import os
# Set Huggingface cache directory to avoid permission issues
os.environ['TRANSFORMERS_CACHE'] = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'hf_cache')
os.makedirs(os.environ['TRANSFORMERS_CACHE'], exist_ok=True)
os.makedirs('models', exist_ok=True)

import numpy as np
import torch
from vae_model import DemoVAE
import matplotlib.pyplot as plt
from visualization import plot_learning_curves

print("Creating synthetic test data...")
# Create small synthetic dataset with only 5 samples
input_dim = 100
n_samples = 5
X = np.random.randn(n_samples, input_dim)
demo_data = [
    np.random.normal(60, 10, n_samples),  # age
    np.random.choice(['M', 'F'], n_samples),  # sex
    np.random.normal(24, 12, n_samples),  # months post stroke
    np.random.normal(50, 15, n_samples)   # WAB score
]
demo_types = ['continuous', 'categorical', 'continuous', 'continuous']

print("Testing DemoVAE initialization...")
# Initialize with nepochs=3 for fast testing
vae = DemoVAE(latent_dim=16, nepochs=3, bsize=5)

print("Testing DemoVAE fit method...")
# Fit model
train_losses, val_losses = vae.fit(X, demo_data, demo_types)

print(f"Train losses shape: {len(train_losses)}")
print(f"Val losses shape: {len(val_losses)}")

print("Testing get_latents method...")
# Test get_latents
latents = vae.get_latents(X)
print(f"Latents shape: {latents.shape}")

print("Testing encode method...")
# Test encode
latents2 = vae.encode(X)
print(f"Latents from encode shape: {latents2.shape}")

print("Testing model save...")
# Save model
vae.save('models/test_vae.pt')

print("Testing model load...")
# Load model
vae2 = DemoVAE()
vae2.load('models/test_vae.pt')

print("Testing learning curve plotting...")
# Test learning curve plotting
fig = plot_learning_curves(vae2.train_losses, vae2.val_losses)
plt.savefig('test_learning.png')
print("Learning curve saved to test_learning.png")

print("All tests passed!")