import torch
import torch.nn as nn
from .Strategy import Strategy
from torch.utils.data import DataLoader

class LinearFiltration(Strategy):
    def __init__(self, target_class_idx: int):
        super().__init__()
        self.target_class_idx = target_class_idx

    def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module:
        model.eval()
        for param in model.parameters():
            param.requires_grad = False
            
        with torch.no_grad():
            W = model.fc.weight.data.clone()
            num_classes = W.shape[0]
            
            A = self._calculate_filtration_matrix(num_classes, self.target_class_idx, W.device)
            sanitized_W = torch.mm(A, W)
            model.fc.weight.copy_(sanitized_W)
            # Filter the bias (if the layer uses one)
            if model.fc.bias is not None:
                b = model.fc.bias.data.clone()
                # b is a 1D tensor of shape (num_classes), 
                # so we use torch.mv (matrix-vector multiplication) or unsqueeze it
                sanitized_b = torch.mv(A, b)
                model.fc.bias.copy_(sanitized_b)
            
        return model

    @staticmethod
    def _calculate_filtration_matrix(num_classes: int, forget_class: int, device: torch.device) -> torch.Tensor:
        A = torch.eye(num_classes, device=device)
        num_remaining = num_classes - 1
        
        # The row of the forgotten class should average all other classes
        for j in range(num_classes):
            if j == forget_class:
                # we zero the forget class
                A[forget_class, j] = 0.0
            else:
                # and we distribute the output to the remaining
                A[forget_class, j] = 1.0 / num_remaining
                
        return A