code: def replace_model_patterns(
      text: str,
      old_model_patterns: ModelPatterns,
      new_model_patterns: ModelPatterns,
  ) -> Tuple[str, str]:
      """
      Replace all patterns present in a given text.
  
      Args:
          text (`str`): The text to treat.
          old_model_patterns (`ModelPatterns`): The patterns for the old model.
          new_model_patterns (`ModelPatterns`): The patterns for the new model.
  
      Returns:
          `Tuple(str, str)`: A tuple of with the treated text and the replacement actually done in it.
      """
      # The order is crucially important as we will check and replace in that order. For instance the config probably
      # contains the camel-cased named, but will be treated before.
      attributes_to_check = ["config_class"]
      # Add relevant preprocessing classes
      for attr in [
          "tokenizer_class",
          "feature_extractor_class",
          "processor_class",
      ]:
          if (
              getattr(old_model_patterns, attr) is not None
              and getattr(new_model_patterns, attr) is not None
          ):
              attributes_to_check.append(attr)
  
      # Special cases for checkpoint and model_type
      if old_model_patterns.checkpoint not in [
          old_model_patterns.model_type,
          old_model_patterns.model_lower_cased,
      ]:
          attributes_to_check.append("checkpoint")
      if (
          old_model_patterns.model_type
          != old_model_patterns.model_lower_cased
      ):
          attributes_to_check.append("model_type")
      else:
          text = re.sub(
              rf'(\s*)model_type = "{old_model_patterns.model_type}"',
              r'\1model_type = "[MODEL_TYPE]"',
              text,
          )
  
      # Special case when the model camel cased and upper cased names are the same for the old model (like for GPT2) but
      # not the new one. We can't just do a replace in all the text and will need a special regex
      if (
          old_model_patterns.model_upper_cased
          == old_model_patterns.model_camel_cased
      ):
          old_model_value = old_model_patterns.model_upper_cased
          if (
              re.search(rf"{old_model_value}_[A-Z_]*[^A-Z_]", text)
              is not None
          ):
              text = re.sub(
                  rf"{old_model_value}([A-Z_]*)([^a-zA-Z_])",
                  r"[MODEL_UPPER_CASED]\1\2",
                  text,
              )
      else:
          attributes_to_check.append("model_upper_cased")
  
      attributes_to_check.extend(
          ["model_camel_cased", "model_lower_cased", "model_name"]
      )
  
      # Now let's replace every other attribute by their placeholder
      for attr in attributes_to_check:
          text = text.replace(
              getattr(old_model_patterns, attr),
              ATTRIBUTE_TO_PLACEHOLDER[attr],
          )
  
      # Finally we can replace the placeholder byt the new values.
      replacements = []
      for attr, placeholder in ATTRIBUTE_TO_PLACEHOLDER.items():
          if placeholder in text:
              replacements.append(
                  (
                      getattr(old_model_patterns, attr),
                      getattr(new_model_patterns, attr),
                  )
              )
              text = text.replace(
                  placeholder, getattr(new_model_patterns, attr)
              )
  
      # If we have two inconsistent replacements, we don't return anything (ex: GPT2->GPT_NEW and GPT2->GPTNew)
      old_replacement_values = [old for old, new in replacements]
      if len(set(old_replacement_values)) != len(
          old_replacement_values
      ):
          return text, ""
  
      replacements = simplify_replacements(replacements)
      replacements = [f"{old}->{new}" for old, new in replacements]
      return text, ",".join(replacements)
  
imports: import difflib
  import json
  import os
  import re
  from argparse import ArgumentParser, Namespace
  from dataclasses import dataclass
  from datetime import date
  from itertools import chain
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Pattern, Tuple, Union
  import transformers.models.auto
  from transformers.models.auto.configuration_auto import model_type_to_module_name
  from ..utils import is_flax_available, is_tf_available, is_torch_available, logging
  from .None import BaseTransformersCLICommand
label_desc: Use image node_id 6 for calling a global function with example usage: replace_model_patterns(text, old_model_patterns, new_model_patterns) and returns: text,  str, text,  str