code: def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
      """Checks whether the casing config is consistent with the checkpoint name."""
  
      # The casing has to be passed in by the user and there is no explicit check
      # as to whether it matches the checkpoint. The casing information probably
      # should have been stored in the bert_config.json file, but it's not, so
      # we have to heuristically detect it to validate.
  
      if not init_checkpoint:
          return
  
      m = re.match(
          "^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint
      )
      if m is None:
          return
  
      model_name = m.group(1)
  
      lower_models = [
          "uncased_L-24_H-1024_A-16",
          "uncased_L-12_H-768_A-12",
          "multilingual_L-12_H-768_A-12",
          "chinese_L-12_H-768_A-12",
      ]
  
      cased_models = [
          "cased_L-12_H-768_A-12",
          "cased_L-24_H-1024_A-16",
          "multi_cased_L-12_H-768_A-12",
      ]
  
      is_bad_config = False
      if model_name in lower_models and not do_lower_case:
          is_bad_config = True
          actual_flag = "False"
          case_name = "lowercased"
          opposite_flag = "True"
  
      if model_name in cased_models and do_lower_case:
          is_bad_config = True
          actual_flag = "True"
          case_name = "cased"
          opposite_flag = "False"
  
      if is_bad_config:
          raise ValueError(
              "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
              "However, `%s` seems to be a %s model, so you "
              "should pass in `--do_lower_case=%s` so that the fine-tuning matches "
              "how the model was pre-training. If this error is wrong, please "
              "just comment out this check."
              % (
                  actual_flag,
                  init_checkpoint,
                  model_name,
                  case_name,
                  opposite_flag,
              )
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import re
  import unicodedata
  from absl import flags
  import six
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling a global function with example usage: validate_case_matches_checkpoint(do_lower_case, init_checkpoint) without return types