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