Remove nested directory: BitTransformerLM/tests/test_dashboard.py

BitTransformerLM/tests/test_dashboard.py

@@ -1,90 +0,0 @@
-import os, sys; sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-
-import torch
-import mcp_server as dash
-from mcp_server import app
-from bit_transformer.dashboard_app import ModelManager, MetricDriftWarning
-from bit_transformer import BitTransformerLM
-from bit_transformer.optimization import configure_optimizer
-from bit_transformer.bit_io import text_to_bits
-import time
-
-
-def test_exec_endpoint_removed():
-    with app.test_client() as client:
-        resp = client.post("/exec", json={"code": "print('OK')"})
-        assert resp.status_code in (403, 404)
-
-
-def test_status_endpoint(tmp_path):
-    dash.manager = ModelManager(snapshot_dir=tmp_path)
-    params = {"d_model": 16, "nhead": 2, "num_layers": 1, "dim_feedforward": 32, "max_seq_len": 8}
-    with app.test_client() as client:
-        client.post("/init", json=params)
-        resp = client.get("/status")
-        data = resp.get_json()
-        assert data["d_model"] == 16 and data["num_layers"] == 1
-
-
-def test_modelmanager_compression(tmp_path):
-    mgr = ModelManager(snapshot_dir=tmp_path)
-    mgr.model = BitTransformerLM(d_model=16, nhead=2, num_layers=1, dim_feedforward=32, max_seq_len=8)
-    mgr.optimizer, mgr.scheduler = configure_optimizer(mgr.model, lr=1e-3, total_steps=1)
-    mgr.set_compression(True)
-    bits = torch.randint(0, 2, (1, 8), dtype=torch.long)
-    loss, ratio = mgr.train_step(bits)
-    assert isinstance(loss, float) and 0 <= ratio <= 1.0
-
-
-def test_metric_drift_warning(tmp_path):
-    mgr = ModelManager(snapshot_dir=tmp_path, drift_window=2, drift_threshold=0.1)
-    tele = {
-        "negentropy_logits": torch.tensor([0.0]),
-        "lz_complexity_logits": torch.tensor([0.0]),
-        "symbiosis_score": torch.tensor([0.0]),
-    }
-    for _ in range(4):
-        mgr._log_metrics(tele)
-    tele_drift = {
-        "negentropy_logits": torch.tensor([1.0]),
-        "lz_complexity_logits": torch.tensor([0.0]),
-        "symbiosis_score": torch.tensor([0.0]),
-    }
-    import pytest
-
-    with pytest.warns(MetricDriftWarning):
-        mgr._log_metrics(tele_drift)
-
-
-def test_dashboard_endpoints(tmp_path):
-    dash.manager = ModelManager(snapshot_dir=tmp_path)
-    params = {"d_model": 16, "nhead": 2, "num_layers": 1, "dim_feedforward": 32, "max_seq_len": 8}
-    with app.test_client() as client:
-        resp = client.post("/init", json=params)
-        assert resp.status_code == 200
-        bits = torch.randint(0, 2, (1, 8), dtype=torch.long).tolist()
-        train = client.post("/train", json={"bits": bits})
-        assert train.status_code == 200
-        job_id = train.get_json()["job_id"]
-        for _ in range(20):
-            job_resp = client.get(f"/job/{job_id}")
-            data = job_resp.get_json()
-            if data["status"] == "completed":
-                assert "loss" in data["result"]
-                break
-            time.sleep(0.1)
-        else:
-            assert False, "training job did not complete"
-        infer = client.post("/infer", json={"bits": bits})
-        assert infer.status_code == 200 and "predicted" in infer.get_json()
-
-
-def test_text_to_bits_and_dataset(tmp_path):
-    dash.manager = ModelManager(snapshot_dir=tmp_path)
-    with app.test_client() as client:
-        resp = client.post("/text_to_bits", json={"text": "hi"})
-        assert resp.status_code == 200
-        assert resp.get_json()["bits"] == text_to_bits("hi")
-        ds = client.get("/dataset?name=wikitext2&split=train&size=1&seq_len=8")
-        data = ds.get_json()
-        assert len(data["bits"]) == 1 and len(data["bits"][0]) == 8