code: def get_config_overrides(config_class, processors):
      # `Bark` configuration is too special. Let's just not handle this for now.
      if config_class.__name__ == "BarkConfig":
          return {}
  
      config_overrides = {}
  
      # Check if there is any tokenizer (prefer fast version if any)
      tokenizer = None
      for processor in processors:
          if isinstance(processor, PreTrainedTokenizerFast):
              tokenizer = processor
              break
          elif isinstance(processor, PreTrainedTokenizer):
              tokenizer = processor
  
      if tokenizer is None:
          return config_overrides
  
      # Get some properties of the (already converted) tokenizer (smaller vocab size, special token ids, etc.)
      # We use `len(tokenizer)` instead of `tokenizer.vocab_size` to avoid potential issues for tokenizers with non-empty
      # `added_tokens_encoder`. One example is the `DebertaV2Tokenizer` where the mask token is the extra token.
      vocab_size = len(tokenizer)
  
      # The original checkpoint has length `35998`, but it doesn't have ids `30400` and `30514` but instead `35998` and
      # `35999`.
      if config_class.__name__ == "GPTSanJapaneseConfig":
          vocab_size += 2
  
      config_overrides["vocab_size"] = vocab_size
  
      # Used to create a new model tester with `tokenizer.vocab_size` in order to get the (updated) special token ids.
      model_tester_kwargs = {"vocab_size": vocab_size}
      # `FSMTModelTester` accepts `src_vocab_size` and `tgt_vocab_size` but not `vocab_size`.
      if config_class.__name__ == "FSMTConfig":
          del model_tester_kwargs["vocab_size"]
          model_tester_kwargs[
              "src_vocab_size"
          ] = tokenizer.src_vocab_size
          model_tester_kwargs[
              "tgt_vocab_size"
          ] = tokenizer.tgt_vocab_size
  
      _tiny_config = get_tiny_config(
          config_class, **model_tester_kwargs
      )
  
      # handle the possibility of `text_config` inside `_tiny_config` for clip-like models (`owlvit`, `groupvit`, etc.)
      if hasattr(_tiny_config, "text_config"):
          _tiny_config = _tiny_config.text_config
  
      # Collect values of some special token ids
      for attr in dir(_tiny_config):
          if attr.endswith("_token_id"):
              token_id = getattr(_tiny_config, attr)
              if token_id is not None:
                  # Using the token id values from `tokenizer` instead of from `_tiny_config`.
                  token_id = get_token_id_from_tokenizer(
                      attr, tokenizer, original_token_id=token_id
                  )
                  config_overrides[attr] = token_id
  
      if config_class.__name__ == "FSMTConfig":
          config_overrides["src_vocab_size"] = tokenizer.src_vocab_size
          config_overrides["tgt_vocab_size"] = tokenizer.tgt_vocab_size
          # `FSMTConfig` has `DecoderConfig` as `decoder` attribute.
          config_overrides[
              "decoder"
          ] = configuration_fsmt.DecoderConfig(
              vocab_size=tokenizer.tgt_vocab_size,
              bos_token_id=config_overrides["eos_token_id"],
          )
  
      return config_overrides
  
imports: import argparse
  import collections.abc
  import copy
  import inspect
  import json
  import multiprocessing
  import os
  import shutil
  import tempfile
  import traceback
  from pathlib import Path
  from check_config_docstrings import get_checkpoint_from_config_class
  from datasets import load_dataset
  from get_test_info import get_model_to_tester_mapping, get_tester_classes_for_model
  from huggingface_hub import Repository, create_repo, hf_api, upload_folder
  from transformers import CONFIG_MAPPING, FEATURE_EXTRACTOR_MAPPING, IMAGE_PROCESSOR_MAPPING, PROCESSOR_MAPPING, TOKENIZER_MAPPING, AutoTokenizer, LayoutLMv3TokenizerFast, PreTrainedTokenizer, PreTrainedTokenizerFast, logging
  from transformers.feature_extraction_utils import FeatureExtractionMixin
  from transformers.file_utils import is_tf_available, is_torch_available
  from transformers.image_processing_utils import BaseImageProcessor
  from transformers.models.auto.configuration_auto import AutoConfig, model_type_to_module_name
  from transformers.models.fsmt import configuration_fsmt
  from transformers.processing_utils import ProcessorMixin, transformers_module
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
label_desc: Use image node_id 15 for calling a global function with example usage: get_config_overrides(config_class, processors) and returns: config_overrides, dict, config_overrides