code: def main():
      args = parse_args()
  
      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry("run_swag_no_trainer", args)
  
      # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
      # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
      # in the environment
      accelerator_log_kwargs = {}
  
      if args.with_tracking:
          accelerator_log_kwargs["log_with"] = args.report_to
          accelerator_log_kwargs["logging_dir"] = args.output_dir
  
      accelerator = Accelerator(
          gradient_accumulation_steps=args.gradient_accumulation_steps,
          **accelerator_log_kwargs,
      )
  
      # Make one log on every process with the configuration for debugging.
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      logger.info(accelerator.state, main_process_only=False)
      if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_info()
      else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
  
      # If passed along, set the training seed now.
      if args.seed is not None:
          set_seed(args.seed)
  
      # Handle the repository creation
      if accelerator.is_main_process:
          if args.push_to_hub:
              if args.hub_model_id is None:
                  repo_name = get_full_repo_name(
                      Path(args.output_dir).name, token=args.hub_token
                  )
              else:
                  repo_name = args.hub_model_id
              repo = Repository(args.output_dir, clone_from=repo_name)
  
              with open(
                  os.path.join(args.output_dir, ".gitignore"), "w+"
              ) as gitignore:
                  if "step_*" not in gitignore:
                      gitignore.write("step_*\n")
                  if "epoch_*" not in gitignore:
                      gitignore.write("epoch_*\n")
          elif args.output_dir is not None:
              os.makedirs(args.output_dir, exist_ok=True)
      accelerator.wait_for_everyone()
  
      # 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 args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          raw_datasets = load_dataset(
              args.dataset_name, args.dataset_config_name
          )
      else:
          data_files = {}
          if args.train_file is not None:
              data_files["train"] = args.train_file
          if args.validation_file is not None:
              data_files["validation"] = args.validation_file
          extension = args.train_file.split(".")[-1]
          raw_datasets = load_dataset(extension, data_files=data_files)
      # Trim a number of training examples
      if args.debug:
          for split in raw_datasets.keys():
              raw_datasets[split] = raw_datasets[split].select(
                  range(100)
              )
      # 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.
  
      if raw_datasets["train"] is not None:
          column_names = raw_datasets["train"].column_names
      else:
          column_names = raw_datasets["validation"].column_names
  
      # 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"
      label_column_name = (
          "label" if "label" in column_names else "labels"
      )
  
      # Load pretrained model and tokenizer
      #
      # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      if args.config_name:
          config = AutoConfig.from_pretrained(args.model_name_or_path)
      elif args.model_name_or_path:
          config = AutoConfig.from_pretrained(args.model_name_or_path)
      else:
          config = CONFIG_MAPPING[args.model_type]()
          logger.warning(
              "You are instantiating a new config instance from scratch."
          )
  
      if args.tokenizer_name:
          tokenizer = AutoTokenizer.from_pretrained(
              args.tokenizer_name, use_fast=not args.use_slow_tokenizer
          )
      elif args.model_name_or_path:
          tokenizer = AutoTokenizer.from_pretrained(
              args.model_name_or_path,
              use_fast=not args.use_slow_tokenizer,
          )
      else:
          raise ValueError(
              "You are instantiating a new tokenizer from scratch. This is not supported by this script."
              "You can do it from another script, save it, and load it from here, using --tokenizer_name."
          )
  
      if args.model_name_or_path:
          model = AutoModelForMultipleChoice.from_pretrained(
              args.model_name_or_path,
              from_tf=bool(".ckpt" in args.model_name_or_path),
              config=config,
          )
      else:
          logger.info("Training new model from scratch")
          model = AutoModelForMultipleChoice.from_config(config)
  
      model.resize_token_embeddings(len(tokenizer))
  
      # Preprocessing the datasets.
      # First we tokenize all the texts.
      padding = "max_length" if args.pad_to_max_length else False
  
      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)
          ]
          labels = examples[label_column_name]
  
          # Flatten out
          first_sentences = list(chain(*first_sentences))
          second_sentences = list(chain(*second_sentences))
  
          # Tokenize
          tokenized_examples = tokenizer(
              first_sentences,
              second_sentences,
              max_length=args.max_length,
              padding=padding,
              truncation=True,
          )
          # Un-flatten
          tokenized_inputs = {
              k: [v[i : i + 4] for i in range(0, len(v), 4)]
              for k, v in tokenized_examples.items()
          }
          tokenized_inputs["labels"] = labels
          return tokenized_inputs
  
      with accelerator.main_process_first():
          processed_datasets = raw_datasets.map(
              preprocess_function,
              batched=True,
              remove_columns=raw_datasets["train"].column_names,
          )
  
      train_dataset = processed_datasets["train"]
      eval_dataset = processed_datasets["validation"]
  
      # Log a few random samples from the training set:
      for index in random.sample(range(len(train_dataset)), 3):
          logger.info(
              f"Sample {index} of the training set: {train_dataset[index]}."
          )
  
      # DataLoaders creation:
      if args.pad_to_max_length:
          # If padding was already done ot max length, we use the default data collator that will just convert everything
          # to tensors.
          data_collator = default_data_collator
      else:
          # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
          # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
          # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
          data_collator = DataCollatorForMultipleChoice(
              tokenizer,
              pad_to_multiple_of=(8 if accelerator.use_fp16 else None),
          )
  
      train_dataloader = DataLoader(
          train_dataset,
          shuffle=True,
          collate_fn=data_collator,
          batch_size=args.per_device_train_batch_size,
      )
      eval_dataloader = DataLoader(
          eval_dataset,
          collate_fn=data_collator,
          batch_size=args.per_device_eval_batch_size,
      )
  
      # Optimizer
      # Split weights in two groups, one with weight decay and the other not.
      no_decay = ["bias", "LayerNorm.weight"]
      optimizer_grouped_parameters = [
          {
              "params": [
                  p
                  for n, p in model.named_parameters()
                  if not any(nd in n for nd in no_decay)
              ],
              "weight_decay": args.weight_decay,
          },
          {
              "params": [
                  p
                  for n, p in model.named_parameters()
                  if any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.0,
          },
      ]
      optimizer = torch.optim.AdamW(
          optimizer_grouped_parameters, lr=args.learning_rate
      )
  
      # Use the device given by the `accelerator` object.
      device = accelerator.device
      model.to(device)
  
      # Scheduler and math around the number of training steps.
      overrode_max_train_steps = False
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if args.max_train_steps is None:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
          overrode_max_train_steps = True
  
      lr_scheduler = get_scheduler(
          name=args.lr_scheduler_type,
          optimizer=optimizer,
          num_warmup_steps=args.num_warmup_steps
          * args.gradient_accumulation_steps,
          num_training_steps=args.max_train_steps
          * args.gradient_accumulation_steps,
      )
  
      # Prepare everything with our `accelerator`.
      (
          model,
          optimizer,
          train_dataloader,
          eval_dataloader,
          lr_scheduler,
      ) = accelerator.prepare(
          model,
          optimizer,
          train_dataloader,
          eval_dataloader,
          lr_scheduler,
      )
  
      # We need to recalculate our total training steps as the size of the training dataloader may have changed.
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if overrode_max_train_steps:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
      # Afterwards we recalculate our number of training epochs
      args.num_train_epochs = math.ceil(
          args.max_train_steps / num_update_steps_per_epoch
      )
  
      # Figure out how many steps we should save the Accelerator states
      checkpointing_steps = args.checkpointing_steps
      if (
          checkpointing_steps is not None
          and checkpointing_steps.isdigit()
      ):
          checkpointing_steps = int(checkpointing_steps)
  
      # We need to initialize the trackers we use, and also store our configuration.
      # The trackers initializes automatically on the main process.
      if args.with_tracking:
          experiment_config = vars(args)
          # TensorBoard cannot log Enums, need the raw value
          experiment_config["lr_scheduler_type"] = experiment_config[
              "lr_scheduler_type"
          ].value
          accelerator.init_trackers(
              "swag_no_trainer", experiment_config
          )
  
      # Metrics
      metric = evaluate.load("accuracy")
  
      # Train!
      total_batch_size = (
          args.per_device_train_batch_size
          * accelerator.num_processes
          * args.gradient_accumulation_steps
      )
  
      logger.info("***** Running training *****")
      logger.info(f"  Num examples = {len(train_dataset)}")
      logger.info(f"  Num Epochs = {args.num_train_epochs}")
      logger.info(
          f"  Instantaneous batch size per device = {args.per_device_train_batch_size}"
      )
      logger.info(
          f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
      )
      logger.info(
          f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}"
      )
      logger.info(
          f"  Total optimization steps = {args.max_train_steps}"
      )
      # Only show the progress bar once on each machine.
      progress_bar = tqdm(
          range(args.max_train_steps),
          disable=not accelerator.is_local_main_process,
      )
      completed_steps = 0
      starting_epoch = 0
  
      # Potentially load in the weights and states from a previous save
      if args.resume_from_checkpoint:
          if (
              args.resume_from_checkpoint is not None
              or args.resume_from_checkpoint != ""
          ):
              accelerator.print(
                  f"Resumed from checkpoint: {args.resume_from_checkpoint}"
              )
              accelerator.load_state(args.resume_from_checkpoint)
              path = os.path.basename(args.resume_from_checkpoint)
          else:
              # Get the most recent checkpoint
              dirs = [
                  f.name for f in os.scandir(os.getcwd()) if f.is_dir()
              ]
              dirs.sort(key=os.path.getctime)
              path = dirs[
                  -1
              ]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          training_difference = os.path.splitext(path)[0]
  
          if "epoch" in training_difference:
              starting_epoch = (
                  int(training_difference.replace("epoch_", "")) + 1
              )
              resume_step = None
          else:
              resume_step = int(
                  training_difference.replace("step_", "")
              )
              starting_epoch = resume_step // len(train_dataloader)
              resume_step -= starting_epoch * len(train_dataloader)
  
      for epoch in range(starting_epoch, args.num_train_epochs):
          model.train()
          if args.with_tracking:
              total_loss = 0
          for step, batch in enumerate(train_dataloader):
              # We need to skip steps until we reach the resumed step
              if (
                  args.resume_from_checkpoint
                  and epoch == starting_epoch
              ):
                  if resume_step is not None and step < resume_step:
                      completed_steps += 1
                      continue
  
              with accelerator.accumulate(model):
                  outputs = model(**batch)
                  loss = outputs.loss
                  # We keep track of the loss at each epoch
                  if args.with_tracking:
                      total_loss += loss.detach().float()
                  accelerator.backward(loss)
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
  
              # Checks if the accelerator has performed an optimization step behind the scenes
              if accelerator.sync_gradients:
                  progress_bar.update(1)
                  completed_steps += 1
  
              if isinstance(checkpointing_steps, int):
                  if completed_steps % checkpointing_steps == 0:
                      output_dir = f"step_{completed_steps }"
                      if args.output_dir is not None:
                          output_dir = os.path.join(
                              args.output_dir, output_dir
                          )
                      accelerator.save_state(output_dir)
  
              if completed_steps >= args.max_train_steps:
                  break
  
          model.eval()
          for step, batch in enumerate(eval_dataloader):
              with torch.no_grad():
                  outputs = model(**batch)
              predictions = outputs.logits.argmax(dim=-1)
              predictions, references = accelerator.gather_for_metrics(
                  (predictions, batch["labels"])
              )
              metric.add_batch(
                  predictions=predictions,
                  references=references,
              )
  
          eval_metric = metric.compute()
          accelerator.print(f"epoch {epoch}: {eval_metric}")
  
          if args.with_tracking:
              accelerator.log(
                  {
                      "accuracy": eval_metric,
                      "train_loss": total_loss.item()
                      / len(train_dataloader),
                      "epoch": epoch,
                      "step": completed_steps,
                  },
                  step=completed_steps,
              )
  
          if args.push_to_hub and epoch < args.num_train_epochs - 1:
              accelerator.wait_for_everyone()
              unwrapped_model = accelerator.unwrap_model(model)
              unwrapped_model.save_pretrained(
                  args.output_dir,
                  is_main_process=accelerator.is_main_process,
                  save_function=accelerator.save,
              )
              if accelerator.is_main_process:
                  tokenizer.save_pretrained(args.output_dir)
                  repo.push_to_hub(
                      commit_message=f"Training in progress epoch {epoch}",
                      blocking=False,
                      auto_lfs_prune=True,
                  )
  
          if args.checkpointing_steps == "epoch":
              output_dir = f"epoch_{epoch}"
              if args.output_dir is not None:
                  output_dir = os.path.join(args.output_dir, output_dir)
              accelerator.save_state(output_dir)
  
      if args.with_tracking:
          accelerator.end_training()
  
      if args.output_dir is not None:
          accelerator.wait_for_everyone()
          unwrapped_model = accelerator.unwrap_model(model)
          unwrapped_model.save_pretrained(
              args.output_dir,
              is_main_process=accelerator.is_main_process,
              save_function=accelerator.save,
          )
          if accelerator.is_main_process:
              tokenizer.save_pretrained(args.output_dir)
              if args.push_to_hub:
                  repo.push_to_hub(
                      commit_message="End of training",
                      auto_lfs_prune=True,
                  )
          with open(
              os.path.join(args.output_dir, "all_results.json"), "w"
          ) as f:
              json.dump({"eval_accuracy": eval_metric["accuracy"]}, f)
  
imports: import argparse
  import json
  import logging
  import math
  import os
  import random
  from dataclasses import dataclass
  from itertools import chain
  from pathlib import Path
  from typing import Optional, Union
  import datasets
  import torch
  from datasets import load_dataset
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  import evaluate
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from huggingface_hub import Repository
  from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, PreTrainedTokenizerBase, SchedulerType, default_data_collator, get_scheduler
  from transformers.utils import PaddingStrategy, check_min_version, get_full_repo_name, send_example_telemetry
label_desc: Use image node_id 2 for calling a global function with example usage: main() and returns: tokenized_inputs