code: def main():
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
  
      parser = HfArgumentParser(
          (
              ModelArguments,
              DataTrainingArguments,
              TrainingArguments,
              AdapterArguments,
          )
      )
  
      if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          (
              model_args,
              data_args,
              training_args,
              adapter_args,
          ) = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1])
          )
      else:
          (
              model_args,
              data_args,
              training_args,
              adapter_args,
          ) = parser.parse_args_into_dataclasses()
  
      if (
          os.path.exists(training_args.output_dir)
          and os.listdir(training_args.output_dir)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          raise ValueError(
              f"Output directory ({training_args.output_dir}) already exists and is not empty."
              " Use --overwrite_output_dir to overcome."
          )
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO
          if training_args.local_rank in [-1, 0]
          else logging.WARN,
      )
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
          training_args.local_rank,
          training_args.device,
          training_args.n_gpu,
          bool(training_args.local_rank != -1),
          training_args.fp16,
      )
      logger.info("Training/evaluation parameters %s", training_args)
  
      # Set seed
      set_seed(training_args.seed)
  
      try:
          num_labels = glue_tasks_num_labels[data_args.task_name]
          output_mode = glue_output_modes[data_args.task_name]
      except KeyError:
          raise ValueError("Task not found: %s" % (data_args.task_name))
  
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
  
      config = AutoConfig.from_pretrained(
          model_args.config_name
          if model_args.config_name
          else model_args.model_name_or_path,
          num_labels=num_labels,
          finetuning_task=data_args.task_name,
          cache_dir=model_args.cache_dir,
      )
      tokenizer = AutoTokenizer.from_pretrained(
          model_args.tokenizer_name
          if model_args.tokenizer_name
          else model_args.model_name_or_path,
          cache_dir=model_args.cache_dir,
      )
      model = AutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path,
          from_tf=bool(".ckpt" in model_args.model_name_or_path),
          config=config,
          cache_dir=model_args.cache_dir,
      )
  
      # Convert the model into an adapter model
      adapters.init(model)
  
      # ~~~~~ Here comes the interesting part of setting up AdapterFusion training ~~~~~
  
      from adapters.configuration import SeqBnConfig
  
      # First, load the pre-trained adapters we want to fuse from Hub
      model.load_adapter(
          "sentiment/sst-2@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "nli/multinli@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "nli/rte@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "sts/mrpc@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "sts/qqp@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "comsense/cosmosqa@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "comsense/csqa@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "comsense/hellaswag@ukp",
          config=SeqBnConfig(),
          with_head=False,
      )
      model.load_adapter(
          "comsense/siqa@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "comsense/winogrande@ukp",
          config=SeqBnConfig(),
          with_head=False,
      )
      model.load_adapter(
          "nli/cb@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "nli/sick@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "nli/scitail@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "qa/boolq@ukp", config=SeqBnConfig(), with_head=False
      )
      model.load_adapter(
          "sentiment/imdb@ukp", config=SeqBnConfig(), with_head=False
      )
  
      adapter_setup = [
          [
              "sst-2",
              "mnli",
              "rte",
              "mrpc",
              "qqp",
              "cosmosqa",
              "csqa",
              "hellaswag",
              "socialiqa",
              "winogrande",
              "cb",
              "sick",
              "scitail",
              "boolq",
              "imdb",
          ]
      ]
  
      # Add a fusion layer and tell the model to train fusion
      model.add_adapter_fusion(adapter_setup[0], "dynamic")
      model.train_adapter_fusion(adapter_setup)
  
      # ~~~~~ Rest is again same as in standard training setup ~~~~~
  
      # Get datasets
      train_dataset = (
          GlueDataset(data_args, tokenizer=tokenizer)
          if training_args.do_train
          else None
      )
      eval_dataset = (
          GlueDataset(data_args, tokenizer=tokenizer, mode="dev")
          if training_args.do_eval
          else None
      )
      test_dataset = (
          GlueDataset(data_args, tokenizer=tokenizer, mode="test")
          if training_args.do_predict
          else None
      )
  
      def compute_metrics(p: EvalPrediction) -> Dict:
          if output_mode == "classification":
              preds = np.argmax(p.predictions, axis=1)
          elif output_mode == "regression":
              preds = np.squeeze(p.predictions)
          return glue_compute_metrics(
              data_args.task_name, preds, p.label_ids
          )
  
      trainer = AdapterTrainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset,
          eval_dataset=eval_dataset,
          compute_metrics=compute_metrics,
      )
  
      # Training
      if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path
              if os.path.isdir(model_args.model_name_or_path)
              else None
          )
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_process_zero():
              tokenizer.save_pretrained(training_args.output_dir)
  
      # Evaluation
      eval_results = {}
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
  
          # Loop to handle MNLI double evaluation (matched, mis-matched)
          eval_datasets = [eval_dataset]
          if data_args.task_name == "mnli":
              mnli_mm_data_args = dataclasses.replace(
                  data_args, task_name="mnli-mm"
              )
              eval_datasets.append(
                  GlueDataset(
                      mnli_mm_data_args, tokenizer=tokenizer, mode="dev"
                  )
              )
  
          for eval_dataset in eval_datasets:
              eval_result = trainer.evaluate(eval_dataset=eval_dataset)
  
              output_eval_file = os.path.join(
                  training_args.output_dir,
                  f"eval_results_{eval_dataset.args.task_name}.txt",
              )
              if trainer.is_world_process_zero():
                  with open(output_eval_file, "w") as writer:
                      logger.info(
                          "***** Eval results {} *****".format(
                              eval_dataset.args.task_name
                          )
                      )
                      for key, value in eval_result.items():
                          logger.info("  %s = %s", key, value)
                          writer.write("%s = %s\n" % (key, value))
  
              eval_results.update(eval_result)
  
      if training_args.do_predict:
          logging.info("*** Test ***")
          test_datasets = [test_dataset]
          if data_args.task_name == "mnli":
              mnli_mm_data_args = dataclasses.replace(
                  data_args, task_name="mnli-mm"
              )
              test_datasets.append(
                  GlueDataset(
                      mnli_mm_data_args,
                      tokenizer=tokenizer,
                      mode="test",
                  )
              )
  
          for test_dataset in test_datasets:
              predictions = trainer.predict(
                  test_dataset=test_dataset
              ).predictions
              if output_mode == "classification":
                  predictions = np.argmax(predictions, axis=1)
  
              output_test_file = os.path.join(
                  training_args.output_dir,
                  f"test_results_{test_dataset.args.task_name}.txt",
              )
              if trainer.is_world_process_zero():
                  with open(output_test_file, "w") as writer:
                      logger.info(
                          "***** Test results {} *****".format(
                              test_dataset.args.task_name
                          )
                      )
                      writer.write("index\tprediction\n")
                      for index, item in enumerate(predictions):
                          if output_mode == "regression":
                              writer.write(
                                  "%d\t%3.3f\n" % (index, item)
                              )
                          else:
                              item = test_dataset.get_labels()[item]
                              writer.write("%d\t%s\n" % (index, item))
      return eval_results
  
file_path: adapters/examples/pytorch/adapterfusion/run_fusion_glue.py
imports: import dataclasses
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Dict, Optional
  import numpy
  import adapters
  from adapters import AdapterArguments, AdapterTrainer
  from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, EvalPrediction, GlueDataset
  from transformers import GlueDataTrainingArguments
  from transformers import HfArgumentParser, TrainingArguments, glue_compute_metrics, glue_output_modes, glue_tasks_num_labels, set_seed
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: eval_results, glue_compute_metrics