import torch
import torch.nn as nn
from typing import Dict, Tuple, List
import numpy as np

class SparseActivationManager:
    def __init__(self, sparsity_threshold: float = 0.95):
        self.sparsity_threshold = sparsity_threshold
        self.activation_history = []
        self.pattern_analyzer = PatternAnalyzer()
        
    def compute_pattern(self, input_tensor: torch.Tensor) -> torch.Tensor:
        importance_scores = self._compute_importance_scores(input_tensor)
        activation_mask = self._generate_activation_mask(importance_scores)
        return self._apply_sparse_activation(input_tensor, activation_mask)
        
    def _compute_importance_scores(self, input_tensor: torch.Tensor) -> torch.Tensor:
        attention_weights = self._calculate_attention_weights(input_tensor)
        gradient_information = self._compute_gradient_information(input_tensor)
        return self._combine_importance_metrics(attention_weights, gradient_information)