import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    """
    Chebyshev + Sigmoid融合实现。
    先计算Chebyshev距离，然后应用Sigmoid激活函数进行加权。
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        """
        Compute Chebyshev + Sigmoid fusion.

        Args:
            x (torch.Tensor): First set of vectors [batch_size, feature_dim]
            y (torch.Tensor): Second set of vectors [batch_size, feature_dim]

        Returns:
            torch.Tensor: Sigmoid-weighted Chebyshev distances [batch_size]
        """
        # Input validation
        if x.shape != y.shape:
            raise ValueError(f"Input tensors must have the same shape, got {x.shape} and {y.shape}")
        
        if x.dim() != 2:
            raise ValueError(f"Input tensors must be 2D, got {x.dim()}D")
        
        # Compute Chebyshev distance: max(|x_i - y_i|)
        chebyshev_dist = torch.max(torch.abs(x - y), dim=1)[0]
        
        # Apply Sigmoid activation
        sigmoid_weights = torch.sigmoid(chebyshev_dist)
        
        # Compute weighted distances
        weighted_distances = sigmoid_weights * chebyshev_dist
        
        return weighted_distances

batch_size = 256
feature_dim = 512

def get_inputs():
    # Generate two sets of vectors
    x = torch.randn(batch_size, feature_dim)
    y = torch.randn(batch_size, feature_dim)
    return [x, y]

def get_init_inputs():
    return []  # No special initialization inputs needed
