"""
Carbon Tracking Module.

This module provides functionality for tracking carbon emissions
and carbon intensity for sustainable federated learning.
"""

import time
import logging
from typing import Optional, Dict, Any
import random

logger = logging.getLogger(__name__)


class CarbonTracker:
    """
    Carbon emission tracker for federated learning operations.
    
    Tracks carbon emissions during training rounds and provides
    carbon intensity information for green scheduling.
    """
    
    def __init__(self, region: str = "US", mock_data: bool = True):
        """
        Initialize carbon tracker.
        
        Args:
            region: Geographic region for carbon intensity data
            mock_data: Whether to use mock data (for development/testing)
        """
        self.region = region
        self.mock_data = mock_data
        self.tracking_start_time = None
        self.current_session_emission = 0.0
        self.total_emission = 0.0
        
        # Mock carbon intensity data (kg CO2/kWh)
        self.mock_intensities = {
            'US': {'avg': 0.4, 'range': (0.2, 0.8)},
            'EU': {'avg': 0.3, 'range': (0.1, 0.6)},
            'ASIA': {'avg': 0.5, 'range': (0.3, 0.9)}
        }
        
        logger.info(f"Initialized carbon tracker for region {region}")
    
    def get_current_intensity(self) -> float:
        """
        Get current carbon intensity.
        
        Returns:
            Carbon intensity in kg CO2/kWh
        """
        if self.mock_data:
            # Simulate time-varying carbon intensity
            base_intensity = self.mock_intensities.get(self.region, {'avg': 0.4})['avg']
            intensity_range = self.mock_intensities.get(self.region, {'range': (0.2, 0.8)})['range']
            
            # Add some randomness to simulate real-world variation
            variation = random.uniform(-0.1, 0.1)
            intensity = max(intensity_range[0], 
                          min(intensity_range[1], base_intensity + variation))
            
            return intensity
        else:
            # In a real implementation, this would fetch from an API
            # like electricityMap, WattTime, or similar carbon intensity services
            return self._fetch_real_carbon_intensity()
    
    def _fetch_real_carbon_intensity(self) -> float:
        """
        Fetch real carbon intensity data from external API.
        
        This is a placeholder for real API integration.
        
        Returns:
            Current carbon intensity
        """
        # Placeholder for real API call
        # In practice, you would integrate with services like:
        # - electricityMap API
        # - WattTime API
        # - Local utility APIs
        logger.warning("Real carbon intensity API not implemented, using mock data")
        return self.get_current_intensity()
    
    def start_tracking(self) -> None:
        """Start tracking carbon emissions for the current session."""
        self.tracking_start_time = time.time()
        self.current_session_emission = 0.0
        logger.debug("Started carbon emission tracking")
    
    def stop_tracking(self) -> float:
        """
        Stop tracking and calculate emissions for the current session.
        
        Returns:
            Carbon emissions in kg CO2 for the session
        """
        if self.tracking_start_time is None:
            logger.warning("Carbon tracking was not started")
            return 0.0
        
        # Calculate energy consumption and emissions
        duration_hours = (time.time() - self.tracking_start_time) / 3600
        
        # Estimate power consumption (this is a simplified model)
        # In practice, this would use more sophisticated power monitoring
        estimated_power_kw = self._estimate_power_consumption()
        energy_consumption_kwh = estimated_power_kw * duration_hours
        
        # Calculate carbon emissions
        carbon_intensity = self.get_current_intensity()
        session_emission = energy_consumption_kwh * carbon_intensity
        
        self.current_session_emission = session_emission
        self.total_emission += session_emission
        
        logger.info(f"Session carbon emission: {session_emission:.6f} kg CO2 "
                   f"(duration: {duration_hours:.4f}h, intensity: {carbon_intensity:.3f} kg CO2/kWh)")
        
        self.tracking_start_time = None
        return session_emission
    
    def _estimate_power_consumption(self) -> float:
        """
        Estimate power consumption during the tracking session.
        
        This is a simplified model. In practice, you would use:
        - Hardware-specific power monitoring
        - GPU utilization metrics
        - CPU utilization metrics
        - System-level power monitoring tools
        
        Returns:
            Estimated power consumption in kW
        """
        # Mock power consumption based on typical ML workload
        # Assumes: CPU + GPU training
        base_power = 0.1  # Base system power (kW)
        compute_power = 0.3  # Additional power for ML computation (kW)
        
        # Add some randomness to simulate varying workloads
        variation = random.uniform(0.8, 1.2)
        total_power = (base_power + compute_power) * variation
        
        return total_power
    
    def get_total_emissions(self) -> float:
        """
        Get total carbon emissions tracked so far.
        
        Returns:
            Total carbon emissions in kg CO2
        """
        return self.total_emission
    
    def get_carbon_metrics(self) -> Dict[str, float]:
        """
        Get comprehensive carbon tracking metrics.
        
        Returns:
            Dictionary with carbon metrics
        """
        return {
            'total_emission_kg_co2': self.total_emission,
            'current_session_emission_kg_co2': self.current_session_emission,
            'current_carbon_intensity_kg_per_kwh': self.get_current_intensity(),
            'region': self.region
        }
    
    def is_green_time(self, threshold: float = 0.3) -> bool:
        """
        Check if current time is considered "green" for scheduling.
        
        Args:
            threshold: Carbon intensity threshold (kg CO2/kWh)
        
        Returns:
            True if current carbon intensity is below threshold
        """
        current_intensity = self.get_current_intensity()
        is_green = current_intensity < threshold
        
        logger.debug(f"Carbon intensity: {current_intensity:.3f}, threshold: {threshold}, "
                    f"is_green: {is_green}")
        
        return is_green
    
    def get_green_score(self) -> float:
        """
        Get a green score (0-1) based on current carbon intensity.
        
        Lower carbon intensity results in higher green score.
        
        Returns:
            Green score between 0 and 1
        """
        current_intensity = self.get_current_intensity()
        max_intensity = self.mock_intensities.get(self.region, {'range': (0.2, 0.8)})['range'][1]
        
        # Normalize to 0-1 scale (higher score = greener)
        green_score = max(0.0, 1.0 - (current_intensity / max_intensity))
        
        return green_score
    
    def reset(self) -> None:
        """Reset all tracking metrics."""
        self.tracking_start_time = None
        self.current_session_emission = 0.0
        self.total_emission = 0.0
        logger.info("Carbon tracker reset")