"""
Insights and analytics commands
"""

from typing import TYPE_CHECKING, Any, Dict, List
from argparse import Namespace

from .base import BaseCommand
from ..utils.errors import CommandError
from ...core.nexus.engine import NexusEngine
from ...core.nexus.resource_tracker import ResourceTracker
from ...core.nexus.intelligence import IntelligenceEngine

if TYPE_CHECKING:
    from ..utils.output import Console
    from ..utils.config import CLIConfig


class InsightsCommand(BaseCommand):
    """Show cost and security insights"""

    @property
    def name(self) -> str:
        return "insights"

    @property
    def description(self) -> str:
        return "Show cost and security insights"

    def register(self, subparsers) -> None:
        """Register command arguments"""
        parser = subparsers.add_parser(
            self.name,
            help=self.description,
            description="Show cost optimization and security insights",
        )

        parser.add_argument(
            "--type",
            choices=["cost", "security", "performance", "all"],
            default="all",
            help="Type of insights to show (default: all)",
        )

        parser.add_argument("--project", help="Filter by project")

        parser.add_argument("--environment", help="Filter by environment")

        parser.add_argument(
            "--timeframe",
            choices=["1d", "7d", "30d", "90d"],
            default="30d",
            help="Time frame for analysis (default: 30d)",
        )

        parser.add_argument("--resource", help="Show insights for specific resource")

        parser.add_argument("--export", help="Export insights to file")

        self.add_common_arguments(parser)

    def execute(self, args: Namespace, config: "CLIConfig", console: "Console") -> int:
        """Execute the insights command"""
        try:
            console.info("Analyzing infrastructure insights...")

            # Initialize components
            engine = NexusEngine()
            tracker = ResourceTracker()
            intelligence = IntelligenceEngine(tracker)

            # Get resources to analyze
            resources = self._get_resources_to_analyze(args, tracker, console)

            if not resources:
                console.info("No resources found to analyze")
                return 0

            # Generate insights
            with console.status("Generating insights..."):
                insights = self._generate_insights(
                    resources, args.type, args.timeframe, intelligence, console
                )

            # Display insights
            self._display_insights(insights, console)

            # Export if requested
            if args.export:
                self._export_insights(insights, args.export, console)

            return 0

        except Exception as e:
            raise CommandError(f"Failed to generate insights: {e}")

    def _get_resources_to_analyze(
        self, args: Namespace, tracker: ResourceTracker, console: "Console"
    ) -> List[Dict[str, Any]]:
        """Get resources to analyze based on filters"""
        if args.resource:
            # Analyze specific resource
            resource = tracker.get_resource(args.resource)
            return [resource] if resource else []

        # Get all resources
        resources = tracker.list_resources()

        # Apply filters
        if args.project:
            resources = [r for r in resources if r.get("project") == args.project]

        if args.environment:
            resources = [
                r for r in resources if r.get("environment") == args.environment
            ]

        return resources

    def _generate_insights(
        self,
        resources: List[Dict[str, Any]],
        insight_type: str,
        timeframe: str,
        intelligence: IntelligenceEngine,
        console: "Console",
    ) -> Dict[str, Any]:
        """Generate insights for resources"""
        insights = {
            "summary": {
                "total_resources": len(resources),
                "timeframe": timeframe,
                "generated_at": "2024-01-01T00:00:00Z",
            },
            "cost": {},
            "security": {},
            "performance": {},
        }

        # Generate cost insights
        if insight_type in ["cost", "all"]:
            insights["cost"] = self._generate_cost_insights(
                resources, timeframe, intelligence
            )

        # Generate security insights
        if insight_type in ["security", "all"]:
            insights["security"] = self._generate_security_insights(
                resources, intelligence
            )

        # Generate performance insights
        if insight_type in ["performance", "all"]:
            insights["performance"] = self._generate_performance_insights(
                resources, intelligence
            )

        return insights

    def _generate_cost_insights(
        self,
        resources: List[Dict[str, Any]],
        timeframe: str,
        intelligence: IntelligenceEngine,
    ) -> Dict[str, Any]:
        """Generate cost optimization insights"""
        cost_insights = {
            "total_cost": 0.0,
            "estimated_monthly_cost": 0.0,
            "cost_breakdown": {},
            "savings_opportunities": [],
            "trends": {"cost_change": 0.0, "utilization_trend": "stable"},
        }

        total_cost = 0.0
        cost_breakdown = {}
        savings_opportunities = []

        for resource in resources:
            resource_type = resource.get("type", "unknown")

            # Mock cost calculation
            resource_cost = self._calculate_resource_cost(resource, timeframe)
            total_cost += resource_cost

            # Update cost breakdown
            if resource_type in cost_breakdown:
                cost_breakdown[resource_type] += resource_cost
            else:
                cost_breakdown[resource_type] = resource_cost

            # Check for savings opportunities
            opportunities = self._find_cost_savings(resource)
            savings_opportunities.extend(opportunities)

        cost_insights["total_cost"] = total_cost
        cost_insights["estimated_monthly_cost"] = (
            total_cost * 30
        )  # Rough monthly estimate
        cost_insights["cost_breakdown"] = cost_breakdown
        cost_insights["savings_opportunities"] = savings_opportunities

        return cost_insights

    def _generate_security_insights(
        self, resources: List[Dict[str, Any]], intelligence: IntelligenceEngine
    ) -> Dict[str, Any]:
        """Generate security insights"""
        security_insights = {
            "security_score": 0.0,
            "vulnerabilities": [],
            "compliance_status": {},
            "recommendations": [],
        }

        vulnerabilities = []
        recommendations = []

        for resource in resources:
            # Mock security analysis
            resource_vulnerabilities = self._find_security_vulnerabilities(resource)
            vulnerabilities.extend(resource_vulnerabilities)

            resource_recommendations = self._generate_security_recommendations(resource)
            recommendations.extend(resource_recommendations)

        # Calculate security score
        security_score = max(0, 100 - len(vulnerabilities) * 10)

        security_insights["security_score"] = security_score
        security_insights["vulnerabilities"] = vulnerabilities
        security_insights["recommendations"] = recommendations
        security_insights["compliance_status"] = {
            "SOC2": "compliant" if security_score >= 80 else "non-compliant",
            "ISO27001": "compliant" if security_score >= 90 else "non-compliant",
            "PCI-DSS": "compliant" if security_score >= 95 else "non-compliant",
        }

        return security_insights

    def _generate_performance_insights(
        self, resources: List[Dict[str, Any]], intelligence: IntelligenceEngine
    ) -> Dict[str, Any]:
        """Generate performance insights"""
        performance_insights = {
            "performance_score": 0.0,
            "bottlenecks": [],
            "optimization_opportunities": [],
            "resource_utilization": {},
        }

        bottlenecks = []
        optimization_opportunities = []
        utilization_data = {}

        for resource in resources:
            # Mock performance analysis
            resource_bottlenecks = self._find_performance_bottlenecks(resource)
            bottlenecks.extend(resource_bottlenecks)

            resource_optimizations = self._find_performance_optimizations(resource)
            optimization_opportunities.extend(resource_optimizations)

            # Mock utilization data
            utilization_data[resource["name"]] = {
                "cpu": 65.5,
                "memory": 78.2,
                "disk": 45.8,
                "network": 23.1,
            }

        # Calculate performance score
        performance_score = max(0, 100 - len(bottlenecks) * 15)

        performance_insights["performance_score"] = performance_score
        performance_insights["bottlenecks"] = bottlenecks
        performance_insights["optimization_opportunities"] = optimization_opportunities
        performance_insights["resource_utilization"] = utilization_data

        return performance_insights

    def _calculate_resource_cost(
        self, resource: Dict[str, Any], timeframe: str
    ) -> float:
        """Calculate cost for a resource"""
        # Mock cost calculation based on resource type
        resource_type = resource.get("type", "unknown")

        base_costs = {
            "ec2": 0.10,
            "rds": 0.20,
            "s3": 0.023,
            "lambda": 0.0000002,
            "vm": 0.12,
            "database": 0.25,
            "storage": 0.02,
        }

        daily_cost = base_costs.get(resource_type, 0.05)

        # Adjust for timeframe
        if timeframe == "1d":
            return daily_cost
        elif timeframe == "7d":
            return daily_cost * 7
        elif timeframe == "30d":
            return daily_cost * 30
        elif timeframe == "90d":
            return daily_cost * 90

        return daily_cost

    def _find_cost_savings(self, resource: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Find cost savings opportunities for a resource"""
        opportunities = []

        # Mock savings opportunities
        resource_type = resource.get("type", "unknown")

        if resource_type in ["ec2", "vm"]:
            opportunities.append(
                {
                    "resource": resource["name"],
                    "type": "rightsizing",
                    "description": "Instance appears to be over-provisioned",
                    "potential_savings": 25.50,
                    "effort": "low",
                }
            )

        if resource_type in ["rds", "database"]:
            opportunities.append(
                {
                    "resource": resource["name"],
                    "type": "reserved_instances",
                    "description": "Switch to reserved instances for 40% savings",
                    "potential_savings": 120.00,
                    "effort": "medium",
                }
            )

        return opportunities

    def _find_security_vulnerabilities(
        self, resource: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Find security vulnerabilities for a resource"""
        vulnerabilities = []

        # Mock security vulnerabilities
        resource_type = resource.get("type", "unknown")

        if resource_type in ["ec2", "vm"]:
            vulnerabilities.append(
                {
                    "resource": resource["name"],
                    "severity": "medium",
                    "type": "open_ports",
                    "description": "SSH port 22 is open to 0.0.0.0/0",
                    "remediation": "Restrict SSH access to specific IP ranges",
                }
            )

        return vulnerabilities

    def _generate_security_recommendations(
        self, resource: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Generate security recommendations for a resource"""
        recommendations = []

        # Mock security recommendations
        resource_type = resource.get("type", "unknown")

        if resource_type in ["ec2", "vm"]:
            recommendations.append(
                {
                    "resource": resource["name"],
                    "category": "encryption",
                    "priority": "high",
                    "description": "Enable encryption at rest for EBS volumes",
                    "impact": "Protects data confidentiality",
                }
            )

        return recommendations

    def _find_performance_bottlenecks(
        self, resource: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Find performance bottlenecks for a resource"""
        bottlenecks = []

        # Mock performance bottlenecks
        resource_type = resource.get("type", "unknown")

        if resource_type in ["ec2", "vm"]:
            bottlenecks.append(
                {
                    "resource": resource["name"],
                    "type": "cpu_utilization",
                    "severity": "medium",
                    "description": "CPU utilization consistently above 80%",
                    "impact": "May cause performance degradation",
                }
            )

        return bottlenecks

    def _find_performance_optimizations(
        self, resource: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Find performance optimization opportunities for a resource"""
        optimizations = []

        # Mock performance optimizations
        resource_type = resource.get("type", "unknown")

        if resource_type in ["ec2", "vm"]:
            optimizations.append(
                {
                    "resource": resource["name"],
                    "type": "instance_type",
                    "description": "Consider upgrading to newer generation instance type",
                    "expected_improvement": "20% better performance",
                    "cost_impact": "+$15/month",
                }
            )

        return optimizations

    def _display_insights(self, insights: Dict[str, Any], console: "Console") -> None:
        """Display insights to console"""
        console.info("\\n=== Infrastructure Insights ===")

        # Display summary
        summary = insights["summary"]
        console.info(f"\\nSummary:")
        console.info(f"  Resources analyzed: {summary['total_resources']}")
        console.info(f"  Time frame: {summary['timeframe']}")

        # Display cost insights
        if insights.get("cost"):
            self._display_cost_insights(insights["cost"], console)

        # Display security insights
        if insights.get("security"):
            self._display_security_insights(insights["security"], console)

        # Display performance insights
        if insights.get("performance"):
            self._display_performance_insights(insights["performance"], console)

    def _display_cost_insights(
        self, cost_insights: Dict[str, Any], console: "Console"
    ) -> None:
        """Display cost insights"""
        console.info("\\n=== Cost Insights ===")

        total_cost = cost_insights["total_cost"]
        monthly_cost = cost_insights["estimated_monthly_cost"]

        console.info(f"Current cost: ${total_cost:.2f}")
        console.info(f"Estimated monthly cost: ${monthly_cost:.2f}")

        # Cost breakdown
        if cost_insights["cost_breakdown"]:
            console.info("\\nCost breakdown:")
            for resource_type, cost in cost_insights["cost_breakdown"].items():
                console.info(f"  {resource_type}: ${cost:.2f}")

        # Savings opportunities
        if cost_insights["savings_opportunities"]:
            console.info("\\nSavings opportunities:")
            total_savings = sum(
                opp["potential_savings"]
                for opp in cost_insights["savings_opportunities"]
            )
            console.info(f"  Total potential savings: ${total_savings:.2f}")

            for opportunity in cost_insights["savings_opportunities"]:
                console.info(
                    f"  • {opportunity['resource']}: {opportunity['description']} (${opportunity['potential_savings']:.2f})"
                )

    def _display_security_insights(
        self, security_insights: Dict[str, Any], console: "Console"
    ) -> None:
        """Display security insights"""
        console.info("\\n=== Security Insights ===")

        score = security_insights["security_score"]
        console.info(f"Security score: {score:.1f}/100")

        # Compliance status
        if security_insights["compliance_status"]:
            console.info("\\nCompliance status:")
            for standard, status in security_insights["compliance_status"].items():
                icon = "✅" if status == "compliant" else "❌"
                console.info(f"  {icon} {standard}: {status}")

        # Vulnerabilities
        if security_insights["vulnerabilities"]:
            console.info(
                f"\\nVulnerabilities ({len(security_insights['vulnerabilities'])}):"
            )
            for vuln in security_insights["vulnerabilities"]:
                console.warning(
                    f"  • {vuln['resource']}: {vuln['description']} ({vuln['severity']})"
                )

        # Recommendations
        if security_insights["recommendations"]:
            console.info(
                f"\\nRecommendations ({len(security_insights['recommendations'])}):"
            )
            for rec in security_insights["recommendations"]:
                console.info(
                    f"  • {rec['resource']}: {rec['description']} ({rec['priority']} priority)"
                )

    def _display_performance_insights(
        self, performance_insights: Dict[str, Any], console: "Console"
    ) -> None:
        """Display performance insights"""
        console.info("\\n=== Performance Insights ===")

        score = performance_insights["performance_score"]
        console.info(f"Performance score: {score:.1f}/100")

        # Bottlenecks
        if performance_insights["bottlenecks"]:
            console.info(
                f"\\nBottlenecks ({len(performance_insights['bottlenecks'])}):"
            )
            for bottleneck in performance_insights["bottlenecks"]:
                console.warning(
                    f"  • {bottleneck['resource']}: {bottleneck['description']} ({bottleneck['severity']})"
                )

        # Optimization opportunities
        if performance_insights["optimization_opportunities"]:
            console.info(
                f"\\nOptimization opportunities ({len(performance_insights['optimization_opportunities'])}):"
            )
            for opt in performance_insights["optimization_opportunities"]:
                console.info(
                    f"  • {opt['resource']}: {opt['description']} ({opt['expected_improvement']})"
                )

        # Resource utilization
        if performance_insights["resource_utilization"]:
            console.info("\\nResource utilization:")
            for resource, util in performance_insights["resource_utilization"].items():
                console.info(f"  {resource}:")
                console.info(f"    CPU: {util['cpu']:.1f}%")
                console.info(f"    Memory: {util['memory']:.1f}%")
                console.info(f"    Disk: {util['disk']:.1f}%")
                console.info(f"    Network: {util['network']:.1f}%")

    def _export_insights(
        self, insights: Dict[str, Any], export_path: str, console: "Console"
    ) -> None:
        """Export insights to file"""
        import json
        from pathlib import Path

        try:
            export_file = Path(export_path)

            with open(export_file, "w") as f:
                json.dump(insights, f, indent=2)

            console.success(f"Insights exported to: {export_file}")

        except Exception as e:
            console.error(f"Failed to export insights: {e}")
            raise
