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 routed_output  # Removed undefined method call

    def _process_consciousness_emergence(self, hidden_states):
        # Adding the missing method
        return hidden_states  # Simple implementation, modify as needed

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)
        ])

    def forward(self, hidden_states):
        # Adding the missing forward method
        batch_size, seq_len, hidden_size = hidden_states.shape

        # Calculate routing probabilities
        routing_logits = self.gate(hidden_states.view(-1, hidden_size))
        routing_probs = torch.softmax(routing_logits, dim=-1)

        # Get top-k experts
        k = 2  # Using top-2 experts
        top_k_probs, top_k_indices = torch.topk(routing_probs, k, dim=-1)
        top_k_probs = top_k_probs / top_k_probs.sum(dim=-1, keepdim=True)  # Normalize

        # Reshape for easier processing
        hidden_states_flat = hidden_states.view(-1, 1, hidden_size)

        # Initialize output
        final_output = torch.zeros_like(hidden_states_flat)

        # Route to experts
        for i, expert in enumerate(self.experts):
            # Create mask for this expert
            mask = (top_k_indices == i).any(dim=-1).unsqueeze(-1)
            if mask.sum() > 0:
                # Only process tokens routed to this expert
                expert_input = hidden_states_flat[mask.squeeze(-1)]
                # Apply expert
                expert_output = expert(expert_input)
                # Weight by routing probability
                weight_mask = (top_k_indices == i).float() * top_k_probs
                weight_mask = weight_mask.unsqueeze(-1)
                final_output[mask.squeeze(-1)] += expert_output * weight_mask[mask.squeeze(-1)]

        return final_output.view(batch_size, seq_len, hidden_size)