from dataclasses import dataclass
import torch
import torch.nn as nn
from typing import List, Dict

@dataclass
class ExpertAllocation:
    expert_id: int
    load_factor: float
    specialization_score: float
    capacity_available: float

class ExpertRoutingSystem:
    def __init__(self, num_experts: int = 128):
        self.num_experts = num_experts
        self.experts = self._initialize_experts()
        self.router = TopologyAwareRouter()
        self.load_balancer = LoadBalancer()
        
    def allocate_experts(self, input_pattern: torch.Tensor) -> Dict[int, float]:
        task_requirements = self._analyze_task_requirements(input_pattern)
        available_experts = self._get_available_experts()
        return self._optimize_expert_allocation(task_requirements, available_experts)
        
    def _analyze_task_requirements(self, input_pattern: torch.Tensor) -> Dict[str, float]:
        complexity = self._estimate_task_complexity(input_pattern)
        specialization_needs = self._determine_specialization_needs(input_pattern)
        return {
            'complexity': complexity,
            'specialization': specialization_needs,
            'resource_requirements': self._estimate_resource_needs(complexity)
        }