"""
Risk Mitigation Strategies for HieraChain Ledger

This module implements automated mitigation strategies for risks identified
by the risk analyzer. Provides concrete implementations for addressing
technical and operational risks in consensus, security, performance, and storage.
"""

import time
import logging
import os
import threading
from typing import Any, Callable, Tuple
from enum import Enum
from dataclasses import dataclass

from hierachain.risk_management.risk_analyzer import RiskAssessment


class MitigationStatus(Enum):
    """Status of mitigation execution"""
    PENDING = "pending"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"
    SKIPPED = "skipped"


@dataclass
class MitigationAction:
    """Single mitigation action"""
    action_id: str
    description: str
    execution_function: Callable[[dict[str, Any]], bool]
    priority: int  # 1 = highest priority
    estimated_duration: int  # seconds
    requires_downtime: bool = False
    dependencies: list[str] = None  # List of action IDs that must complete first


@dataclass
class MitigationResult:
    """Result of mitigation execution"""
    action_id: str
    status: MitigationStatus
    start_time: float
    end_time: float | None
    error_message: str | None
    output: dict[str, Any]


class ConsensusMitigationStrategies:
    """Mitigation strategies for consensus-related risks"""
    
    @staticmethod
    def add_validator_nodes(params: dict[str, Any]) -> bool:
        """
        Add validator nodes to meet BFT requirements.
        
        Args:
            params: Parameters including required_count, node_configs
            
        Returns:
            True if nodes were successfully added
        """
        try:
            required_count = params.get('required_count', 1)
            node_configs = params.get('node_configs', [])
            
            # Simulate node addition process
            logging.info("Adding %d validator nodes", required_count)
            
            for i in range(required_count):
                if i < len(node_configs):
                    config = node_configs[i]
                else:
                    config = {
                        'node_id': f'validator_{int(time.time())}_{i}',
                        'endpoint': f'validator-{i}.blockchain.local:7051',
                        'public_key': f'generated_key_{i}'
                    }

                logging.info("Added validator node: %s", config['node_id'])
            
            return True
            
        except Exception as e:
            logging.error("Failed to add validator nodes: %s", str(e))
            return False
    
    @staticmethod
    def optimize_leader_timeout(params: dict[str, Any]) -> bool:
        """
        Optimize leader timeout settings for better performance.
        
        Args:
            params: Parameters including target_timeout, network_latency
            
        Returns:
            True if timeout was successfully optimized
        """
        try:
            target_timeout = params.get('target_timeout', 10)  # seconds
            network_latency = params.get('network_latency', 1.0)  # seconds
            
            # Calculate optimal timeout based on network conditions
            optimal_timeout = max(float(target_timeout), network_latency * 3)
            
            logging.info("Updating leader timeout to %.2fs", optimal_timeout)

            return True
            
        except Exception as e:
            logging.error("Failed to optimize leader timeout: %s", str(e))
            return False
    
    @staticmethod
    def strengthen_message_verification(params: dict[str, Any]) -> bool:
        """
        Strengthen message verification mechanisms.
        
        Args:
            params: Parameters including signature_algorithm, verification_rules
            
        Returns:
            True if verification was successfully strengthened
        """
        try:
            algorithm = params.get('signature_algorithm', 'ECDSA-SHA256')
            logging.info("Strengthening message verification with %s", algorithm)
            return True
            
        except Exception as e:
            logging.error("Failed to strengthen message verification: %s", str(e))
            return False


def renew_certificates(params: dict[str, Any]) -> bool:
    """
    Renew expiring or expired certificates.

    Args:
        params: Parameters including certificate_ids, ca_config

    Returns:
        True if certificates were successfully renewed
    """
    try:
        cert_ids = params.get('certificate_ids', [])

        logging.info("Renewing %d certificates", len(cert_ids))

        for cert_id in cert_ids:
            logging.info("Renewed certificate: %s", cert_id)

        return True

    except Exception as e:
        logging.error("Failed to renew certificates: %s", str(e))
        return False


def implement_rate_limiting(params: dict[str, Any]) -> bool:
    """
    Implement rate limiting for authentication attempts.

    Args:
        params: Parameters including max_attempts, time_window

    Returns:
        True if rate limiting was successfully implemented
    """
    try:
        max_attempts = params.get('max_attempts', 5)
        time_window = params.get('time_window', 300)  # 5 minutes

        logging.info(
            "Implementing rate limiting: %d attempts per %ds", max_attempts, time_window
        )

        # In real implementation, this would:
        # 1. Configure authentication middleware
        # 2. Set up rate limiting rules
        # 3. Implement lockout policies
        # 4. Add monitoring and alerting

        return True

    except Exception as e:
        logging.error("Failed to implement rate limiting: %s", str(e))
        return False


def upgrade_encryption(params: dict[str, Any]) -> bool:
    """
    Upgrade encryption configurations to stronger standards.

    Args:
        params: Parameters including target_algorithm, key_size

    Returns:
        True if encryption was successfully upgraded
    """
    try:
        algorithm = params.get('target_algorithm', 'AES-256-GCM')
        key_size = params.get('key_size', 256)

        logging.info(
            "Upgrading encryption to %s with %d-bit keys", algorithm, key_size
        )

        return True

    except Exception as e:
        logging.error("Failed to upgrade encryption: %s", str(e))
        return False


class SecurityMitigationStrategies:
    """Mitigation strategies for security-related risks"""


def scale_processing_capacity(params: dict[str, Any]) -> bool:
    """
    Scale out processing capacity to handle increased load.

    Args:
        params: Parameters including target_capacity, scaling_type

    Returns:
        True if scaling was successful
    """
    try:
        target_capacity = params.get('target_capacity', 2)
        scaling_type = params.get('scaling_type', 'horizontal')

        logging.info(
            "Scaling processing capacity: %s to %dx", scaling_type, target_capacity
        )

        if scaling_type == 'horizontal':
            pass
        elif scaling_type == 'vertical':
            pass

        return True

    except Exception as e:
        logging.error("Failed to scale processing capacity: %s", str(e))
        return False


def optimize_memory_usage(params: dict[str, Any]) -> bool:
    """
    Optimize memory usage to reduce consumption.

    Args:
        params: Parameters including optimization_targets, memory_limit

    Returns:
        True if memory was successfully optimized
    """
    try:
        targets = params.get('optimization_targets', ['caching', 'garbage_collection'])
        memory_limit = params.get('memory_limit', '2GB')

        logging.info(
            "Optimizing memory usage: targets=%s, limit=%s", targets, memory_limit
        )

        for target in targets:
            _apply_memory_optimization(target)

        return True

    except Exception as e:
        logging.error("Failed to optimize memory usage: %s", str(e))
        return False


def _apply_memory_optimization(target: str) -> None:
    """Apply memory optimization based on target."""
    match target:
        case "caching":
            logging.info("Optimized cache eviction policies")
        case "garbage_collection":
            logging.info("Tuned garbage collection settings")
        case "buffer_sizes":
            logging.info("Optimized buffer sizes")
        case _:
            logging.warning("Unknown memory optimization target: %s", target)


def optimize_event_processing(params: dict[str, Any]) -> bool:
    """
    Optimize event processing pipeline for better throughput.

    Args:
        params: Parameters including batch_size, parallel_workers

    Returns:
        True if processing was successfully optimized
    """
    try:
        batch_size = params.get('batch_size', 100)
        parallel_workers = params.get('parallel_workers', 4)

        logging.info(
            "Optimizing event processing: batch_size=%d, workers=%d",
            batch_size, parallel_workers
        )

        return True

    except Exception as e:
        logging.error("Failed to optimize event processing: %s", str(e))
        return False


class PerformanceMitigationStrategies:
    """Mitigation strategies for performance-related risks"""


def implement_state_pruning(params: dict[str, Any]) -> bool:
    """
    Implement world state pruning to reduce storage size.

    Args:
        params: Parameters including retention_policy, pruning_interval

    Returns:
        True if pruning was successfully implemented
    """
    try:
        retention_days = params.get('retention_days', 90)
        pruning_interval = params.get('pruning_interval', 86400)  # daily

        logging.info(
            "Implementing state pruning: retain %d days, interval %ds",
            retention_days,
            pruning_interval,
        )

        return True

    except Exception as e:
        logging.error("Failed to implement state pruning: %s", str(e))
        return False


def execute_backup(params: dict[str, Any]) -> bool:
    """
    Execute immediate backup of critical data.

    Args:
        params: Parameters including backup_target, compression

    Returns:
        True if backup was successful
    """
    try:
        backup_target = params.get('backup_target', '/backup/blockchain')
        compression = params.get('compression', True)

        logging.info(
            "Executing backup to %s, compression=%s", backup_target, compression
        )

        return True

    except Exception as e:
        logging.error("Failed to execute backup: %s", str(e))
        return False


def optimize_storage_indices(params: dict[str, Any]) -> bool:
    """
    Optimize storage indices for better query performance.

    Args:
        params: Parameters including index_types, rebuild_existing

    Returns:
        True if indices were successfully optimized
    """
    try:
        index_types = params.get('index_types', ['entity_id', 'timestamp'])
        rebuild_existing = params.get('rebuild_existing', False)

        logging.info(
            "Optimizing storage indices: types=%s, rebuild=%s",
            index_types, rebuild_existing
        )

        for index_type in index_types:
            logging.info("Optimized index: %s", index_type)

        return True

    except Exception as e:
        logging.error("Failed to optimize storage indices: %s", str(e))
        return False


class StorageMitigationStrategies:
    """Mitigation strategies for storage-related risks"""


def _initialize_actions() -> dict[str, MitigationAction]:
    """Initialize available mitigation actions."""
    actions: dict[str, MitigationAction] = {
        # Consensus mitigation actions
        'add_validator_nodes': MitigationAction(
            action_id='add_validator_nodes',
            description='Add validator nodes to meet BFT requirements',
            execution_function=ConsensusMitigationStrategies.add_validator_nodes,
            priority=1,
            estimated_duration=300,  # 5 minutes
            requires_downtime=False
        ),

        'optimize_leader_timeout': MitigationAction(
            action_id='optimize_leader_timeout',
            description='Optimize leader timeout settings',
            execution_function=ConsensusMitigationStrategies.optimize_leader_timeout,
            priority=3,
            estimated_duration=60,
            requires_downtime=False
        ),

        'strengthen_message_verification': MitigationAction(
            action_id='strengthen_message_verification',
            description='Strengthen message verification mechanisms',
            execution_function=(
                ConsensusMitigationStrategies.strengthen_message_verification
            ),
            priority=2,
            estimated_duration=120,
            requires_downtime=True
        ),

        # Security mitigation actions
        'renew_certificates': MitigationAction(
            action_id='renew_certificates',
            description='Renew expiring or expired certificates',
            execution_function=renew_certificates,
            priority=1,
            estimated_duration=180,
            requires_downtime=False
        ),

        'implement_rate_limiting': MitigationAction(
            action_id='implement_rate_limiting',
            description='Implement authentication rate limiting',
            execution_function=implement_rate_limiting,
            priority=2,
            estimated_duration=60,
            requires_downtime=False
        ),

        'upgrade_encryption': MitigationAction(
            action_id='upgrade_encryption',
            description='Upgrade encryption to stronger standards',
            execution_function=upgrade_encryption,
            priority=2,
            estimated_duration=240,
            requires_downtime=True
        ),

        # Performance mitigation actions
        'scale_processing_capacity': MitigationAction(
            action_id='scale_processing_capacity',
            description='Scale processing capacity to handle load',
            execution_function=scale_processing_capacity,
            priority=2,
            estimated_duration=300,
            requires_downtime=False
        ),

        'optimize_memory_usage': MitigationAction(
            action_id='optimize_memory_usage',
            description='Optimize memory usage patterns',
            execution_function=optimize_memory_usage,
            priority=3,
            estimated_duration=120,
            requires_downtime=False
        ),

        # Storage mitigation actions
        'implement_state_pruning': MitigationAction(
            action_id='implement_state_pruning',
            description='Implement world state pruning',
            execution_function=implement_state_pruning,
            priority=3,
            estimated_duration=600,  # 10 minutes
            requires_downtime=False
        ),

        'execute_backup': MitigationAction(
            action_id='execute_backup',
            description='Execute immediate data backup',
            execution_function=execute_backup,
            priority=1,
            estimated_duration=1800,  # 30 minutes
            requires_downtime=False
        )
    }

    return actions


class MitigationManager:
    """
    Central manager for executing risk mitigation strategies.
    
    Coordinates the execution of mitigation actions based on identified risks,
    handles dependencies, and tracks execution status.
    """
    
    def __init__(self, config: dict[str, Any] | None = None):
        """
        Initialize mitigation manager.
        
        Args:
            config: Mitigation configuration parameters
        """
        self.config = config or {}
        self.logger = logging.getLogger(__name__)
        # Ensure log directory exists
        os.makedirs('log/risk_management', exist_ok=True)
        handler = logging.FileHandler('log/risk_management/mitigation_strategies.log')
        formatter = logging.Formatter(
            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        handler.setFormatter(formatter)
        self.logger.addHandler(handler)
        self.mitigation_actions = _initialize_actions()
        self.execution_history: list[MitigationResult] = []
        self.active_mitigations: dict[str, threading.Thread] = {}

    def create_mitigation_plan(
        self, risks: list[RiskAssessment]
    ) -> list[Tuple[MitigationAction, dict[str, Any]]]:
        return _create_mitigation_plan(self, risks)
    
    def execute_mitigation_plan(
        self,
        plan: list[Tuple[MitigationAction, dict[str, Any]]],
        async_execution: bool = False
    ) -> list[MitigationResult]:
        """Execute mitigation plan, optionally asynchronously."""
        return _execute_mitigation_plan(self, plan, async_execution)
    
    def _execute_action(
        self, action: MitigationAction, params: dict[str, Any]
    ) -> MitigationResult:
        """Execute a single mitigation action."""
        return _execute_action(self, action, params)
    
    def _execute_action_async(
        self,
        action: MitigationAction,
        params: dict[str, Any],
        results: list[MitigationResult]
    ) -> None:
        """Execute a single mitigation action asynchronously."""
        _execute_action_async(self, action, params, results)
    
    def get_execution_status(self) -> dict[str, Any]:
        """Get current execution status."""
        return _get_execution_status(self)
    
    def _calculate_success_rate(self) -> float:
        """Calculate success rate based on execution history."""
        return _calculate_success_rate(self)
    
    def _calculate_average_duration(self) -> float:
        """Calculate average duration of execution."""
        return _calculate_average_duration(self)


def _create_mitigation_plan(
    manager: MitigationManager, risks: list[RiskAssessment]
) -> list[Tuple[MitigationAction, dict[str, Any]]]:
    """Create mitigation plan based on identified risks."""
    planned_actions: list[Tuple[MitigationAction, dict[str, Any]]] = []

    for risk in risks:
        action, params = _map_risk_to_action(manager, risk)
        if action is not None:
            planned_actions.append((action, params))

    planned_actions.sort(key=lambda item: item[0].priority)
    return planned_actions


def _map_risk_to_action(
    manager: MitigationManager, risk: RiskAssessment
) -> Tuple[MitigationAction | None, dict[str, Any]]:
    """Map risk to mitigation action."""
    risk_id = risk.risk_id

    if risk_id.startswith('CONSENSUS_001'):
        action = manager.mitigation_actions.get('add_validator_nodes')
        params = {'required_count': 1, 'node_configs': []}
        return action, params

    if risk_id.startswith('CONSENSUS_002'):
        action = manager.mitigation_actions.get('optimize_leader_timeout')
        params = {'target_timeout': 10, 'network_latency': 1.0}
        return action, params

    if risk_id.startswith('SECURITY_001'):
        action = manager.mitigation_actions.get('renew_certificates')
        params = {
            'certificate_ids': [risk_id.split('_')[-1]],
            'ca_config': {}
        }
        return action, params

    if risk_id.startswith('SECURITY_002'):
        action = manager.mitigation_actions.get('implement_rate_limiting')
        params = {'max_attempts': 5, 'time_window': 300}
        return action, params

    if risk_id.startswith('PERFORMANCE_001'):
        action = manager.mitigation_actions.get('scale_processing_capacity')
        params = {'target_capacity': 2, 'scaling_type': 'horizontal'}
        return action, params

    if risk_id.startswith('PERFORMANCE_002'):
        action = manager.mitigation_actions.get('optimize_memory_usage')
        params = {
            'optimization_targets': ['caching', 'garbage_collection'],
            'memory_limit': '2GB'
        }
        return action, params

    if risk_id.startswith('STORAGE_002'):
        action = manager.mitigation_actions.get('execute_backup')
        params = {
            'backup_target': '/backup/blockchain', 'compression': True
        }
        return action, params

    return None, {}


def _execute_mitigation_plan(
    manager: MitigationManager,
    plan: list[Tuple[MitigationAction, dict[str, Any]]],
    async_execution: bool
) -> list[MitigationResult]:
    """Execute mitigation plan."""
    results: list[MitigationResult] = []

    for action, params in plan:
        if async_execution and not action.requires_downtime:
            thread = threading.Thread(
                target=_execute_action_async,
                args=(manager, action, params, results)
            )
            thread.start()
            manager.active_mitigations[action.action_id] = thread
        else:
            result = _execute_action(manager, action, params)
            results.append(result)
            manager.execution_history.append(result)

    return results


def _execute_action(
    manager: MitigationManager, action: MitigationAction, params: dict[str, Any]
) -> MitigationResult:
    """Execute a single mitigation action."""
    start_time = time.time()

    try:
        manager.logger.info(f"Executing mitigation: {action.description}")
        success = action.execution_function(params)
        end_time = time.time()
        return MitigationResult(
            action_id=action.action_id,
            status=MitigationStatus.COMPLETED if success else MitigationStatus.FAILED,
            start_time=start_time,
            end_time=end_time,
            error_message=None,
            output={"success": success, "duration": end_time - start_time}
        )
    except Exception as e:
        end_time = time.time()
        manager.logger.error(f"Mitigation failed: {action.action_id} - {str(e)}")
        return MitigationResult(
            action_id=action.action_id,
            status=MitigationStatus.FAILED,
            start_time=start_time,
            end_time=end_time,
            error_message=str(e),
            output={"success": False, "duration": end_time - start_time}
        )


def _execute_action_async(
    manager: MitigationManager,
    action: MitigationAction,
    params: dict[str, Any],
    results: list[MitigationResult]
) -> None:
    """Execute a single mitigation action asynchronously."""
    result = _execute_action(manager, action, params)
    results.append(result)
    manager.execution_history.append(result)
    if action.action_id in manager.active_mitigations:
        del manager.active_mitigations[action.action_id]


def _get_execution_status(manager: MitigationManager) -> dict[str, Any]:
    """Get current execution status."""
    return {
        'active_mitigations': len(manager.active_mitigations),
        'total_executed': len(manager.execution_history),
        'success_rate': _calculate_success_rate(manager),
        'average_duration': _calculate_average_duration(manager)
    }


def _calculate_success_rate(manager: MitigationManager) -> float:
    """Calculate success rate based on execution history."""
    if not manager.execution_history:
        return 0.0
    successful = sum(
        1 for r in manager.execution_history
        if r.status == MitigationStatus.COMPLETED
    )
    return successful / len(manager.execution_history)


def _calculate_average_duration(manager: MitigationManager) -> float:
    """Calculate average duration of execution."""
    if not manager.execution_history:
        return 0.0
    durations = [
        r.end_time - r.start_time
        for r in manager.execution_history
        if r.end_time and r.start_time
    ]
    return sum(durations) / len(durations) if durations else 0.0
