code: def replace_transformer_module(
      self, dst_state_dict: StateDict, src_state_dict: StateDict
  ):
      dst_prefix: str = "transformer_module"
      src_prefix: str = "sem_seg_head.predictor"
  
      def rename_keys_for_weight_bias(src_prefix: str, dst_prefix: str):
          return [
              (f"{src_prefix}.weight", f"{dst_prefix}.weight"),
              (f"{src_prefix}.bias", f"{dst_prefix}.bias"),
          ]
  
      def rename_keys_for_attn(src_prefix: str, dst_prefix: str):
          attn_keys = [
              (
                  f"{src_prefix}.in_proj_bias",
                  f"{dst_prefix}.in_proj_bias",
              ),
              (
                  f"{src_prefix}.in_proj_weight",
                  f"{dst_prefix}.in_proj_weight",
              ),
          ]
          attn_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.out_proj", f"{dst_prefix}.out_proj"
              )
          )
  
          return attn_keys
  
      def rename_keys_for_self_attn(src_prefix: str, dst_prefix: str):
          attn_keys = []
          attn_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.out_proj", f"{dst_prefix}.out_proj"
              )
          )
  
          return attn_keys
  
      def rename_keys_for_query_transformer_layer(
          src_prefix: str, dst_prefix: str
      ):
          query_transformer_layer_keys = []
  
          query_transformer_layer_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.linear1", f"{dst_prefix}.linear1"
              )
          )
          query_transformer_layer_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.linear2", f"{dst_prefix}.linear2"
              )
          )
          query_transformer_layer_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.norm1", f"{dst_prefix}.norm1"
              )
          )
          query_transformer_layer_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.norm2", f"{dst_prefix}.norm2"
              )
          )
          query_transformer_layer_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.norm3", f"{dst_prefix}.norm3"
              )
          )
  
          query_transformer_layer_keys.extend(
              rename_keys_for_attn(
                  f"{src_prefix}.self_attn", f"{dst_prefix}.self_attn"
              )
          )
  
          query_transformer_layer_keys.extend(
              rename_keys_for_attn(
                  f"{src_prefix}.multihead_attn",
                  f"{dst_prefix}.multihead_attn",
              )
          )
  
          return query_transformer_layer_keys
  
      def rename_keys_for_cross_attn_layer(
          src_prefix: str, dst_prefix: str
      ):
          cross_attn_layer_keys = []
  
          cross_attn_layer_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.norm", f"{dst_prefix}.norm"
              )
          )
          cross_attn_layer_keys.extend(
              rename_keys_for_attn(
                  f"{src_prefix}.multihead_attn",
                  f"{dst_prefix}.multihead_attn",
              )
          )
  
          return cross_attn_layer_keys
  
      def rename_keys_for_self_attn_layer(
          src_prefix: str, dst_prefix: str
      ):
          self_attn_layer_keys = []
  
          self_attn_layer_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.norm", f"{dst_prefix}.norm"
              )
          )
          self_attn_layer_keys.extend(
              rename_keys_for_self_attn(
                  f"{src_prefix}.self_attn", f"{dst_prefix}.self_attn"
              )
          )
  
          return self_attn_layer_keys
  
      def rename_keys_for_ffn_layer(src_prefix: str, dst_prefix: str):
          ffn_layer_keys = []
  
          ffn_layer_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.linear1", f"{dst_prefix}.linear1"
              )
          )
          ffn_layer_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.linear2", f"{dst_prefix}.linear2"
              )
          )
          ffn_layer_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.norm", f"{dst_prefix}.norm"
              )
          )
  
          return ffn_layer_keys
  
      def rename_keys_for_transformer_decoder_layer(
          src_prefix: str, dst_prefix: str, idx: int
      ):
          transformer_decoder_layer_keys = []
  
          transformer_decoder_layer_keys.extend(
              rename_keys_for_cross_attn_layer(
                  f"{src_prefix}.transformer_cross_attention_layers.{idx}",
                  f"{dst_prefix}.{idx}.cross_attn",
              )
          )
  
          transformer_decoder_layer_keys.extend(
              rename_keys_for_self_attn_layer(
                  f"{src_prefix}.transformer_self_attention_layers.{idx}",
                  f"{dst_prefix}.{idx}.self_attn",
              )
          )
  
          transformer_decoder_layer_keys.extend(
              rename_keys_for_ffn_layer(
                  f"{src_prefix}.transformer_ffn_layers.{idx}",
                  f"{dst_prefix}.{idx}.ffn",
              )
          )
  
          return transformer_decoder_layer_keys
  
      # positional embedding for object queries
      renamed_keys = [
          (
              f"{src_prefix}.query_embed.weight",
              f"{dst_prefix}.queries_embedder.weight",
          ),
          (
              f"{src_prefix}.level_embed.weight",
              f"{dst_prefix}.level_embed.weight",
          ),
      ]
  
      # norm
      renamed_keys.extend(
          rename_keys_for_weight_bias(
              f"{src_prefix}.decoder_norm",
              f"{dst_prefix}.decoder.decoder_norm",
          )
      )
  
      # proj
      renamed_keys.extend(
          rename_keys_for_weight_bias(
              f"{src_prefix}.class_input_proj",
              f"{dst_prefix}.decoder.query_input_projection",
          )
      )
  
      renamed_keys.extend(
          rename_keys_for_weight_bias(
              f"{src_prefix}.class_embed",
              f"{dst_prefix}.decoder.class_embed",
          )
      )
  
      for i in range(3):
          renamed_keys.extend(
              rename_keys_for_weight_bias(
                  f"{src_prefix}.mask_embed.layers.{i}",
                  f"{dst_prefix}.decoder.mask_embed.layers.{i}.0",
              )
          )
  
      # norm
      renamed_keys.extend(
          rename_keys_for_weight_bias(
              f"{src_prefix}.class_transformer.decoder.norm",
              f"{dst_prefix}.decoder.query_transformer.decoder.norm",
          )
      )
  
      # transformer to update queries with task tokens
      for i in range(self.config.query_dec_layers):
          renamed_keys.extend(
              rename_keys_for_query_transformer_layer(
                  f"{src_prefix}.class_transformer.decoder.layers.{i}",
                  f"{dst_prefix}.decoder.query_transformer.decoder.layers.{i}",
              )
          )
  
      # decoder layers
      for i in range(self.config.decoder_layers - 1):
          renamed_keys.extend(
              rename_keys_for_transformer_decoder_layer(
                  f"{src_prefix}",
                  f"{dst_prefix}.decoder.layers",
                  i,
              )
          )
  
      self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
      self.replace_keys_qkv_transformer_decoder(
          dst_state_dict, src_state_dict
      )
  
imports: import os
  import sys
  from argparse import ArgumentParser
  from dataclasses import dataclass
  from pathlib import Path
  from pprint import pformat
  from typing import Any, Dict, Iterator, List, Set, Tuple
  import requests
  import torch
  import torchvision.transforms
  from PIL import Image
  from torch import Tensor, nn
  from transformers import CLIPTokenizer, DinatConfig, SwinConfig
  from transformers.models.oneformer.image_processing_oneformer import OneFormerImageProcessor
  from transformers.models.oneformer.modeling_oneformer import OneFormerConfig, OneFormerForUniversalSegmentation, OneFormerForUniversalSegmentationOutput, OneFormerModel, OneFormerModelOutput
  from transformers.models.oneformer.processing_oneformer import OneFormerProcessor
  from transformers.utils import logging
label_desc: Use image node_id 7 for calling the OriginalOneFormerCheckpointToOursConverter obj's underlying member method code with example usage: obj.replace_transformer_module(dst_state_dict, src_state_dict) and returns: list, attn_keys, attn_keys, query_transformer_layer_keys, cross_attn_layer_keys, self_attn_layer_keys, ffn_layer_keys, transformer_decoder_layer_keys