refactor(tests): split into tests/unit and tests/tools

tests/tools/conftest.py

@@ -0,0 +1,56 @@
+"""Shared test fixtures for ai_mouse."""
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+
+from tools.models import TrajectoryFlowModel
+from tools.scroll.models import ScrollCVAE
+
+
+@pytest.fixture
+def model_dir(tmp_path: Path) -> Path:
+    """Create a temporary directory with trained Flow model artifacts."""
+    # Flow model
+    model = TrajectoryFlowModel(seq_len=64)
+    torch.save(model.state_dict(), tmp_path / "flow_model.pt")
+
+    # Click distribution
+    click_dist = {"mu": 80.0, "sigma": 30.0, "low": 20.0, "high": 300.0}
+    (tmp_path / "click_dist.json").write_text(json.dumps(click_dist))
+
+    # Duration distribution
+    dur_dist = {
+        "bins": [0, 50, 100, 200, 400, 600, 800, 1200, float("inf")],
+        "params": [{"mu_log": 5.5, "sigma_log": 0.5}] * 8,
+    }
+    (tmp_path / "duration_dist.json").write_text(json.dumps(dur_dist))
+
+    # Train config (architecture params)
+    train_cfg = {
+        "seq_len": 64,
+        "d_model": 128,
+        "nhead": 4,
+        "num_layers": 4,
+        "dim_feedforward": 256,
+        "cond_dim": 3,
+    }
+    (tmp_path / "train_config.json").write_text(json.dumps(train_cfg))
+
+    return tmp_path
+
+
+@pytest.fixture
+def scroll_model_dir(tmp_path: Path) -> Path:
+    """Create a temporary directory with trained scroll model artifacts."""
+    model = ScrollCVAE(seq_len=32)
+    torch.save(model.state_dict(), tmp_path / "scroll_model.pt")
+
+    scroll_cfg = {"seq_len": 32, "latent_dim": 16, "hidden": 64, "cond_dim": 7}
+    (tmp_path / "scroll_config.json").write_text(json.dumps(scroll_cfg))
+
+    return tmp_path