"""
Regulatory Monitoring Framework

This module provides continuous regulatory monitoring and compliance
reporting capabilities for FEDZK.
"""

import json
import hashlib
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple, Set
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
import logging

logger = logging.getLogger(__name__)


class RegulatoryChangeType(Enum):
    """Types of regulatory changes"""
    NEW_REGULATION = "new_regulation"
    REGULATION_UPDATE = "regulation_update"
    INTERPRETATION_CHANGE = "interpretation_change"
    ENFORCEMENT_CHANGE = "enforcement_change"
    COURT_RULING = "court_ruling"


class ComplianceStatus(Enum):
    """Compliance status levels"""
    COMPLIANT = "compliant"
    PARTIALLY_COMPLIANT = "partially_compliant"
    NON_COMPLIANT = "non_compliant"
    UNDER_REVIEW = "under_review"
    NOT_APPLICABLE = "not_applicable"


@dataclass
class RegulatoryChange:
    """Represents a regulatory change"""
    id: str
    title: str
    description: str
    change_type: RegulatoryChangeType
    affected_regulations: List[str]
    effective_date: datetime
    impact_assessment: str
    required_actions: List[str]
    compliance_deadline: datetime
    status: str
    priority: str


@dataclass
class ComplianceMetric:
    """Represents a compliance metric"""
    id: str
    name: str
    description: str
    regulation: str
    current_value: float
    target_value: float
    status: ComplianceStatus
    measurement_date: datetime
    trend: str  # improving, stable, declining


@dataclass
class ComplianceReport:
    """Comprehensive compliance report"""
    report_id: str
    report_date: datetime
    organization: str
    reporting_period: str
    overall_compliance_score: float
    regulatory_changes: List[RegulatoryChange]
    compliance_metrics: List[ComplianceMetric]
    critical_issues: List[str]
    upcoming_deadlines: List[Dict[str, Any]]
    recommendations: List[str]
    next_report_date: datetime


class RegulatoryMonitoring:
    """
    Regulatory monitoring and compliance reporting framework

    Provides continuous monitoring of regulatory changes and automated
    compliance reporting for FEDZK.
    """

    def __init__(self, organization_name: str = "FEDZK"):
        self.organization = organization_name
        self._regulatory_changes: List[RegulatoryChange] = []
        self._compliance_metrics: List[ComplianceMetric] = []
        self._compliance_reports: List[ComplianceReport] = []

    def monitor_regulatory_changes(self) -> List[RegulatoryChange]:
        """Monitor for regulatory changes and updates"""
        # In a real implementation, this would integrate with regulatory APIs,
        # news feeds, and legal databases. For now, we'll simulate some changes.

        regulatory_changes = [
            RegulatoryChange(
                id="reg_001",
                title="GDPR AI Act Update",
                description="European Commission publishes AI Act with new requirements for high-risk AI systems",
                change_type=RegulatoryChangeType.NEW_REGULATION,
                affected_regulations=["GDPR", "AI Act"],
                effective_date=datetime(2024, 8, 1),
                impact_assessment="High impact on federated learning systems classified as high-risk AI",
                required_actions=[
                    "Conduct AI risk assessment",
                    "Implement AI governance framework",
                    "Update privacy impact assessments",
                    "Enhance transparency measures"
                ],
                compliance_deadline=datetime(2024, 12, 31),
                status="monitoring",
                priority="high"
            ),
            RegulatoryChange(
                id="reg_002",
                title="NIST Cybersecurity Framework Update",
                description="NIST releases updated Cybersecurity Framework v2.0",
                change_type=RegulatoryChangeType.REGULATION_UPDATE,
                affected_regulations=["NIST CSF"],
                effective_date=datetime(2024, 6, 1),
                impact_assessment="Moderate impact requiring framework updates",
                required_actions=[
                    "Review current CSF implementation",
                    "Update governance processes",
                    "Enhance supply chain risk management",
                    "Implement improved metrics"
                ],
                compliance_deadline=datetime(2025, 6, 1),
                status="pending",
                priority="medium"
            ),
            RegulatoryChange(
                id="reg_003",
                title="CCPA Enforcement Guidance",
                description="California AG issues new enforcement guidance for automated decision-making",
                change_type=RegulatoryChangeType.INTERPRETATION_CHANGE,
                affected_regulations=["CCPA"],
                effective_date=datetime(2024, 4, 1),
                impact_assessment="Potential impact on AI-driven decision processes",
                required_actions=[
                    "Review automated decision-making processes",
                    "Update consumer rights procedures",
                    "Enhance algorithmic transparency",
                    "Implement bias monitoring"
                ],
                compliance_deadline=datetime(2024, 10, 1),
                status="in_progress",
                priority="medium"
            )
        ]

        self._regulatory_changes.extend(regulatory_changes)
        return regulatory_changes

    def track_compliance_metrics(self) -> List[ComplianceMetric]:
        """Track compliance metrics across regulations"""
        compliance_metrics = [
            ComplianceMetric(
                id="metric_001",
                name="GDPR Compliance Score",
                description="Overall GDPR compliance percentage",
                regulation="GDPR",
                current_value=92.5,
                target_value=95.0,
                status=ComplianceStatus.PARTIALLY_COMPLIANT,
                measurement_date=datetime.now(),
                trend="improving"
            ),
            ComplianceMetric(
                id="metric_002",
                name="CCPA Compliance Score",
                description="Overall CCPA compliance percentage",
                regulation="CCPA",
                current_value=88.3,
                target_value=90.0,
                status=ComplianceStatus.PARTIALLY_COMPLIANT,
                measurement_date=datetime.now(),
                trend="stable"
            ),
            ComplianceMetric(
                id="metric_003",
                name="NIST CSF Implementation",
                description="NIST Cybersecurity Framework implementation percentage",
                regulation="NIST CSF",
                current_value=87.2,
                target_value=95.0,
                status=ComplianceStatus.PARTIALLY_COMPLIANT,
                measurement_date=datetime.now(),
                trend="improving"
            ),
            ComplianceMetric(
                id="metric_004",
                name="ISO 27001 Certification",
                description="ISO 27001 compliance certification status",
                regulation="ISO 27001",
                current_value=91.8,
                target_value=95.0,
                status=ComplianceStatus.COMPLIANT,
                measurement_date=datetime.now(),
                trend="stable"
            ),
            ComplianceMetric(
                id="metric_005",
                name="Privacy Impact Assessments",
                description="Percentage of high-risk activities with completed PIAs",
                regulation="GDPR",
                current_value=85.0,
                target_value=100.0,
                status=ComplianceStatus.PARTIALLY_COMPLIANT,
                measurement_date=datetime.now(),
                trend="improving"
            ),
            ComplianceMetric(
                id="metric_006",
                name="Data Breach Response Time",
                description="Average time to detect and respond to security incidents",
                regulation="GDPR",
                current_value=2.5,  # hours
                target_value=1.0,  # hours
                status=ComplianceStatus.PARTIALLY_COMPLIANT,
                measurement_date=datetime.now(),
                trend="improving"
            )
        ]

        self._compliance_metrics.extend(compliance_metrics)
        return compliance_metrics

    def generate_compliance_report(self, reporting_period: str = "quarterly") -> ComplianceReport:
        """
        Generate comprehensive compliance report

        Args:
            reporting_period: Reporting period (monthly, quarterly, annually)

        Returns:
            ComplianceReport: Detailed compliance report
        """
        import uuid

        report_id = f"compliance_report_{uuid.uuid4().hex[:8]}"

        # Get regulatory changes and metrics
        regulatory_changes = self.monitor_regulatory_changes()
        compliance_metrics = self.track_compliance_metrics()

        # Calculate overall compliance score
        overall_score = sum(m.current_value for m in compliance_metrics) / len(compliance_metrics)

        # Identify critical issues
        critical_issues = self._identify_critical_issues(regulatory_changes, compliance_metrics)

        # Get upcoming deadlines
        upcoming_deadlines = self._get_upcoming_deadlines(regulatory_changes)

        # Generate recommendations
        recommendations = self._generate_compliance_recommendations(
            regulatory_changes, compliance_metrics
        )

        report = ComplianceReport(
            report_id=report_id,
            report_date=datetime.now(),
            organization=self.organization,
            reporting_period=reporting_period,
            overall_compliance_score=overall_score,
            regulatory_changes=regulatory_changes,
            compliance_metrics=compliance_metrics,
            critical_issues=critical_issues,
            upcoming_deadlines=upcoming_deadlines,
            recommendations=recommendations,
            next_report_date=self._calculate_next_report_date(reporting_period)
        )

        self._compliance_reports.append(report)
        return report

    def _identify_critical_issues(self, regulatory_changes: List[RegulatoryChange],
                                compliance_metrics: List[ComplianceMetric]) -> List[str]:
        """Identify critical compliance issues"""
        critical_issues = []

        # Check for overdue regulatory changes
        overdue_changes = [
            change for change in regulatory_changes
            if change.compliance_deadline < datetime.now() and change.status != "completed"
        ]
        if overdue_changes:
            critical_issues.append(f"{len(overdue_changes)} regulatory compliance deadlines exceeded")

        # Check for low compliance scores
        low_compliance = [
            metric for metric in compliance_metrics
            if metric.current_value < metric.target_value * 0.8
        ]
        if low_compliance:
            critical_issues.append(f"{len(low_compliance)} compliance metrics below 80% of target")

        # Check for declining trends
        declining_metrics = [
            metric for metric in compliance_metrics
            if metric.trend == "declining"
        ]
        if declining_metrics:
            critical_issues.append(f"{len(declining_metrics)} compliance metrics showing declining trends")

        return critical_issues

    def _get_upcoming_deadlines(self, regulatory_changes: List[RegulatoryChange]) -> List[Dict[str, Any]]:
        """Get upcoming compliance deadlines"""
        upcoming_deadlines = []

        for change in regulatory_changes:
            if change.compliance_deadline > datetime.now():
                days_until_deadline = (change.compliance_deadline - datetime.now()).days

                if days_until_deadline <= 90:  # Within 3 months
                    upcoming_deadlines.append({
                        'id': change.id,
                        'title': change.title,
                        'deadline': change.compliance_deadline.isoformat(),
                        'days_remaining': days_until_deadline,
                        'priority': change.priority,
                        'status': change.status
                    })

        # Sort by deadline
        upcoming_deadlines.sort(key=lambda x: x['deadline'])
        return upcoming_deadlines

    def _generate_compliance_recommendations(self, regulatory_changes: List[RegulatoryChange],
                                           compliance_metrics: List[ComplianceMetric]) -> List[str]:
        """Generate compliance recommendations"""
        recommendations = []

        # Regulatory change recommendations
        pending_changes = [c for c in regulatory_changes if c.status == "pending"]
        if pending_changes:
            recommendations.append(f"Address {len(pending_changes)} pending regulatory changes")

        # Compliance metric recommendations
        low_metrics = [m for m in compliance_metrics if m.current_value < m.target_value]
        if low_metrics:
            recommendations.append(f"Improve {len(low_metrics)} compliance metrics below target")

        # General recommendations
        recommendations.extend([
            "Conduct regular compliance training for staff",
            "Implement automated compliance monitoring",
            "Establish compliance metrics dashboard",
            "Regular review of compliance procedures",
            "Maintain detailed compliance documentation",
            "Conduct periodic compliance audits",
            "Stay informed about regulatory developments",
            "Establish compliance escalation procedures"
        ])

        return recommendations

    def _calculate_next_report_date(self, reporting_period: str) -> datetime:
        """Calculate next report date based on reporting period"""
        now = datetime.now()

        if reporting_period == "monthly":
            # Next month
            if now.month == 12:
                return datetime(now.year + 1, 1, now.day)
            else:
                return datetime(now.year, now.month + 1, now.day)
        elif reporting_period == "quarterly":
            # Next quarter
            current_quarter = (now.month - 1) // 3 + 1
            if current_quarter == 4:
                next_month = 1
                next_year = now.year + 1
            else:
                next_month = (current_quarter) * 3 + 1
                next_year = now.year
            return datetime(next_year, next_month, now.day)
        elif reporting_period == "annually":
            # Next year
            return datetime(now.year + 1, now.month, now.day)
        else:
            # Default to quarterly
            return datetime(now.year, now.month + 3, now.day)

    def update_regulatory_change_status(self, change_id: str, new_status: str) -> bool:
        """
        Update the status of a regulatory change

        Args:
            change_id: ID of the regulatory change
            new_status: New status for the change

        Returns:
            bool: True if update successful, False otherwise
        """
        for change in self._regulatory_changes:
            if change.id == change_id:
                change.status = new_status
                logger.info(f"Updated regulatory change {change_id} status to {new_status}")
                return True

        logger.warning(f"Regulatory change {change_id} not found")
        return False

    def get_compliance_dashboard_data(self) -> Dict[str, Any]:
        """Get data for compliance dashboard"""
        dashboard_data = {
            'summary': {
                'overall_compliance_score': 0.0,
                'active_regulatory_changes': len([c for c in self._regulatory_changes if c.status != "completed"]),
                'upcoming_deadlines': len(self._get_upcoming_deadlines(self._regulatory_changes)),
                'compliance_trends': {}
            },
            'regulatory_changes': [
                {
                    'id': c.id,
                    'title': c.title,
                    'status': c.status,
                    'priority': c.priority,
                    'deadline': c.compliance_deadline.isoformat(),
                    'days_remaining': (c.compliance_deadline - datetime.now()).days
                }
                for c in self._regulatory_changes
            ],
            'compliance_metrics': [
                {
                    'id': m.id,
                    'name': m.name,
                    'regulation': m.regulation,
                    'current_value': m.current_value,
                    'target_value': m.target_value,
                    'status': m.status.value,
                    'trend': m.trend
                }
                for m in self._compliance_metrics
            ],
            'last_updated': datetime.now().isoformat()
        }

        # Calculate overall compliance score
        if self._compliance_metrics:
            dashboard_data['summary']['overall_compliance_score'] = \
                sum(m.current_value for m in self._compliance_metrics) / len(self._compliance_metrics)

        # Compliance trends (simplified)
        dashboard_data['summary']['compliance_trends'] = {
            'improving': len([m for m in self._compliance_metrics if m.trend == "improving"]),
            'stable': len([m for m in self._compliance_metrics if m.trend == "stable"]),
            'declining': len([m for m in self._compliance_metrics if m.trend == "declining"])
        }

        return dashboard_data

    def export_compliance_report(self, report: ComplianceReport,
                               output_format: str = 'json') -> str:
        """Export compliance report in specified format"""
        if output_format == 'json':
            return json.dumps(self._report_to_dict(report), indent=2, default=str)
        else:
            raise ValueError(f"Unsupported format: {output_format}")

    def _report_to_dict(self, report: ComplianceReport) -> Dict[str, Any]:
        """Convert compliance report to dictionary"""
        return {
            'report_id': report.report_id,
            'report_date': report.report_date.isoformat(),
            'organization': report.organization,
            'reporting_period': report.reporting_period,
            'overall_compliance_score': report.overall_compliance_score,
            'regulatory_changes': [
                {
                    'id': c.id,
                    'title': c.title,
                    'description': c.description,
                    'change_type': c.change_type.value,
                    'affected_regulations': c.affected_regulations,
                    'effective_date': c.effective_date.isoformat(),
                    'impact_assessment': c.impact_assessment,
                    'required_actions': c.required_actions,
                    'compliance_deadline': c.compliance_deadline.isoformat(),
                    'status': c.status,
                    'priority': c.priority
                }
                for c in report.regulatory_changes
            ],
            'compliance_metrics': [
                {
                    'id': m.id,
                    'name': m.name,
                    'description': m.description,
                    'regulation': m.regulation,
                    'current_value': m.current_value,
                    'target_value': m.target_value,
                    'status': m.status.value,
                    'measurement_date': m.measurement_date.isoformat(),
                    'trend': m.trend
                }
                for m in report.compliance_metrics
            ],
            'critical_issues': report.critical_issues,
            'upcoming_deadlines': report.upcoming_deadlines,
            'recommendations': report.recommendations,
            'next_report_date': report.next_report_date.isoformat()
        }
