"""
Unit Tests for Adversarial Insight ML (AIML)

This module contains unit tests for the AIML package that uses the Adversarial Robustness Toolbox (ART).
The tests cover various attack scenarios to ensure the program works as expected.

Use pytest to run this test on the main code.
"""

import torch
import pytest
from my_project import MyModel, load_data
from art.estimators.classification import PyTorchClassifier
from art.attacks.evasion import FastGradientMethod

@pytest.fixture
def model():
    return MyModel()  # Instantiate your model

@pytest.fixture
def classifier(model):
    return PyTorchClassifier(model=model, loss="cross_entropy", optimizer=None)

def test_model_prediction(model):
    """Test if the model makes expected prediction"""
    input_data = torch.rand(10, 1, 28, 28)  # Image data with appropriate input shape (batch_size, channels, height, width)
    predictions = model(input_data)
    assert predictions.shape == (10, 10)  # Check if predictions have the expected shape

def test_fast_gradient_method(classifier):
    attack = FastGradientMethod(estimator=classifier, eps=0.1)
    input_data = torch.rand(10, 1, 28, 28)  # Image data with appropriate input shape (batch_size, channels, height, width)
    adv_examples = attack.generate(input_data)
    assert torch.all(adv_examples >= 0) and torch.all(adv_examples <= 1)  # Check if adversarial examples are within valid range

def test_data_loading():
    x_train, y_train, x_test, y_test = load_data()  # Implement your data loading function
    assert len(x_train) == len(y_train)  # Check if labels match data length
