero = (out != 0).sum().item()
    assert nonzero > 0
    assert nonzero <= delta.numel()  # top-20% per tensor, never more


def test_ties_merge_ignores_missing_task_keys():
    base = {"w1": torch.randn(4, 4), "mtp_heads.0.weight": torch.randn(4, 4)}
    task = {"w1": torch.randn(4, 4)}  # folded ckpt: no mtp keys
    out = ties_merge(base, [task, task], keep=0.5)
    assert "w1" in out
    assert "mtp_heads.0.weight" not in out


def test_soup_taskarith_intersect_keys():
    from train.parallel_merges import model_soup, task_arithmetic
    base = {"w1": torch.randn(4, 4), "mtp_heads.0.weight": torch.randn(4, 4)}
    t1 = {"w1": torch.randn(4, 4)}
    t2 = {"w1": torch.randn(4, 4)}
    soup = model_soup([t1, t2])
    assert set(soup.keys()) == {"w1"}
    ta = task_arithmetic(base, [t1, t2], lam=0.5)
    assert set(ta.keys()) == {"w1"}
    assert torch.allclose(ta["w1"], base["w1"] + 0.5 * ((t1["w1"] - base["w1"]) + (t2["w1"] - base["w1"])))