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()
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          handlers=[logging.StreamHandler(sys.stdout)],
      )
      log_level = training_args.get_process_log_level()
      logger.setLevel(log_level)
      datasets.utils.logging.set_verbosity(log_level)
      transformers.utils.logging.set_verbosity(log_level)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
  
      # Log on each process the small summary:
      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
          + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
      )
      logger.info(f"Training/evaluation parameters {training_args}")
  
      # Detecting last checkpoint.
      last_checkpoint = None
      if (
          os.path.isdir(training_args.output_dir)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          last_checkpoint = get_last_checkpoint(
              training_args.output_dir
          )
          if (
              last_checkpoint is None
              and len(os.listdir(training_args.output_dir)) > 0
          ):
              raise ValueError(
                  f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                  "Use --overwrite_output_dir to overcome."
              )
          elif (
              last_checkpoint is not None
              and training_args.resume_from_checkpoint is None
          ):
              logger.info(
                  f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                  "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
              )
  
      # Set seed before initializing model.
      set_seed(training_args.seed)
  
      # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
      # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
      # (the dataset will be downloaded automatically from the datasets Hub).
  
      # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
      # 'text' is found. You can easily tweak this behavior (see below).
  
      # In distributed training, the load_dataset function guarantee that only one local process can concurrently
      # download the dataset.
      if (
          data_args.train_file is not None
          or data_args.validation_file is not None
      ):
          data_files = {}
          if data_args.train_file is not None:
              data_files["train"] = data_args.train_file
          if data_args.validation_file is not None:
              data_files["validation"] = data_args.validation_file
          extension = data_args.train_file.split(".")[-1]
          raw_datasets = load_dataset(
              extension,
              data_files=data_files,
              cache_dir=model_args.cache_dir,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
      else:
          # Downloading and loading the swag dataset from the hub.
          raw_datasets = load_dataset(
              "swag",
              "regular",
              cache_dir=model_args.cache_dir,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
      # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
      # https://huggingface.co/docs/datasets/loading_datasets.html.
  
      # 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,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
      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,
          use_fast=model_args.use_fast_tokenizer,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
      model = AutoModelForMultipleChoice.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,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
  
      # Convert the model into an adapter model
      adapters.init(model)
  
      # When using your own dataset or a different dataset from swag, you will probably need to change this.
      ending_names = [f"ending{i}" for i in range(4)]
      context_name = "sent1"
      question_header_name = "sent2"
  
      if data_args.max_seq_length is None:
          max_seq_length = tokenizer.model_max_length
          if max_seq_length > 1024:
              logger.warning(
                  f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
                  "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
              )
              max_seq_length = 1024
      else:
          if data_args.max_seq_length > tokenizer.model_max_length:
              logger.warning(
                  f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
                  f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
              )
          max_seq_length = min(
              data_args.max_seq_length, tokenizer.model_max_length
          )
  
      # Preprocessing the datasets.
      def preprocess_function(examples):
          first_sentences = [
              [context] * 4 for context in examples[context_name]
          ]
          question_headers = examples[question_header_name]
          second_sentences = [
              [f"{header} {examples[end][i]}" for end in ending_names]
              for i, header in enumerate(question_headers)
          ]
  
          # Flatten out
          first_sentences = list(chain(*first_sentences))
          second_sentences = list(chain(*second_sentences))
  
          # Tokenize
          tokenized_examples = tokenizer(
              first_sentences,
              second_sentences,
              truncation=True,
              max_length=max_seq_length,
              padding="max_length"
              if data_args.pad_to_max_length
              else False,
          )
          # Un-flatten
          return {
              k: [v[i : i + 4] for i in range(0, len(v), 4)]
              for k, v in tokenized_examples.items()
          }
  
      if training_args.do_train:
          if "train" not in raw_datasets:
              raise ValueError("--do_train requires a train dataset")
          train_dataset = raw_datasets["train"]
          if data_args.max_train_samples is not None:
              max_train_samples = min(
                  len(train_dataset), data_args.max_train_samples
              )
              train_dataset = train_dataset.select(
                  range(max_train_samples)
              )
          with training_args.main_process_first(
              desc="train dataset map pre-processing"
          ):
              train_dataset = train_dataset.map(
                  preprocess_function,
                  batched=True,
                  num_proc=data_args.preprocessing_num_workers,
                  load_from_cache_file=not data_args.overwrite_cache,
              )
  
      if training_args.do_eval:
          if "validation" not in raw_datasets:
              raise ValueError(
                  "--do_eval requires a validation dataset"
              )
          eval_dataset = raw_datasets["validation"]
          if data_args.max_eval_samples is not None:
              max_eval_samples = min(
                  len(eval_dataset), data_args.max_eval_samples
              )
              eval_dataset = eval_dataset.select(
                  range(max_eval_samples)
              )
          with training_args.main_process_first(
              desc="validation dataset map pre-processing"
          ):
              eval_dataset = eval_dataset.map(
                  preprocess_function,
                  batched=True,
                  num_proc=data_args.preprocessing_num_workers,
                  load_from_cache_file=not data_args.overwrite_cache,
              )
  
      # Data collator
      data_collator = (
          default_data_collator
          if data_args.pad_to_max_length
          else DataCollatorForMultipleChoice(
              tokenizer=tokenizer,
              pad_to_multiple_of=8 if training_args.fp16 else None,
          )
      )
  
      # Metric
      def compute_metrics(eval_predictions):
          predictions, label_ids = eval_predictions
          preds = np.argmax(predictions, axis=1)
          return {
              "accuracy": (preds == label_ids)
              .astype(np.float32)
              .mean()
              .item()
          }
  
      # Setup adapters
      setup_adapter_training(model, adapter_args, "swag")
      # Initialize our Trainer
      trainer_class = (
          AdapterTrainer if adapter_args.train_adapter else Trainer
      )
      trainer = trainer_class(
          model=model,
          args=training_args,
          train_dataset=train_dataset
          if training_args.do_train
          else None,
          eval_dataset=eval_dataset if training_args.do_eval else None,
          tokenizer=tokenizer,
          data_collator=data_collator,
          compute_metrics=compute_metrics,
      )
  
      # Training
      if training_args.do_train:
          checkpoint = None
          if training_args.resume_from_checkpoint is not None:
              checkpoint = training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
              checkpoint = last_checkpoint
          train_result = trainer.train(
              resume_from_checkpoint=checkpoint
          )
          trainer.save_model()  # Saves the tokenizer too for easy upload
          metrics = train_result.metrics
  
          max_train_samples = (
              data_args.max_train_samples
              if data_args.max_train_samples is not None
              else len(train_dataset)
          )
          metrics["train_samples"] = min(
              max_train_samples, len(train_dataset)
          )
  
          trainer.log_metrics("train", metrics)
          trainer.save_metrics("train", metrics)
          trainer.save_state()
  
      # Evaluation
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
  
          metrics = trainer.evaluate()
          max_eval_samples = (
              data_args.max_eval_samples
              if data_args.max_eval_samples is not None
              else len(eval_dataset)
          )
          metrics["eval_samples"] = min(
              max_eval_samples, len(eval_dataset)
          )
  
          trainer.log_metrics("eval", metrics)
          trainer.save_metrics("eval", metrics)
  
      kwargs = dict(
          finetuned_from=model_args.model_name_or_path,
          tasks="multiple-choice",
          dataset_tags="swag",
          dataset_args="regular",
          dataset="SWAG",
          language="en",
      )
  
      if training_args.push_to_hub:
          trainer.push_to_hub(**kwargs)
      else:
          trainer.create_model_card(**kwargs)
  
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from itertools import chain
  from typing import Optional, Union
  import datasets
  import numpy
  import torch
  from datasets import load_dataset
  import adapters
  import transformers
  from adapters import AdapterArguments, AdapterTrainer, setup_adapter_training
  from transformers import AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import PaddingStrategy, check_min_version
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: unknown, dict