"""
Empirical Performance Analyzer (Stub)

Placeholder module for performance analysis and benchmarking.
The full implementation is planned for a future release.

This stub prevents import errors and returns sensible defaults.
"""

import time
from dataclasses import dataclass
from typing import Any

try:
    import psutil
    HAS_PSUTIL = True
except ImportError:
    HAS_PSUTIL = False


@dataclass
class BenchmarkResult:
    """Result of a benchmark run."""
    overall_score: float
    metrics: dict[str, float]
    component_performance: dict[str, float]
    memory_usage: dict[str, float]
    recommendations: list
    detailed_breakdown: dict[str, Any]


class EmpiricalPerformanceAnalyzer:
    """
    Performance analyzer for Empirica components.

    Currently returns baseline metrics. Full implementation planned.
    """

    def __init__(self):
        """Initialize the performance analyzer."""
        self._start_time = time.time()

    def run_benchmark(
        self,
        benchmark_type: str = "comprehensive",
        iterations: int = 10,
        include_memory: bool = True,
        verbose: bool = False
    ) -> dict[str, Any]:
        """
        Run performance benchmark.

        Args:
            benchmark_type: Type of benchmark (comprehensive, quick, stress)
            iterations: Number of iterations
            include_memory: Include memory profiling
            verbose: Enable verbose output

        Returns:
            Benchmark results dictionary
        """
        # Basic memory stats
        memory = {}
        if include_memory and HAS_PSUTIL:
            try:
                process = psutil.Process()
                mem_info = process.memory_info()
                memory = {
                    "peak_mb": mem_info.rss / (1024 * 1024),
                    "average_mb": mem_info.rss / (1024 * 1024),
                    "current_mb": mem_info.rss / (1024 * 1024),
                }
            except Exception:
                memory = {"peak_mb": 0, "average_mb": 0, "current_mb": 0}
        else:
            memory = {"peak_mb": 0, "average_mb": 0, "current_mb": 0}

        return {
            "overall_score": 0.75,  # Baseline score
            "metrics": {
                "latency_ms": 50.0,
                "throughput": 100.0,
                "efficiency": 0.8,
            },
            "component_performance": {
                "cli": 0.85,
                "database": 0.80,
                "cascade": 0.75,
            },
            "memory_usage": memory,
            "recommendations": [
                "Full performance analysis not yet implemented",
                "Baseline metrics shown - actual profiling coming soon",
            ],
            "detailed_breakdown": {} if not verbose else {
                "note": "Detailed breakdown requires full implementation"
            },
        }

    def analyze_performance(
        self,
        target: str = "system",
        context: dict | None = None,
        detailed: bool = False
    ) -> dict[str, Any]:
        """
        Analyze performance of a target component.

        Args:
            target: Target to analyze (system, cli, database, etc.)
            context: Additional context for analysis
            detailed: Include detailed metrics

        Returns:
            Performance analysis results
        """
        return {
            "performance_score": 0.75,
            "performance_grade": "B",
            "dimensions": {
                "latency": 0.80,
                "throughput": 0.75,
                "memory": 0.70,
                "reliability": 0.85,
            },
            "bottlenecks": [
                {
                    "description": "Performance analysis not fully implemented",
                    "severity": "low",
                }
            ],
            "optimizations": [
                {
                    "suggestion": "Full implementation will provide actionable insights",
                    "impact": "medium",
                    "effort": "planned",
                }
            ],
            "detailed_metrics": {} if not detailed else {
                "note": {"status": "Detailed metrics require full implementation"}
            },
            "historical_comparison": {
                "trend": "stable",
                "change_percentage": 0.0,
            },
        }
