"""
Test Enhanced CNN Adapter

This script tests the EnhancedCNNAdapter with standardized input format.
"""

import logging
import time
from datetime import datetime

from core.standardized_data_provider import StandardizedDataProvider
from core.enhanced_cnn_adapter import EnhancedCNNAdapter
from core.data_models import create_model_output

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def test_cnn_adapter():
    """Test the EnhancedCNNAdapter with standardized input format"""
    try:
        # Initialize data provider
        symbols = ['ETH/USDT', 'BTC/USDT']
        timeframes = ['1s', '1m', '1h', '1d']
        data_provider = StandardizedDataProvider(symbols=symbols, timeframes=timeframes)
        
        # Initialize CNN adapter
        cnn_adapter = EnhancedCNNAdapter(checkpoint_dir="models/enhanced_cnn")
        
        # Load best checkpoint if available
        cnn_adapter.load_best_checkpoint()
        
        # Get standardized input data
        logger.info("Getting standardized input data...")
        base_data = data_provider.get_base_data_input('ETH/USDT')
        
        if base_data is None:
            logger.error("Failed to get base data input")
            return
        
        # Make prediction
        logger.info("Making prediction...")
        model_output = cnn_adapter.predict(base_data)
        
        # Log prediction
        logger.info(f"Prediction: {model_output.predictions['action']} with confidence {model_output.confidence:.4f}")
        
        # Store model output
        data_provider.store_model_output(model_output)
        
        # Add training sample (simulated)
        logger.info("Adding training sample...")
        cnn_adapter.add_training_sample(base_data, 'BUY', 0.05)
        
        # Train model
        logger.info("Training model...")
        metrics = cnn_adapter.train(epochs=1)
        
        # Log training metrics
        logger.info(f"Training metrics: {metrics}")
        
        # Make another prediction
        logger.info("Making another prediction...")
        model_output = cnn_adapter.predict(base_data)
        
        # Log prediction
        logger.info(f"Prediction: {model_output.predictions['action']} with confidence {model_output.confidence:.4f}")
        
        # Test model output manager
        logger.info("Testing model output manager...")
        output_manager = data_provider.get_model_output_manager()
        
        # Get current outputs
        current_outputs = output_manager.get_all_current_outputs('ETH/USDT')
        logger.info(f"Current outputs: {len(current_outputs)} models")
        
        # Evaluate model performance
        metrics = output_manager.evaluate_model_performance('ETH/USDT', 'enhanced_cnn_v1')
        logger.info(f"Performance metrics: {metrics}")
        
        logger.info("Test completed successfully")
        
    except Exception as e:
        logger.error(f"Error in test: {e}", exc_info=True)

if __name__ == "__main__":
    test_cnn_adapter()