"""
Performance Profiler Agent

Profiles code performance, identifies bottlenecks, and provides
optimization recommendations.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent


class PerformanceProfilerAgent(BaseAgent):
    """
    Profiles and optimizes code performance.

    Features:
    - CPU profiling
    - Memory profiling
    - I/O profiling
    - Bottleneck detection
    - Performance metrics
    - Optimization suggestions
    """

    def __init__(self):
        super().__init__(
            name='performance-profiler',
            description='Profile and optimize code performance',
            category='engineering',
            version='1.0.0',
            tags=['performance', 'profiling', 'optimization', 'benchmarking']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Profile code performance.

        Args:
            params: {
                'target': str,           # File, function, or endpoint to profile
                'language': 'python|javascript|typescript|go|rust',
                'profile_type': 'cpu|memory|io|all',
                'duration': int,         # Profiling duration in seconds
                'options': {
                    'flamegraph': bool,
                    'call_graph': bool,
                    'line_profiling': bool,
                    'compare_baseline': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'profile_results': Dict,
                'bottlenecks': List[Dict],
                'optimization_suggestions': List[str],
                'metrics': Dict
            }
        """
        target = params.get('target')
        language = params.get('language', 'python')
        profile_type = params.get('profile_type', 'cpu')
        duration = params.get('duration', 30)
        options = params.get('options', {})

        self.logger.info(
            f"Profiling {target} ({profile_type} profile for {duration}s)"
        )

        # Mock profiling results
        bottlenecks = [
            {
                'function': 'process_data',
                'file': 'src/services/data_processor.py',
                'line': 156,
                'cpu_time': 2.34,
                'cpu_percent': 45.2,
                'calls': 1234,
                'issue': 'CPU-intensive loop with O(n²) complexity',
                'severity': 'high'
            },
            {
                'function': 'database_query',
                'file': 'src/models/user.py',
                'line': 89,
                'cpu_time': 1.23,
                'cpu_percent': 23.8,
                'calls': 567,
                'issue': 'N+1 query problem',
                'severity': 'high'
            },
            {
                'function': 'json_serialization',
                'file': 'src/api/serializers.py',
                'line': 234,
                'cpu_time': 0.89,
                'cpu_percent': 17.2,
                'calls': 2345,
                'issue': 'Inefficient JSON serialization',
                'severity': 'medium'
            }
        ]

        optimization_suggestions = [
            'Replace O(n²) loop in process_data with more efficient algorithm (e.g., hash map lookup)',
            'Use batch queries or select_related/prefetch_related to fix N+1 queries',
            'Cache frequently accessed data to reduce database calls',
            'Use orjson or ujson for faster JSON serialization',
            'Add database indexes on frequently queried columns',
            'Implement pagination for large result sets',
            'Use connection pooling for database connections',
            'Consider async/await for I/O-bound operations'
        ]

        profile_results = {
            'cpu_profile': {
                'total_time': 5.18,
                'function_calls': 12456,
                'top_functions': [
                    {'name': 'process_data', 'time': 2.34, 'percent': 45.2},
                    {'name': 'database_query', 'time': 1.23, 'percent': 23.8},
                    {'name': 'json_serialization', 'time': 0.89, 'percent': 17.2}
                ]
            },
            'memory_profile': {
                'peak_memory': '256 MB',
                'average_memory': '128 MB',
                'memory_leaks': [],
                'top_allocations': [
                    {'location': 'data_processor.py:156', 'size': '45 MB'},
                    {'location': 'serializers.py:234', 'size': '23 MB'}
                ]
            },
            'io_profile': {
                'total_io_time': 1.45,
                'read_operations': 234,
                'write_operations': 89,
                'database_time': 1.23,
                'network_time': 0.22
            }
        }

        metrics = {
            'requests_per_second': 156.3,
            'average_response_time': '32ms',
            'p50_response_time': '28ms',
            'p95_response_time': '67ms',
            'p99_response_time': '123ms',
            'error_rate': 0.02,
            'throughput': '2.3 MB/s'
        }

        return {
            'status': 'success',
            'target': target,
            'language': language,
            'profile_type': profile_type,
            'duration_seconds': duration,
            'profile_results': profile_results,
            'bottlenecks': bottlenecks,
            'total_bottlenecks': len(bottlenecks),
            'critical_issues': sum(1 for b in bottlenecks if b['severity'] == 'high'),
            'optimization_suggestions': optimization_suggestions,
            'metrics': metrics,
            'performance_score': 6.8,
            'improvement_potential': {
                'cpu_optimization': '40-50%',
                'memory_optimization': '20-30%',
                'io_optimization': '30-40%'
            },
            'files_generated': [
                f'profiles/{target.replace("/", "_")}_cpu.prof',
                f'profiles/{target.replace("/", "_")}_memory.prof',
                f'profiles/{target.replace("/", "_")}_flamegraph.svg' if options.get('flamegraph') else None,
                f'profiles/{target.replace("/", "_")}_report.html'
            ],
            'visualization': {
                'flamegraph': options.get('flamegraph', False),
                'call_graph': options.get('call_graph', False),
                'timeline': True
            },
            'next_steps': [
                'Review bottlenecks and prioritize fixes',
                'Implement optimization suggestions',
                'Re-profile after optimizations',
                'Set up continuous profiling',
                'Add performance benchmarks',
                'Monitor production performance'
            ]
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate performance profiling parameters."""
        if 'target' not in params:
            self.logger.error("Missing required field: target")
            return False

        valid_profile_types = ['cpu', 'memory', 'io', 'all']
        profile_type = params.get('profile_type', 'cpu')

        if profile_type not in valid_profile_types:
            self.logger.error(f"Invalid profile type: {profile_type}")
            return False

        return True
