import math
import random
from interplanetary_edge_orchestrator_privacy import Client, Server


def test_training_clipping_applies():
    random.seed(0)
    # Create a dataset with large feature values to encourage large updates
    X = [[100.0, 100.0], [100.0, -100.0]]
    y = [0.0, 0.0]
    c = Client(client_id=99, data_X=X, data_y=y, connected=True)
    c.initialize(n_features=2)
    w = [0.0, 0.0]

    # Clip updates to a small norm to enforce DP-like behavior
    update = c.train(w, lr=0.01, epochs=1, clip_norm=1.0)

    # verify that the resulting update has L2 norm <= clip_norm
    norm = math.sqrt(sum(v * v for v in update))
    assert norm <= 1.0 + 1e-9