import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import deque
from typing import Deque, Dict, Any

class CognitiveMemory(nn.Module):
    """Memory system dengan manajemen dimensi yang ketat"""
    def __init__(self, context_size: int, capacity: int = 100):
        super().__init__()
        self.context_size = context_size
        self.capacity = capacity
        self.memory_queue: Deque[Dict[str, Any]] = deque(maxlen=capacity)
        
        # Proyeksi linear dengan dimensi input/output sama
        self.key_proj = nn.Linear(context_size, context_size)
        self.value_proj = nn.Linear(context_size, context_size)
        self.importance_decay = nn.Parameter(torch.tensor(0.95))

    def add_memory(self, context: torch.Tensor, activation: float):
        """Menyimpan memori dengan dimensi terkontrol"""
        importance = torch.sigmoid(torch.tensor(activation * 0.5 + 0.2))
        self.memory_queue.append({
            'context': context.detach().clone().squeeze(),
            'importance': importance,
            'age': torch.tensor(0.0)
        })

    def consolidate_memories(self):
        """Konsolidasi memori dengan validasi dimensi"""
        self.memory_queue = deque(
            [m for m in self.memory_queue if m['importance'] > 0.2],
            maxlen=self.capacity
        )

    def retrieve(self, query: torch.Tensor) -> torch.Tensor:
        """Retrieval dengan penanganan tensor 1D"""
        if not self.memory_queue:
            return torch.zeros(self.context_size, device=query.device)
            
        # Penanganan dimensi yang konsisten
        contexts = torch.stack([m['context'] for m in self.memory_queue])
        keys = self.key_proj(contexts)
        values = self.value_proj(contexts)
        query_proj = self.key_proj(query.squeeze())
        
        scores = F.softmax(keys @ query_proj, dim=0)
        return (scores.unsqueeze(1) * values).sum(dim=0)