import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Hamming Distance implementation.
    Computes the Hamming distance between two sets of vectors.
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        """
        Compute Hamming distance between x and y.

        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: Hamming 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 Hamming distance: count of different elements
        # Step 1: Compare elements (x != y gives boolean tensor)
        diff = (x != y)
        
        # Step 2: Convert to float and sum along feature dimension
        distance = torch.sum(diff.float(), dim=1)
        
        return distance

batch_size = 256
feature_dim = 512

def get_inputs():
    # Generate two sets of integer vectors (0 or 1 for simplicity)
    x = torch.randint(0, 2, (batch_size, feature_dim)).float()
    y = torch.randint(0, 2, (batch_size, feature_dim)).float()
    return [x, y]

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