code: def get_tokenizer_config(
      pretrained_model_name_or_path: Union[str, os.PathLike],
      cache_dir: Optional[Union[str, os.PathLike]] = None,
      force_download: bool = False,
      resume_download: bool = False,
      proxies: Optional[Dict[str, str]] = None,
      use_auth_token: Optional[Union[bool, str]] = None,
      revision: Optional[str] = None,
      local_files_only: bool = False,
      **kwargs,
  ):
      """
      Loads the tokenizer configuration from a pretrained model tokenizer configuration.
  
      Args:
          pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
              This can be either:
  
              - a string, the `model id` of a pretrained model configuration hosted inside a model repo on
                huggingface.co. Valid model ids can be located at the root-level, like ``bert-base-uncased``, or
                namespaced under a user or organization name, like ``dbmdz/bert-base-german-cased``.
              - a path to a `directory` containing a configuration file saved using the
                :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g., ``./my_model_directory/``.
  
          cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
              Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
              cache should not be used.
          force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether or not to force to (re-)download the configuration files and override the cached versions if they
              exist.
          resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
          proxies (:obj:`Dict[str, str]`, `optional`):
              A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
              'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
          use_auth_token (:obj:`str` or `bool`, `optional`):
              The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
              generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
          revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
              The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
              git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
              identifier allowed by git.
          local_files_only (:obj:`bool`, `optional`, defaults to :obj:`False`):
              If :obj:`True`, will only try to load the tokenizer configuration from local files.
  
      .. note::
  
          Passing :obj:`use_auth_token=True` is required when you want to use a private model.
  
  
      Returns:
          :obj:`Dict`: The configuration of the tokenizer.
  
      Examples::
  
          # Download configuration from huggingface.co and cache.
          tokenizer_config = get_tokenizer_config("bert-base-uncased")
          # This model does not have a tokenizer config so the result will be an empty dict.
          tokenizer_config = get_tokenizer_config("xlm-roberta-base")
  
          # Save a pretrained tokenizer locally and you can reload its config
          from transformers import AutoTokenizer
  
          tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
          tokenizer.save_pretrained("tokenizer-test")
          tokenizer_config = get_tokenizer_config("tokenizer-test")
      """
      if is_offline_mode() and not local_files_only:
          logger.info("Offline mode: forcing local_files_only=True")
          local_files_only = True
  
      pretrained_model_name_or_path = str(pretrained_model_name_or_path)
      if os.path.isdir(pretrained_model_name_or_path):
          config_file = os.path.join(
              pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE
          )
      else:
          config_file = hf_bucket_url(
              pretrained_model_name_or_path,
              filename=TOKENIZER_CONFIG_FILE,
              revision=revision,
              mirror=None,
          )
  
      try:
          # Load from URL or cache if already cached
          resolved_config_file = cached_path(
              config_file,
              cache_dir=cache_dir,
              force_download=force_download,
              proxies=proxies,
              resume_download=resume_download,
              local_files_only=local_files_only,
              use_auth_token=use_auth_token,
          )
  
      except EnvironmentError:
          logger.info(
              "Could not locate the tokenizer configuration file, will try to use the model config instead."
          )
          return {}
  
      with open(resolved_config_file, encoding="utf-8") as reader:
          return json.load(reader)
  
file_path: fastNLP/fastNLP/transformers/torch/models/auto/tokenization_auto.py
imports: import importlib
  import json
  import os
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
  from ...configuration_utils import PretrainedConfig
  from ...file_utils import cached_path, hf_bucket_url, is_offline_mode, is_sentencepiece_available, is_tokenizers_available
  from ...tokenization_utils_base import TOKENIZER_CONFIG_FILE
  from ..encoder_decoder import EncoderDecoderConfig
  from .auto_factory import _LazyAutoMapping
  from .configuration_auto import CONFIG_MAPPING_NAMES, AutoConfig, config_class_to_model_type, model_type_to_module_name, replace_list_option_in_docstrings
  from fastNLP.core.log import logger
label_desc: Use image node_id 2 for calling a global function with example usage: get_tokenizer_config(pretrained_model_name_or_path, cache_dir, force_download, resume_download, proxies, use_auth_token, revision, local_files_only) and returns: json, dict