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

class Model(nn.Module):
    """
    Performs a 3D transposed convolution operation with asymmetric input and square kernel.
    The input is padded before the convolution.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (int): Size of the square convolution kernel.
        stride (int, optional): Stride of the convolution. Defaults to 1.
        padding (int, optional): Padding applied to the input. Defaults to 0.
        groups (int, optional): Number of blocked connections from input channels to output channels. Defaults to 1.
        bias (bool, optional): If `True`, adds a learnable bias to the output. Defaults to `False`.
    """
    def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, padding: int = 0, output_padding: int = 0, groups: int = 1, bias: bool = False):
        super(Model, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.output_padding = output_padding
        self.groups = groups
        self.bias = bias

        # Initialize parameters
        self.weight = nn.Parameter(torch.Tensor(
            in_channels,
            out_channels // groups,
            kernel_size,
            kernel_size,
            kernel_size
        ))
        if bias:
            self.bias_param = nn.Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias_param', None)

        # Initialize weights
        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias_param is not None:
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / math.sqrt(fan_in)
            nn.init.uniform_(self.bias_param, -bound, bound)

    def forward(self, x: torch.Tensor, fn=F.conv_transpose3d) -> torch.Tensor:
        """
        Performs the 3D transposed convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, depth_out, height_out, width_out).
        """
        return fn(
            x,
            self.weight,
            self.bias_param,
            stride=self.stride,
            padding=self.padding,
            output_padding=self.output_padding,
            groups=self.groups
        )

# Test code
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = 3
depth = 16
height = 32
width = 32
stride = 2
padding = 3
groups = 4

def get_inputs():
    x = torch.randn(batch_size, in_channels, depth, height, width)
    return [x]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, groups]