code: def get_tiny_config(
      config_class, model_class=None, **model_tester_kwargs
  ):
      """Retrieve a tiny configuration from `config_class` using each model's `ModelTester`.
  
      Args:
          config_class: Subclass of `PreTrainedConfig`.
  
      Returns:
          An instance of `config_class` with tiny hyperparameters
      """
      model_type = config_class.model_type
  
      # For model type like `data2vec-vision` and `donut-swin`, we can't get the config/model file name directly via
      # `model_type` as it would be sth. like `configuration_data2vec_vision.py`.
      # A simple way is to use `inspect.getsourcefile(config_class)`.
      config_source_file = inspect.getsourcefile(config_class)
      # The modeling file name without prefix (`modeling_`) and postfix (`.py`)
      modeling_name = (
          config_source_file.split(os.path.sep)[-1]
          .replace("configuration_", "")
          .replace(".py", "")
      )
  
      try:
          print("Importing", model_type_to_module_name(model_type))
          module_name = model_type_to_module_name(model_type)
          if not modeling_name.startswith(module_name):
              raise ValueError(
                  f"{modeling_name} doesn't start with {module_name}!"
              )
          test_file = os.path.join(
              "tests",
              "models",
              module_name,
              f"test_modeling_{modeling_name}.py",
          )
          models_to_model_testers = get_model_to_tester_mapping(
              test_file
          )
          # Find the model tester class
          model_tester_class = None
          tester_classes = []
          if model_class is not None:
              tester_classes = get_tester_classes_for_model(
                  test_file, model_class
              )
          else:
              for _tester_classes in models_to_model_testers.values():
                  tester_classes.extend(_tester_classes)
          if len(tester_classes) > 0:
              # sort with the length of the class names first, then the alphabetical order
              # This is to avoid `T5EncoderOnlyModelTest` is used instead of `T5ModelTest`, which has
              # `is_encoder_decoder=False` and causes some pipeline tests failing (also failures in `Optimum` CI).
              # TODO: More fine grained control of the desired tester class.
              model_tester_class = sorted(
                  tester_classes,
                  key=lambda x: (len(x.__name__), x.__name__),
              )[0]
      except ModuleNotFoundError:
          error = f"Tiny config not created for {model_type} - cannot find the testing module from the model name."
          raise ValueError(error)
  
      if model_tester_class is None:
          error = f"Tiny config not created for {model_type} - no model tester is found in the testing module."
          raise ValueError(error)
  
      # CLIP-like models have `text_model_tester` and `vision_model_tester`, and we need to pass `vocab_size` to
      # `text_model_tester` via `text_kwargs`. The same trick is also necessary for `Flava`.
  
      if "vocab_size" in model_tester_kwargs:
          if (
              "text_kwargs"
              in inspect.signature(
                  model_tester_class.__init__
              ).parameters.keys()
          ):
              vocab_size = model_tester_kwargs.pop("vocab_size")
              model_tester_kwargs["text_kwargs"] = {
                  "vocab_size": vocab_size
              }
  
      # `parent` is an instance of `unittest.TestCase`, but we don't need it here.
      model_tester = model_tester_class(
          parent=None, **model_tester_kwargs
      )
  
      if hasattr(model_tester, "get_pipeline_config"):
          config = model_tester.get_pipeline_config()
      elif hasattr(model_tester, "prepare_config_and_inputs"):
          # `PoolFormer` has no `get_config` defined. Furthermore, it's better to use `prepare_config_and_inputs` even if
          # `get_config` is defined, since there might be some extra changes in `prepare_config_and_inputs`.
          config = model_tester.prepare_config_and_inputs()[0]
      elif hasattr(model_tester, "get_config"):
          config = model_tester.get_config()
      else:
          error = (
              f"Tiny config not created for {model_type} - the model tester {model_tester_class.__name__} lacks"
              " necessary method to create config."
          )
          raise ValueError(error)
  
      # make sure this is long enough (some model tester has `20` for this attr.) to pass `text-generation`
      # pipeline tests.
      max_positions = []
      for key in [
          "max_position_embeddings",
          "max_source_positions",
          "max_target_positions",
      ]:
          if getattr(config, key, 0) > 0:
              max_positions.append(getattr(config, key))
          if getattr(config, "text_config", None) is not None:
              if getattr(config.text_config, key, None) is not None:
                  max_positions.append(getattr(config.text_config, key))
      if len(max_positions) > 0:
          max_position = max(200, min(max_positions))
          for key in [
              "max_position_embeddings",
              "max_source_positions",
              "max_target_positions",
          ]:
              if getattr(config, key, 0) > 0:
                  setattr(config, key, max_position)
              if getattr(config, "text_config", None) is not None:
                  if getattr(config.text_config, key, None) is not None:
                      setattr(config.text_config, key, max_position)
  
      return config
  
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 5 for calling a global function with example usage: get_tiny_config(config_class, model_class) and returns: config