import torch.nn as nn

def _conv_forward_asymmetric(self, input, weight, bias):
    """
    Patch for Conv2d._conv_forward that supports asymmetric padding
    """
    working = nn.functional.pad(input, self.asymmetric_padding['x'], mode=self.asymmetric_padding_mode['x'])
    working = nn.functional.pad(working, self.asymmetric_padding['y'], mode=self.asymmetric_padding_mode['y'])
    return nn.functional.conv2d(working, weight, bias, self.stride, nn.modules.utils._pair(0), self.dilation, self.groups)

def configure_model_padding(model, seamless, seamless_axes):
    """
    Modifies the 2D convolution layers to use a circular padding mode based on the `seamless` and `seamless_axes` options.
    """
    for m in model.modules():
        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
            if seamless:
                m.asymmetric_padding_mode = {}
                m.asymmetric_padding = {}
                m.asymmetric_padding_mode['x'] = 'circular' if ('x' in seamless_axes) else 'constant'
                m.asymmetric_padding['x'] = (m._reversed_padding_repeated_twice[0], m._reversed_padding_repeated_twice[1], 0, 0)
                m.asymmetric_padding_mode['y'] = 'circular' if ('y' in seamless_axes) else 'constant'
                m.asymmetric_padding['y'] = (0, 0, m._reversed_padding_repeated_twice[2], m._reversed_padding_repeated_twice[3])
                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
            else:
                m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
                if hasattr(m, 'asymmetric_padding_mode'):
                    del m.asymmetric_padding_mode
                if hasattr(m, 'asymmetric_padding'):
                    del m.asymmetric_padding
