"""
Training Simulator - Handles model loading, training, and inference simulation

Integrates with the main system's orchestrator and models for training and testing.
"""

import logging
import uuid
import time
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, asdict
from datetime import datetime
from pathlib import Path
import json

logger = logging.getLogger(__name__)


@dataclass
class TrainingResults:
    """Results from training session"""
    training_id: str
    model_name: str
    test_cases_used: int
    epochs_completed: int
    final_loss: float
    training_duration_seconds: float
    checkpoint_path: str
    metrics: Dict[str, float]
    status: str = "completed"


@dataclass
class InferenceResults:
    """Results from inference simulation"""
    annotation_id: str
    model_name: str
    predictions: List[Dict]
    accuracy: float
    precision: float
    recall: float
    f1_score: float
    confusion_matrix: Dict
    prediction_timeline: List[Dict]


class TrainingSimulator:
    """Simulates training and inference on annotated data"""
    
    def __init__(self, orchestrator=None):
        """Initialize training simulator"""
        self.orchestrator = orchestrator
        self.model_cache = {}
        self.training_sessions = {}
        
        # Storage for training results
        self.results_dir = Path("TESTCASES/data/training_results")
        self.results_dir.mkdir(parents=True, exist_ok=True)
        
        logger.info("TrainingSimulator initialized")

    def load_model(self, model_name: str):
        """Load model from orchestrator"""
        if model_name in self.model_cache:
            return self.model_cache[model_name]
        
        if not self.orchestrator:
            logger.error("Orchestrator not available")
            return None
        
        # Get model from orchestrator
        # This will be implemented when we integrate with actual models
        logger.info(f"Loading model: {model_name}")
        return None
    
    def start_training(self, model_name: str, test_cases: List[Dict]) -> str:
        """Start training session with test cases"""
        training_id = str(uuid.uuid4())
        
        # Create training session
        self.training_sessions[training_id] = {
            'status': 'running',
            'model_name': model_name,
            'test_cases_count': len(test_cases),
            'current_epoch': 0,
            'total_epochs': 50,
            'current_loss': 0.0,
            'start_time': time.time()
        }
        
        logger.info(f"Started training session: {training_id}")
        
        # TODO: Implement actual training in background thread
        # For now, simulate training completion
        self._simulate_training(training_id)
        
        return training_id
    
    def _simulate_training(self, training_id: str):
        """Simulate training progress (placeholder)"""
        import threading
        
        def train():
            session = self.training_sessions[training_id]
            total_epochs = session['total_epochs']
            
            for epoch in range(total_epochs):
                time.sleep(0.1)  # Simulate training time
                session['current_epoch'] = epoch + 1
                session['current_loss'] = 1.0 / (epoch + 1)  # Decreasing loss
            
            # Mark as completed
            session['status'] = 'completed'
            session['final_loss'] = session['current_loss']
            session['duration_seconds'] = time.time() - session['start_time']
            session['accuracy'] = 0.85
            
            logger.info(f"Training completed: {training_id}")
        
        thread = threading.Thread(target=train, daemon=True)
        thread.start()
    
    def get_training_progress(self, training_id: str) -> Dict:
        """Get training progress"""
        if training_id not in self.training_sessions:
            return {
                'status': 'not_found',
                'error': 'Training session not found'
            }
        
        return self.training_sessions[training_id]
    
    def simulate_inference(self, annotation_id: str, model_name: str) -> InferenceResults:
        """Simulate inference on annotated period"""
        # Placeholder implementation
        logger.info(f"Simulating inference for annotation: {annotation_id}")
        
        # Generate dummy predictions
        predictions = []
        for i in range(10):
            predictions.append({
                'timestamp': datetime.now().isoformat(),
                'predicted_action': 'BUY' if i % 2 == 0 else 'SELL',
                'confidence': 0.7 + (i * 0.02),
                'actual_action': 'BUY' if i % 2 == 0 else 'SELL',
                'correct': True
            })
        
        results = InferenceResults(
            annotation_id=annotation_id,
            model_name=model_name,
            predictions=predictions,
            accuracy=0.85,
            precision=0.82,
            recall=0.88,
            f1_score=0.85,
            confusion_matrix={
                'tp_buy': 4,
                'fn_buy': 1,
                'fp_sell': 1,
                'tn_sell': 4
            },
            prediction_timeline=predictions
        )
        
        return results