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

class PatternAnalyzer:
    def __init__(self):
        pass

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)

    def _generate_activation_mask(self, importance_scores: torch.Tensor) -> torch.Tensor:
        # Create a binary mask based on importance scores and sparsity threshold
        return (importance_scores > self.sparsity_threshold).float()

    def _apply_sparse_activation(self, input_tensor: torch.Tensor, activation_mask: torch.Tensor) -> torch.Tensor:
        # Apply the activation mask to the input tensor
        return input_tensor * activation_mask

    def _calculate_attention_weights(self, input_tensor: torch.Tensor) -> torch.Tensor:
        # Calculate attention weights for the input tensor
        return torch.sigmoid(input_tensor)

    def _compute_gradient_information(self, input_tensor: torch.Tensor) -> torch.Tensor:
        # Compute gradient information for the input tensor
        return torch.abs(input_tensor)

    def _combine_importance_metrics(self, attention_weights: torch.Tensor,
                                   gradient_information: torch.Tensor) -> torch.Tensor:
        # Combine multiple importance metrics into a single score
        return attention_weights * gradient_information