import torch
import torch.nn as nn
from transformers import AutoModel, AutoConfig

class FoundationLayer(nn.Module):
    def __init__(self, model_name: str = "gpt2-xl"):
        super().__init__()
        self.config = AutoConfig.from_pretrained(model_name)
        self.transformer = AutoModel.from_pretrained(model_name)
        self.sparse_router = MixtureOfExperts(
            num_experts=128,
            input_size=self.config.hidden_size
        )
        
    def forward(self, input_ids, attention_mask=None):
        transformer_output = self.transformer(
            input_ids=input_ids,
            attention_mask=attention_mask
        )
        
        routed_output = self.sparse_router(transformer_output.last_hidden_state)
        return self._process_consciousness_emergence(routed_output)

class MixtureOfExperts(nn.Module):
    def __init__(self, num_experts: int, input_size: int):
        super().__init__()
        self.num_experts = num_experts
        self.gate = nn.Linear(input_size, num_experts)
        self.experts = nn.ModuleList([
            nn.TransformerEncoderLayer(
                d_model=input_size,
                nhead=8
            ) for _ in range(num_experts)
        ])