#!/usr/bin/env python3
"""
神经网络模拟：人格演化的神经网络层
"""

import json
import numpy as np
from pathlib import Path
from typing import Dict, List


class NeuralNetworkSimulator:
    """神经网络模拟器"""

    # 神经网络层级
    NETWORK_LAYERS = [
        'input_layer',          # 输入层
        'hidden_layer_1',       # 隐藏层1
        'hidden_layer_2',       # 隐藏层2
        'hidden_layer_3',       # 隐藏层3
        'output_layer'          # 输出层
    ]

    # 激活函数类型
    ACTIVATION_FUNCTIONS = ['sigmoid', 'tanh', 'relu', 'softmax']

    def __init__(self):
        self.network_config = {}
        self.trained_weights = {}

    def initialize_network(self, personality_model: Dict) -> Dict:
        """初始化神经网络"""

        network = {
            'metadata': {
                'personality_type': personality_model.get('integrated_profile', {}).get('personality_type', 'holographic'),
                'total_layers': len(self.NETWORK_LAYERS),
                'initialized_at': str(np.datetime64('now'))
            },
            'layers': {},
            'connections': {},
            'activation_functions': {},
            'learning_parameters': {}
        }

        # 初始化每层
        for i, layer_name in enumerate(self.NETWORK_LAYERS):
            layer_config = self._initialize_layer(layer_name, i, personality_model)
            network['layers'][layer_name] = layer_config

        # 初始化连接
        network['connections'] = self._initialize_connections(network['layers'])

        # 设置激活函数
        network['activation_functions'] = self._assign_activation_functions()

        # 设置学习参数
        network['learning_parameters'] = self._set_learning_parameters()

        return network

    def _initialize_layer(self, layer_name: str, layer_index: int,
                          personality_model: Dict) -> Dict:
        """初始化单层"""

        # 简化处理：随机生成神经元数量
        np.random.seed(hash(layer_name) % 2**32)

        if layer_name == 'input_layer':
            num_neurons = 12  # 对应8维+4轴
        elif layer_name == 'output_layer':
            num_neurons = 8  # 对应8维输出
        else:
            num_neurons = np.random.randint(16, 32)

        return {
            'layer_id': layer_index,
            'layer_name': layer_name,
            'num_neurons': num_neurons,
            'neuron_states': np.zeros(num_neurons).tolist(),
            'bias': np.random.uniform(-0.5, 0.5, num_neurons).tolist()
        }

    def _initialize_connections(self, layers: Dict) -> Dict:
        """初始化层间连接"""

        connections = {}
        layer_names = list(layers.keys())

        for i in range(len(layer_names) - 1):
            from_layer = layer_names[i]
            to_layer = layer_names[i + 1]

            num_from = layers[from_layer]['num_neurons']
            num_to = layers[to_layer]['num_neurons']

            # 初始化权重矩阵
            np.random.seed(hash(f"{from_layer}_{to_layer}") % 2**32)
            weights = np.random.uniform(-1, 1, (num_to, num_from))

            connections[f"{from_layer}_to_{to_layer}"] = {
                'from_layer': from_layer,
                'to_layer': to_layer,
                'weights': weights.tolist(),
                'weight_matrix_shape': (num_to, num_from)
            }

        return connections

    def _assign_activation_functions(self) -> Dict:
        """分配激活函数"""

        activation_map = {
            'input_layer': None,
            'hidden_layer_1': 'relu',
            'hidden_layer_2': 'relu',
            'hidden_layer_3': 'relu',
            'output_layer': 'sigmoid'
        }

        return activation_map

    def _set_learning_parameters(self) -> Dict:
        """设置学习参数"""

        return {
            'learning_rate': 0.01,
            'momentum': 0.9,
            'decay_rate': 0.001,
            'batch_size': 32,
            'epochs': 100
        }

    def simulate_forward_propagation(self, network: Dict,
                                    input_data: Dict) -> Dict:
        """模拟前向传播"""

        simulation = {
            'input_data': input_data,
            'layer_outputs': {},
            'activations': {},
            'final_output': None
        }

        # 准备输入
        current_input = self._prepare_input_layer(input_data)
        simulation['layer_outputs']['input_layer'] = current_input

        # 逐层传播
        for i in range(len(self.NETWORK_LAYERS) - 1):
            from_layer = self.NETWORK_LAYERS[i]
            to_layer = self.NETWORK_LAYERS[i + 1]

            # 获取连接权重
            connection_key = f"{from_layer}_to_{to_layer}"
            if connection_key not in network['connections']:
                continue

            connection = network['connections'][connection_key]
            weights = np.array(connection['weights'])

            # 矩阵乘法
            layer_output = np.dot(weights, current_input)

            # 加偏置
            bias = np.array(network['layers'][to_layer]['bias'])
            layer_output = layer_output + bias

            # 应用激活函数
            activation_func = network['activation_functions'][to_layer]
            if activation_func:
                layer_output = self._apply_activation(layer_output, activation_func)

            current_input = layer_output

            simulation['layer_outputs'][to_layer] = current_input.tolist()

        # 最终输出
        simulation['final_output'] = current_input.tolist()

        return simulation

    def _prepare_input_layer(self, input_data: Dict) -> np.ndarray:
        """准备输入层"""

        # 简化处理：从输入数据中提取特征
        features = []

        # 8维特征
        if 'dimensions' in input_data:
            for dim_name in ['cognitive', 'emotional', 'behavioral', 'social',
                            'creative', 'spiritual', 'practical', 'shadow']:
                if dim_name in input_data['dimensions']:
                    score = input_data['dimensions'][dim_name]['scores']['normalized_score']
                    features.append(score)

        # 填充到12个神经元
        while len(features) < 12:
            features.append(0.0)

        return np.array(features[:12])

    def _apply_activation(self, x: np.ndarray, activation: str) -> np.ndarray:
        """应用激活函数"""

        if activation == 'sigmoid':
            return 1 / (1 + np.exp(-x))
        elif activation == 'tanh':
            return np.tanh(x)
        elif activation == 'relu':
            return np.maximum(0, x)
        elif activation == 'softmax':
            exp_x = np.exp(x - np.max(x))
            return exp_x / exp_x.sum()
        else:
            return x

    def train_network(self, network: Dict, training_data: List[Dict],
                     labels: List[Dict]) -> Dict:
        """训练网络（简化版）"""

        training_result = {
            'network_state': network,
            'training_loss': [],
            'training_accuracy': [],
            'epochs_trained': 0
        }

        # 简化处理：仅模拟训练过程
        np.random.seed(42)

        for epoch in range(network['learning_parameters']['epochs']):
            epoch_loss = np.random.uniform(0.1, 0.5)
            epoch_accuracy = np.random.uniform(0.6, 0.9)

            training_result['training_loss'].append(epoch_loss)
            training_result['training_accuracy'].append(epoch_accuracy)

        training_result['epochs_trained'] = network['learning_parameters']['epochs']

        return training_result

    def predict_personality_evolution(self, network: Dict,
                                     current_state: Dict,
                                     time_horizon: int) -> Dict:
        """预测人格演化"""

        prediction = {
            'current_state': current_state,
            'time_horizon': time_horizon,
            'evolution_trajectory': [],
            'predicted_outcomes': []
        }

        # 模拟演化轨迹
        for t in range(time_horizon):
            # 简化处理：随机生成演化状态
            np.random.seed(hash(f"evolution_{t}") % 2**32)

            evolution_step = {
                'time_step': t,
                'state_changes': self._generate_state_changes(current_state),
                'network_activation': self._simulate_network_activation(network)
            }

            prediction['evolution_trajectory'].append(evolution_step)

        # 预测最终结果
        prediction['predicted_outcomes'] = self._predict_final_outcomes(
            prediction['evolution_trajectory']
        )

        return prediction

    def _generate_state_changes(self, current_state: Dict) -> Dict:
        """生成状态变化"""
        return {
            'dimension_shifts': [],
            'axis_realignments': [],
            'path_progressions': []
        }

    def _simulate_network_activation(self, network: Dict) -> Dict:
        """模拟网络激活"""
        return {
            'activation_patterns': {},
            'neural_plasticity': 0.7,
            'learning_rate_adaptation': 0.01
        }

    def _predict_final_outcomes(self, trajectory: List[Dict]) -> List[str]:
        """预测最终结果"""

        outcomes = [
            "Increased dimensional integration",
            "Enhanced path mastery",
            "Greater axis balance",
            "Deepened personality coherence"
        ]

        return outcomes

    def visualize_network(self, network: Dict) -> Dict:
        """可视化网络结构"""

        visualization = {
            'network_architecture': {
                'layers': list(network['layers'].keys()),
                'connections': list(network['connections'].keys())
            },
            'layer_sizes': {
                layer_name: layer_info['num_neurons']
                for layer_name, layer_info in network['layers'].items()
            },
            'activation_summary': network['activation_functions'],
            'connection_weights_summary': {}
        }

        # 生成连接权重摘要
        for conn_name, conn_info in network['connections'].items():
            weights = np.array(conn_info['weights'])
            visualization['connection_weights_summary'][conn_name] = {
                'mean': float(np.mean(weights)),
                'std': float(np.std(weights)),
                'min': float(np.min(weights)),
                'max': float(np.max(weights))
            }

        return visualization


def main():
    import argparse

    parser = argparse.ArgumentParser(description='神经网络模拟')
    parser.add_argument('--model', type=str, required=True,
                       help='人格模型文件路径 (JSON)')
    parser.add_argument('--scan', type=str, help='扫描结果文件路径 (JSON)')
    parser.add_argument('--mode', choices=['init', 'simulate', 'predict'],
                       default='init', help='执行模式')

    args = parser.parse_args()

    # 读取人格模型
    with open(args.model, 'r', encoding='utf-8') as f:
        personality_model = json.load(f)

    simulator = NeuralNetworkSimulator()

    if args.mode == 'init':
        # 初始化网络
        network = simulator.initialize_network(personality_model)
        print(json.dumps(network, ensure_ascii=False, indent=2))

    elif args.mode == 'simulate':
        # 模拟前向传播
        with open(args.scan, 'r', encoding='utf-8') as f:
            scan_result = json.load(f)

        network = simulator.initialize_network(personality_model)
        simulation = simulator.simulate_forward_propagation(network, scan_result)
        print(json.dumps(simulation, ensure_ascii=False, indent=2))

    elif args.mode == 'predict':
        # 预测演化
        network = simulator.initialize_network(personality_model)
        prediction = simulator.predict_personality_evolution(
            network,
            personality_model,
            time_horizon=5
        )
        print(json.dumps(prediction, ensure_ascii=False, indent=2))


if __name__ == '__main__':
    main()