import torch
import torch.nn as nn

N, C, H, W = 32, 64, 56, 56
EPS = 1e-8

class PearsonCorrelation(nn.Module):
    def __init__(self, eps=1e-8):
        super().__init__()
        self.eps = eps

    def forward(self, x, y):
        x_flat = x.view(x.size(0), -1)
        y_flat = y.view(y.size(0), -1)

        x_mean = x_flat.mean(dim=1, keepdim=True)
        y_mean = y_flat.mean(dim=1, keepdim=True)

        x_centered = x_flat - x_mean
        y_centered = y_flat - y_mean

        cov = (x_centered * y_centered).sum(dim=1)
        x_var = (x_centered ** 2).sum(dim=1)
        y_var = (y_centered ** 2).sum(dim=1)

        denom = torch.sqrt(x_var * y_var)
        return cov / (denom + self.eps)

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.op = PearsonCorrelation(EPS)

    def forward(self, x, y):
        return self.op(x, y)

def get_inputs():
    x = torch.randn(N, C, H, W, dtype=torch.float32)
    y = torch.randn(N, C, H, W, dtype=torch.float32)
    return [x, y]

def get_init_inputs():
    return []