def window_unpartition(
        self, windows: torch.Tensor, window_size: int, padding_shape: Tuple[int, int], original_shape: Tuple[int, int]
    ) -> torch.Tensor:
        """
        Args:
        Window unpartition into original sequences and removing padding.
            hidden_states (tensor):
                input tokens with [batch_size * num_windows, window_size, window_size, channel].
            window_size (int):
                window size.
            padding_shape (Tuple):
                padded height and width (pad_height, pad_width).
            original_shape (Tuple): original height and width (height, width) before padding.

        Returns:
            hidden_states: unpartitioned sequences with [batch_size, height, width, channel].
        """
        pad_height, pad_width = padding_shape
        height, width = original_shape
        batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size)
        hidden_states = windows.reshape(
            batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1
        )
        hidden_states = (
            hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(batch_size, pad_height, pad_width, -1)
        )

        hidden_states = hidden_states[:, :height, :width, :].contiguous()
        return hidden_states

        Args:
        Window unpartition into original sequences and removing padding.
            hidden_states (tensor):
                input tokens with [batch_size * num_windows, window_size, window_size, channel].
            window_size (int):
                window size.
            padding_shape (Tuple):
                padded height and width (pad_height, pad_width).
            original_shape (Tuple): original height and width (height, width) before padding.

        Returns:
            hidden_states: unpartitioned sequences with [batch_size, height, width, channel].
        
dpkd/transformers/src/transformers/models/sam/modeling_sam.py