"""
OrchestratorAI Advanced Components
This module contains advanced features for the OrchestratorAI system, including
Transfer Learning, Neural Architecture Search, and Federated Optimization.
"""
import random
from typing import Dict, List, Any

class FederatedOptimizer:
    """Optimizes models using federated learning principles.

    This class simulates a federated learning process where a global model is
    improved by aggregating updates from multiple clients, without exposing
    their local data.
    """

    def __init__(self, num_clients: int):
        """Initializes the FederatedOptimizer.

        Args:
            num_clients (int): The number of clients to simulate in the
                federated learning process.
        """
        self.num_clients = num_clients

    def optimize(self, global_model: Any, client_data_sizes: List[int]) -> Any:
        """
        Performs a round of federated optimization using the FedAvg algorithm.
        This is a simulation where client training is abstracted.
        """
        print(f"  [FO] Optimizing with {self.num_clients} clients.")

        # 1. Simulate client training
        client_models = []
        for i in range(self.num_clients):
            # In a real scenario, the global_model would be sent to a client,
            # trained on its local data, and the updated model would be returned.
            # Here, we simulate this by creating a slightly modified version
            # of the global model to represent a local update.
            local_model = self._simulate_client_training(global_model)
            client_models.append(local_model)

        # 2. Aggregate the client models
        updated_global_model = self._aggregate(client_models, client_data_sizes)

        return updated_global_model

    def _simulate_client_training(self, model: Any) -> Any:
        """
        Simulates the local training process on a client's device.
        For this simulation, we'll just create a copy and slightly perturb its weights.
        """
        if not hasattr(model, 'clone'):
            # This is a fallback for models that don't have a clone method.
            # A more robust implementation would require a specific model interface.
            return model

        local_model = model.clone()

        # Perturb weights to simulate local training
        if hasattr(local_model, 'weights') and isinstance(local_model.weights, dict):
            for key in local_model.weights:
                local_model.weights[key] *= (1 + random.uniform(-0.05, 0.05))

        return local_model

    def _aggregate(self, client_models: List[Any], client_data_sizes: List[int]) -> Any:
        """
        Aggregates client models into a new global model using Federated Averaging.
        """
        if not client_models:
            return None

        total_data_size = sum(client_data_sizes)

        # Initialize the aggregated model from the first client model structure
        aggregated_model = client_models[0].clone()

        # Zero out the weights, biases, and temperature of the aggregated model
        if hasattr(aggregated_model, 'weights'):
            for key in aggregated_model.weights:
                aggregated_model.weights[key] = 0.0
        if hasattr(aggregated_model, 'biases'):
            for key in aggregated_model.biases:
                aggregated_model.biases[key] = 0.0
        if hasattr(aggregated_model, 'temperature'):
            aggregated_model.temperature = 0.0

        # Perform weighted averaging
        for model, data_size in zip(client_models, client_data_sizes):
            weight = data_size / total_data_size
            if hasattr(aggregated_model, 'weights'):
                for key in aggregated_model.weights:
                    aggregated_model.weights[key] += model.weights.get(key, 0.0) * weight
            if hasattr(aggregated_model, 'biases'):
                for key in aggregated_model.biases:
                    aggregated_model.biases[key] += model.biases.get(key, 0.0) * weight
            if hasattr(aggregated_model, 'temperature'):
                aggregated_model.temperature += model.temperature * weight

        return aggregated_model
