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"""
Fast unit tests for student agent with progress bars.
Optimized for speed with tqdm progress bars:
- Shows progress during slow operations (model loading, training, evaluation)
- Shared student instance where possible
- Reduced iteration counts for fast tests
- Minimal evaluation sets
"""
import sys
from student_agent import StudentAgent
from mock_task_generator import MockTaskGenerator
import torch
try:
from tqdm import tqdm
HAS_TQDM = True
except ImportError:
HAS_TQDM = False
print("⚠️ tqdm not installed. Install with: pip install tqdm")
# Dummy tqdm if not available
class tqdm:
def __init__(self, iterable=None, *args, **kwargs):
self.iterable = iterable
def __enter__(self):
return self.iterable
def __exit__(self, *args):
pass
def __iter__(self):
return iter(self.iterable) if self.iterable else iter([])
def update(self, n=1):
pass
def test_student_can_load():
"""Test DistilBERT loads successfully (or graceful fallback)."""
print("Testing student initialization...", end=" ", flush=True)
# Model loading can be slow - show that we're working
try:
student = StudentAgent(device='cpu')
print("✅ Student model initialized")
return student
except Exception as e:
print(f"⚠️ Error: {e}")
raise
def test_student_can_answer():
"""Test student can predict answers."""
print("Testing answer prediction...", end=" ", flush=True)
student = StudentAgent(device='cpu')
generator = MockTaskGenerator()
task = generator.generate_task('history', 'easy')
answer = student.answer(task)
assert 0 <= answer < 4, f"Answer should be 0-3, got {answer}"
print("✅ Student can answer tasks")
def test_student_learns():
"""Test student improves with practice (with progress bar)."""
print("Testing learning capability...", flush=True)
student = StudentAgent(device='cpu')
generator = MockTaskGenerator()
topic = 'science'
# Smaller eval set for speed
print(" Generating eval set...", end=" ", flush=True)
eval_tasks = [generator.generate_task(topic, 'easy') for _ in range(5)]
print("Done")
# Measure initial accuracy
print(" Evaluating initial accuracy...", end=" ", flush=True)
initial_acc = student.evaluate(eval_tasks)
print(f"{initial_acc:.3f}")
# Training with progress bar
num_iterations = 15
print(f" Training on {num_iterations} tasks:")
if HAS_TQDM:
pbar = tqdm(range(num_iterations), desc=" Progress", leave=False)
for i in pbar:
task = generator.generate_task(topic, 'easy')
student.learn(task)
pbar.set_postfix({'tasks': i+1})
else:
# Fallback: simple progress indicator
for i in range(num_iterations):
if (i + 1) % 5 == 0:
print(f" {i+1}/{num_iterations}...", end="\r", flush=True)
task = generator.generate_task(topic, 'easy')
student.learn(task)
print(f" {num_iterations}/{num_iterations} ") # Clear line
# Measure final accuracy
print(" Evaluating final accuracy...", end=" ", flush=True)
final_acc = student.evaluate(eval_tasks)
print(f"{final_acc:.3f}")
improvement = final_acc - initial_acc
print(f"✅ Learning verified (improvement: {improvement:+.3f})")
def test_student_forgets():
"""Test memory decay works (with progress bar)."""
print("Testing memory decay...", flush=True)
student = StudentAgent(device='cpu', retention_constant=20.0)
generator = MockTaskGenerator()
topic = 'literature'
# Training with progress bar
num_iterations = 20
print(f" Training on {num_iterations} tasks:")
if HAS_TQDM:
pbar = tqdm(range(num_iterations), desc=" Progress", leave=False)
for i in pbar:
task = generator.generate_task(topic, 'easy')
student.learn(task)
pbar.set_postfix({'tasks': i+1})
else:
for i in range(num_iterations):
if (i + 1) % 5 == 0:
print(f" {i+1}/{num_iterations}...", end="\r", flush=True)
task = generator.generate_task(topic, 'easy')
student.learn(task)
print(f" {num_iterations}/{num_iterations} ")
print(" Evaluating before forgetting...", end=" ", flush=True)
eval_tasks = [generator.generate_task(topic, 'easy') for _ in range(5)]
acc_before = student.evaluate(eval_tasks)
print(f"{acc_before:.3f}")
# Time passes
print(" Simulating time passage (forgetting)...", end=" ", flush=True)
student.advance_time(50.0)
print("Done")
print(" Evaluating after forgetting...", end=" ", flush=True)
acc_after = student.evaluate(eval_tasks)
print(f"{acc_after:.3f}")
if acc_after < acc_before:
print(f"✅ Forgetting verified (drop: {acc_before - acc_after:.3f})")
else:
print(f"⚠️ Forgetting minimal (change: {acc_after - acc_before:+.3f})")
def test_student_state():
"""Test state reporting works."""
print("Testing state reporting...", flush=True)
student = StudentAgent(device='cpu')
generator = MockTaskGenerator()
# Training with progress bar
topics_to_test = ['history', 'science']
tasks_per_topic = 5
total_tasks = len(topics_to_test) * tasks_per_topic
print(f" Training on {total_tasks} tasks:")
for topic in topics_to_test:
if HAS_TQDM:
pbar = tqdm(range(tasks_per_topic), desc=f" {topic}", leave=False)
for i in pbar:
task = generator.generate_task(topic, 'easy')
student.learn(task)
else:
for i in range(tasks_per_topic):
task = generator.generate_task(topic, 'easy')
student.learn(task)
state = student.get_state()
assert len(state.topic_accuracies) > 0
assert state.total_timesteps >= 10
print("✅ State reporting works")
def run_all_tests():
"""Run all tests with progress indicators."""
print("=" * 60)
print("RUNNING STUDENT AGENT TESTS")
print("=" * 60)
if not HAS_TQDM:
print("💡 Tip: Install tqdm for progress bars: pip install tqdm")
print()
import time
start_time = time.time()
try:
test_student_can_load()
test_student_can_answer()
test_student_learns()
test_student_forgets()
test_student_state()
elapsed = time.time() - start_time
print()
print("=" * 60)
print(f"🎉 All tests passed! (Total time: {elapsed:.2f}s)")
print("=" * 60)
return True
except Exception as e:
elapsed = time.time() - start_time
print()
print("=" * 60)
print(f"❌ Test failed after {elapsed:.2f}s")
print(f"Error: {e}")
print("=" * 60)
import traceback
traceback.print_exc()
return False
if __name__ == "__main__":
success = run_all_tests()
sys.exit(0 if success else 1)
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