text: import os
  import sys
  
  SRC_DIR = os.path.join(os.path.dirname(__file__), "src")
  sys.path.append(SRC_DIR)
  
  
  from transformers import (
      AutoConfig,
      AutoModel,
      AutoModelForQuestionAnswering,
      AutoModelForSequenceClassification,
      AutoModelWithLMHead,
      AutoTokenizer,
      add_start_docstrings,
  )
  
  
  dependencies = ["torch", "numpy", "tokenizers", "filelock", "requests", "tqdm", "regex", "sentencepiece", "sacremoses"]
  
  
  @add_start_docstrings(AutoConfig.__doc__)
  def config(*args, **kwargs):
      r"""
                  # Using torch.hub !
                  import torch
  
                  config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased')  # Download configuration from huggingface.co and cache.
                  config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
                  config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/my_configuration.json')
                  config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attentions=True, foo=False)
                  assert config.output_attentions == True
                  config, unused_kwargs = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attentions=True, foo=False, return_unused_kwargs=True)
                  assert config.output_attentions == True
                  assert unused_kwargs == {'foo': False}
  
              """
  
      return AutoConfig.from_pretrained(*args, **kwargs)
  
  
  @add_start_docstrings(AutoTokenizer.__doc__)
  def tokenizer(*args, **kwargs):
      r"""
          # Using torch.hub !
          import torch
  
          tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'bert-base-uncased')    # Download vocabulary from huggingface.co and cache.
          tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', './test/bert_saved_model/')  # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
  
      """
  
      return AutoTokenizer.from_pretrained(*args, **kwargs)
  
  
  @add_start_docstrings(AutoModel.__doc__)
  def model(*args, **kwargs):
      r"""
              # Using torch.hub !
              import torch
  
              model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
              model = torch.hub.load('huggingface/transformers', 'model', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
              model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
              assert model.config.output_attentions == True
              # Loading from a TF checkpoint file instead of a PyTorch model (slower)
              config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
              model = torch.hub.load('huggingface/transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  
          """
  
      return AutoModel.from_pretrained(*args, **kwargs)
  
  
  @add_start_docstrings(AutoModelWithLMHead.__doc__)
  def modelWithLMHead(*args, **kwargs):
      r"""
          # Using torch.hub !
          import torch
  
          model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
          model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
          model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
          assert model.config.output_attentions == True
          # Loading from a TF checkpoint file instead of a PyTorch model (slower)
          config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
          model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  
      """
      return AutoModelWithLMHead.from_pretrained(*args, **kwargs)
  
  
  @add_start_docstrings(AutoModelForSequenceClassification.__doc__)
  def modelForSequenceClassification(*args, **kwargs):
      r"""
              # Using torch.hub !
              import torch
  
              model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
              model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
              model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
              assert model.config.output_attentions == True
              # Loading from a TF checkpoint file instead of a PyTorch model (slower)
              config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
              model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  
          """
  
      return AutoModelForSequenceClassification.from_pretrained(*args, **kwargs)
  
  
  @add_start_docstrings(AutoModelForQuestionAnswering.__doc__)
  def modelForQuestionAnswering(*args, **kwargs):
      r"""
          # Using torch.hub !
          import torch
  
          model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
          model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
          model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
          assert model.config.output_attentions == True
          # Loading from a TF checkpoint file instead of a PyTorch model (slower)
          config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
          model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  
      """
      return AutoModelForQuestionAnswering.from_pretrained(*args, **kwargs)
  