"""
Inference Optimizer Agent

Optimizes ML model inference for production performance.
Supports quantization, pruning, distillation, and hardware acceleration.
"""

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


class InferenceOptimizerAgent(BaseAgent):
    """
    Optimizes ML model inference performance.

    Features:
    - Model quantization (int8, int16, float16)
    - Model pruning and sparsification
    - Knowledge distillation
    - Graph optimization and fusion
    - Hardware-specific optimization (GPU, TPU, CPU)
    - Batch inference optimization
    - Model compilation (TensorRT, OpenVINO, TVM)
    - ONNX export and optimization
    """

    def __init__(self):
        super().__init__(
            name='inference-optimizer',
            description='Optimize ML model inference for production',
            category='ai_ml',
            version='1.0.0',
            tags=['ml', 'optimization', 'inference', 'quantization', 'performance']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Optimize model for inference.

        Args:
            params: {
                'model_config': {
                    'model_path': str,
                    'framework': 'tensorflow|pytorch|onnx',
                    'model_type': str
                },
                'optimization_techniques': {
                    'quantization': {
                        'enabled': bool,
                        'precision': 'int8|int16|float16|mixed',
                        'calibration_dataset': str,
                        'quantize_weights': bool,
                        'quantize_activations': bool
                    },
                    'pruning': {
                        'enabled': bool,
                        'method': 'magnitude|structured|unstructured',
                        'sparsity_target': float,  # e.g., 0.5 for 50% sparse
                        'fine_tune_after': bool
                    },
                    'distillation': {
                        'enabled': bool,
                        'teacher_model': str,
                        'temperature': float,
                        'alpha': float  # Distillation loss weight
                    },
                    'graph_optimization': {
                        'enabled': bool,
                        'techniques': ['fusion', 'constant_folding', 'dead_code_elimination']
                    }
                },
                'target_hardware': {
                    'device': 'cpu|gpu|tpu|edge|mobile',
                    'architecture': str,  # e.g., 'x86', 'arm', 'cuda'
                    'optimization_level': 'basic|moderate|aggressive'
                },
                'compilation': {
                    'enabled': bool,
                    'compiler': 'tensorrt|openvino|tvm|xla',
                    'target_platform': str,
                    'optimization_flags': List[str]
                },
                'batch_optimization': {
                    'dynamic_batching': bool,
                    'max_batch_size': int,
                    'batch_timeout_ms': int
                },
                'validation': {
                    'accuracy_threshold': float,  # Min acceptable accuracy after optimization
                    'benchmark_data': str,
                    'compare_with_original': bool
                },
                'export_config': {
                    'format': 'onnx|tflite|torchscript|savedmodel',
                    'output_path': str
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'optimization_id': str,
                'original_model': {
                    'size_mb': float,
                    'parameters': int,
                    'inference_time_ms': float,
                    'accuracy': float
                },
                'optimized_model': {
                    'size_mb': float,
                    'parameters': int,
                    'inference_time_ms': float,
                    'accuracy': float,
                    'export_format': str,
                    'path': str
                },
                'improvements': {
                    'size_reduction_percentage': float,
                    'speedup_factor': float,
                    'accuracy_drop_percentage': float,
                    'throughput_increase': float
                },
                'techniques_applied': List[Dict[str, Any]],
                'performance_metrics': {
                    'latency': {
                        'p50_ms': float,
                        'p95_ms': float,
                        'p99_ms': float
                    },
                    'throughput': {
                        'samples_per_second': float,
                        'batch_size': int
                    },
                    'memory': {
                        'peak_usage_mb': float,
                        'reduction_percentage': float
                    },
                    'power_consumption': {
                        'watts': float,
                        'reduction_percentage': float
                    }
                },
                'accuracy_validation': {
                    'original_accuracy': float,
                    'optimized_accuracy': float,
                    'accuracy_drop': float,
                    'within_threshold': bool,
                    'test_samples': int
                },
                'hardware_utilization': {
                    'device': str,
                    'gpu_utilization': float,
                    'cpu_utilization': float,
                    'memory_bandwidth_utilization': float
                },
                'recommendations': List[str]
            }
        """
        model_config = params.get('model_config', {})
        optimization_techniques = params.get('optimization_techniques', {})
        target_hardware = params.get('target_hardware', {})

        self.logger.info(
            f"Optimizing model for {target_hardware.get('device', 'cpu')} inference"
        )

        # Mock optimization results
        original_size = 245.6
        original_time = 45.3
        original_accuracy = 0.9712

        techniques = []
        if optimization_techniques.get('quantization', {}).get('enabled'):
            techniques.append('quantization')
        if optimization_techniques.get('pruning', {}).get('enabled'):
            techniques.append('pruning')
        if optimization_techniques.get('graph_optimization', {}).get('enabled'):
            techniques.append('graph_optimization')

        # Calculate improvements
        size_reduction = 0.0
        speedup = 1.0
        accuracy_drop = 0.0

        if 'quantization' in techniques:
            size_reduction += 0.75  # 75% reduction
            speedup *= 2.5
            accuracy_drop += 0.005

        if 'pruning' in techniques:
            size_reduction += 0.50
            speedup *= 1.8
            accuracy_drop += 0.003

        optimized_size = original_size * (1 - min(size_reduction, 0.9))
        optimized_time = original_time / speedup
        optimized_accuracy = original_accuracy - accuracy_drop

        return {
            'status': 'success',
            'optimization_id': f'opt_{model_config.get("framework", "pytorch")}',
            'original_model': {
                'size_mb': original_size,
                'parameters': 2456789,
                'inference_time_ms': original_time,
                'accuracy': original_accuracy,
                'framework': model_config.get('framework', 'pytorch')
            },
            'optimized_model': {
                'size_mb': round(optimized_size, 2),
                'parameters': int(2456789 * (1 - size_reduction * 0.5)),
                'inference_time_ms': round(optimized_time, 2),
                'accuracy': round(optimized_accuracy, 4),
                'export_format': params.get('export_config', {}).get('format', 'onnx'),
                'path': '/models/optimized/model_optimized.onnx'
            },
            'improvements': {
                'size_reduction_percentage': round(size_reduction * 100, 2),
                'speedup_factor': round(speedup, 2),
                'accuracy_drop_percentage': round(accuracy_drop * 100, 3),
                'throughput_increase': round((speedup - 1) * 100, 2),
                'memory_reduction_percentage': round(size_reduction * 80, 2)
            },
            'techniques_applied': [
                {
                    'technique': 'quantization',
                    'precision': 'int8',
                    'size_reduction': '75%',
                    'speedup': '2.5x',
                    'accuracy_impact': '-0.5%'
                },
                {
                    'technique': 'graph_optimization',
                    'operations_fused': 45,
                    'nodes_removed': 23,
                    'speedup': '1.2x'
                }
            ] if techniques else [],
            'performance_metrics': {
                'latency': {
                    'p50_ms': round(optimized_time * 0.8, 2),
                    'p95_ms': round(optimized_time * 1.2, 2),
                    'p99_ms': round(optimized_time * 1.5, 2),
                    'original_p50_ms': round(original_time * 0.8, 2)
                },
                'throughput': {
                    'samples_per_second': round(1000 / optimized_time, 2),
                    'original_samples_per_second': round(1000 / original_time, 2),
                    'batch_size': params.get('batch_optimization', {}).get('max_batch_size', 32)
                },
                'memory': {
                    'peak_usage_mb': round(optimized_size * 1.5, 2),
                    'reduction_percentage': round(size_reduction * 80, 2),
                    'original_peak_usage_mb': round(original_size * 1.5, 2)
                },
                'power_consumption': {
                    'watts': 45.5,
                    'reduction_percentage': 35.2,
                    'original_watts': 70.3
                }
            },
            'accuracy_validation': {
                'original_accuracy': original_accuracy,
                'optimized_accuracy': optimized_accuracy,
                'accuracy_drop': round(accuracy_drop, 4),
                'within_threshold': accuracy_drop < params.get('validation', {}).get('accuracy_threshold', 0.02),
                'test_samples': 10000,
                'validation_passed': True
            },
            'hardware_utilization': {
                'device': target_hardware.get('device', 'cpu'),
                'gpu_utilization': 78.5 if target_hardware.get('device') == 'gpu' else 0.0,
                'cpu_utilization': 45.2,
                'memory_bandwidth_utilization': 67.8,
                'cache_hit_rate': 89.3
            },
            'compatibility': {
                'original_framework': model_config.get('framework', 'pytorch'),
                'export_format': params.get('export_config', {}).get('format', 'onnx'),
                'supported_runtimes': ['onnxruntime', 'tensorrt', 'openvino'],
                'target_platforms': ['x86', 'arm', 'cuda']
            },
            'recommendations': [
                f'Model size reduced by {round(size_reduction * 100, 1)}% (from {original_size}MB to {round(optimized_size, 2)}MB)',
                f'Inference speed improved by {round(speedup, 2)}x (from {original_time}ms to {round(optimized_time, 2)}ms)',
                f'Accuracy drop of only {round(accuracy_drop * 100, 3)}% - within acceptable threshold',
                'Quantization to int8 provides best speed/accuracy tradeoff',
                'Consider dynamic batching to improve throughput further',
                'Model is now ready for production deployment',
                'Use TensorRT for additional GPU optimizations',
                'Enable mixed precision for better accuracy retention'
            ]
        }

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

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

        return True
