"""
Neural Architecture Search Agent

Searches for optimal neural network architectures using NAS techniques.
Supports various search strategies and optimization methods.
"""

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


class NeuralArchitectureSearchAgent(BaseAgent):
    """
    Searches for optimal neural network architectures.

    Features:
    - Multiple NAS strategies (random, evolutionary, RL-based, gradient-based)
    - AutoKeras, NASNet, ENAS, DARTS integration
    - Cell-based and layer-wise search
    - Multi-objective optimization (accuracy, latency, size)
    - Hardware-aware NAS
    - Transfer learning from searched architectures
    - One-shot and multi-shot NAS
    - Architecture encoding and search space design
    """

    def __init__(self):
        super().__init__(
            name='neural-architecture-search',
            description='Search for optimal neural network architectures',
            category='ai_ml',
            version='1.0.0',
            tags=['ml', 'nas', 'deep-learning', 'optimization', 'architecture']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Search for optimal neural architecture.

        Args:
            params: {
                'task_config': {
                    'task_type': 'classification|detection|segmentation|nlp',
                    'dataset': str,
                    'input_shape': tuple,
                    'num_classes': int,
                    'metric': 'accuracy|mAP|iou|bleu'
                },
                'search_config': {
                    'strategy': 'random|evolutionary|rl|gradient_based|bayesian',
                    'search_space': 'macro|micro|cell_based|layer_wise',
                    'max_trials': int,
                    'time_budget_hours': int,
                    'population_size': int,  # For evolutionary
                    'generations': int  # For evolutionary
                },
                'architecture_space': {
                    'operations': [
                        'conv3x3', 'conv5x5', 'depthwise_conv',
                        'max_pool', 'avg_pool', 'skip_connection',
                        'dilated_conv', 'squeeze_excite'
                    ],
                    'layers': {
                        'min_layers': int,
                        'max_layers': int
                    },
                    'channels': {
                        'min_channels': int,
                        'max_channels': int,
                        'channel_multiplier': List[int]
                    },
                    'cells': {
                        'num_cells': int,
                        'nodes_per_cell': int
                    }
                },
                'objectives': {
                    'primary': 'accuracy|loss',
                    'secondary': ['latency', 'model_size', 'flops'],
                    'multi_objective': bool,
                    'constraints': {
                        'max_latency_ms': float,
                        'max_model_size_mb': float,
                        'max_flops': int
                    }
                },
                'training_config': {
                    'epochs_per_trial': int,
                    'batch_size': int,
                    'learning_rate': float,
                    'early_stopping': bool
                },
                'hardware_config': {
                    'target_hardware': 'gpu|tpu|mobile|edge',
                    'hardware_aware': bool,
                    'measure_latency': bool
                },
                'optimization': {
                    'weight_sharing': bool,
                    'one_shot_nas': bool,
                    'progressive_search': bool,
                    'transfer_learning': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'search_id': str,
                'best_architecture': {
                    'architecture_id': str,
                    'description': str,
                    'structure': Dict[str, Any],
                    'cell_structure': List[Dict[str, Any]],
                    'operations': List[str],
                    'parameters': int,
                    'flops': int
                },
                'performance': {
                    'accuracy': float,
                    'validation_accuracy': float,
                    'test_accuracy': float,
                    'training_time_hours': float
                },
                'efficiency_metrics': {
                    'model_size_mb': float,
                    'inference_latency_ms': float,
                    'flops': int,
                    'parameters': int,
                    'memory_usage_mb': float
                },
                'search_statistics': {
                    'total_architectures_evaluated': int,
                    'search_time_hours': float,
                    'best_found_at_iteration': int,
                    'convergence_iteration': int
                },
                'pareto_front': List[Dict[str, Any]],
                'top_architectures': List[Dict[str, Any]],
                'architecture_insights': {
                    'most_common_operations': List[str],
                    'optimal_depth': int,
                    'optimal_width': int,
                    'operation_importance': Dict[str, float]
                },
                'comparison': {
                    'baseline_architecture': str,
                    'baseline_accuracy': float,
                    'improvement_percentage': float,
                    'efficiency_improvement': float
                },
                'artifacts': {
                    'architecture_config': str,
                    'trained_model': str,
                    'search_history': str,
                    'visualization': str
                },
                'recommendations': List[str]
            }
        """
        task_config = params.get('task_config', {})
        search_config = params.get('search_config', {})
        objectives = params.get('objectives', {})

        task_type = task_config.get('task_type', 'classification')
        search_strategy = search_config.get('strategy', 'evolutionary')

        self.logger.info(
            f"Starting NAS for {task_type} using {search_strategy} strategy"
        )

        # Mock NAS results
        return {
            'status': 'success',
            'search_id': f'nas_{search_strategy}_{task_type}',
            'search_strategy': search_strategy,
            'task_type': task_type,
            'best_architecture': {
                'architecture_id': 'nas_arch_optimal_001',
                'description': 'Efficient convolutional architecture with residual connections',
                'structure': {
                    'stem': ['conv3x3_32', 'conv3x3_64'],
                    'cells': [
                        {
                            'cell_type': 'normal',
                            'operations': [
                                'depthwise_conv_128',
                                'squeeze_excite',
                                'skip_connection'
                            ]
                        },
                        {
                            'cell_type': 'reduction',
                            'operations': [
                                'conv3x3_256',
                                'max_pool',
                                'dilated_conv_256'
                            ]
                        }
                    ],
                    'head': ['global_avg_pool', 'dense_1024', 'dense_classes']
                },
                'cell_structure': [
                    {
                        'node_0': ['input', 'depthwise_conv'],
                        'node_1': ['node_0', 'squeeze_excite'],
                        'node_2': ['input', 'skip_connection'],
                        'output': ['concat', 'node_1', 'node_2']
                    }
                ],
                'operations': [
                    'depthwise_conv',
                    'squeeze_excite',
                    'skip_connection',
                    'dilated_conv',
                    'max_pool'
                ],
                'parameters': 3456789,
                'flops': 1234567890,
                'depth': 28,
                'width_multiplier': 1.0
            },
            'performance': {
                'accuracy': 0.9734,
                'validation_accuracy': 0.9712,
                'test_accuracy': 0.9689,
                'top5_accuracy': 0.9945,
                'training_time_hours': 2.5,
                'convergence_epoch': 85
            },
            'efficiency_metrics': {
                'model_size_mb': 13.2,
                'inference_latency_ms': 8.4,
                'flops': 1234567890,
                'parameters': 3456789,
                'memory_usage_mb': 245.6,
                'throughput_samples_per_sec': 1250,
                'energy_consumption_mj': 45.2
            },
            'search_statistics': {
                'total_architectures_evaluated': 500,
                'search_time_hours': 48.5,
                'best_found_at_iteration': 342,
                'convergence_iteration': 450,
                'architectures_per_hour': 10.3,
                'total_gpu_hours': 145.6
            },
            'pareto_front': [
                {
                    'architecture_id': 'nas_arch_001',
                    'accuracy': 0.9734,
                    'latency_ms': 8.4,
                    'size_mb': 13.2
                },
                {
                    'architecture_id': 'nas_arch_002',
                    'accuracy': 0.9689,
                    'latency_ms': 5.2,
                    'size_mb': 8.1
                },
                {
                    'architecture_id': 'nas_arch_003',
                    'accuracy': 0.9623,
                    'latency_ms': 3.4,
                    'size_mb': 5.6
                }
            ],
            'top_architectures': [
                {
                    'rank': 1,
                    'architecture_id': 'nas_arch_optimal_001',
                    'accuracy': 0.9734,
                    'latency_ms': 8.4,
                    'score': 0.9712
                },
                {
                    'rank': 2,
                    'architecture_id': 'nas_arch_002',
                    'accuracy': 0.9689,
                    'latency_ms': 5.2,
                    'score': 0.9623
                },
                {
                    'rank': 3,
                    'architecture_id': 'nas_arch_003',
                    'accuracy': 0.9656,
                    'latency_ms': 4.1,
                    'score': 0.9589
                }
            ],
            'architecture_insights': {
                'most_common_operations': [
                    'depthwise_conv (78%)',
                    'squeeze_excite (65%)',
                    'skip_connection (82%)',
                    'dilated_conv (45%)'
                ],
                'optimal_depth': 28,
                'optimal_width': 128,
                'optimal_cell_repeats': 6,
                'operation_importance': {
                    'skip_connection': 0.89,
                    'depthwise_conv': 0.85,
                    'squeeze_excite': 0.72,
                    'dilated_conv': 0.58,
                    'max_pool': 0.45
                },
                'design_patterns': [
                    'Residual connections improve training stability',
                    'Depthwise separable convolutions reduce parameters',
                    'Squeeze-and-excitation blocks boost accuracy',
                    'Progressive channel expansion works well'
                ]
            },
            'comparison': {
                'baseline_architecture': 'ResNet-50',
                'baseline_accuracy': 0.9234,
                'baseline_latency_ms': 15.6,
                'baseline_size_mb': 98.3,
                'improvement_percentage': 5.42,
                'latency_improvement': '46% faster',
                'size_improvement': '87% smaller'
            },
            'hardware_compatibility': {
                'gpu_optimized': True,
                'tpu_compatible': True,
                'mobile_ready': True,
                'edge_deployable': True,
                'quantization_friendly': True
            },
            'artifacts': {
                'architecture_config': '/models/nas/architecture_config.json',
                'trained_model': '/models/nas/best_model.pth',
                'search_history': '/models/nas/search_history.json',
                'visualization': '/models/nas/architecture_viz.png',
                'pareto_front_plot': '/models/nas/pareto_front.png',
                'cell_diagram': '/models/nas/cell_structure.png'
            },
            'recommendations': [
                'Found architecture achieves 97.34% accuracy with 8.4ms latency',
                'Architecture is 46% faster and 87% smaller than ResNet-50',
                'Depthwise separable convolutions are key to efficiency',
                'Skip connections improve accuracy by ~3%',
                'Architecture is well-suited for mobile deployment',
                'Consider using this architecture as starting point for transfer learning',
                'Squeeze-and-excitation blocks provide good accuracy/cost tradeoff',
                'Architecture generalizes well across different datasets',
                'Further optimization possible with quantization (2-3x speedup)'
            ]
        }

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

        task_config = params['task_config']
        if 'task_type' not in task_config:
            self.logger.error("Missing required field: task_config.task_type")
            return False

        valid_tasks = ['classification', 'detection', 'segmentation', 'nlp']
        if task_config['task_type'] not in valid_tasks:
            self.logger.error(f"Invalid task type: {task_config['task_type']}")
            return False

        return True
