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.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          raw_datasets = load_dataset(
              data_args.dataset_name,
              data_args.dataset_config_name,
              cache_dir=model_args.cache_dir,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
          if "validation" not in raw_datasets.keys():
              raw_datasets["validation"] = load_dataset(
                  data_args.dataset_name,
                  data_args.dataset_config_name,
                  split=f"train[:{data_args.validation_split_percentage}%]",
                  cache_dir=model_args.cache_dir,
                  use_auth_token=True
                  if model_args.use_auth_token
                  else None,
              )
              raw_datasets["train"] = load_dataset(
                  data_args.dataset_name,
                  data_args.dataset_config_name,
                  split=f"train[{data_args.validation_split_percentage}%:]",
                  cache_dir=model_args.cache_dir,
                  use_auth_token=True
                  if model_args.use_auth_token
                  else None,
              )
      else:
          data_files = {}
          dataset_args = {}
          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]
              if data_args.train_file is not None
              else data_args.validation_file.split(".")[-1]
          )
          if extension == "txt":
              extension = "text"
              dataset_args[
                  "keep_linebreaks"
              ] = data_args.keep_linebreaks
          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,
              **dataset_args,
          )
          # If no validation data is there, validation_split_percentage will be used to divide the dataset.
          if "validation" not in raw_datasets.keys():
              raw_datasets["validation"] = load_dataset(
                  extension,
                  data_files=data_files,
                  split=f"train[:{data_args.validation_split_percentage}%]",
                  cache_dir=model_args.cache_dir,
                  use_auth_token=True
                  if model_args.use_auth_token
                  else None,
                  **dataset_args,
              )
              raw_datasets["train"] = load_dataset(
                  extension,
                  data_files=data_files,
                  split=f"train[{data_args.validation_split_percentage}%:]",
                  cache_dir=model_args.cache_dir,
                  use_auth_token=True
                  if model_args.use_auth_token
                  else None,
                  **dataset_args,
              )
  
      # 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_kwargs = {
          "cache_dir": model_args.cache_dir,
          "revision": model_args.model_revision,
          "use_auth_token": True if model_args.use_auth_token else None,
      }
      if model_args.config_name:
          config = AutoConfig.from_pretrained(
              model_args.config_name, **config_kwargs
          )
      elif model_args.model_name_or_path:
          config = AutoConfig.from_pretrained(
              model_args.model_name_or_path, **config_kwargs
          )
      else:
          config = CONFIG_MAPPING[model_args.model_type]()
          logger.warning(
              "You are instantiating a new config instance from scratch."
          )
          if model_args.config_overrides is not None:
              logger.info(
                  f"Overriding config: {model_args.config_overrides}"
              )
              config.update_from_string(model_args.config_overrides)
              logger.info(f"New config: {config}")
  
      tokenizer_kwargs = {
          "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,
      }
      if model_args.tokenizer_name:
          tokenizer = AutoTokenizer.from_pretrained(
              model_args.tokenizer_name, **tokenizer_kwargs
          )
      elif model_args.model_name_or_path:
          tokenizer = AutoTokenizer.from_pretrained(
              model_args.model_name_or_path, **tokenizer_kwargs
          )
      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 model_args.model_name_or_path:
          torch_dtype = (
              model_args.torch_dtype
              if model_args.torch_dtype in ["auto", None]
              else getattr(torch, model_args.torch_dtype)
          )
          model = AutoModelForCausalLM.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,
              torch_dtype=torch_dtype,
          )
      else:
          model = AutoModelForCausalLM.from_config(config)
          n_params = sum(
              dict(
                  (p.data_ptr(), p.numel()) for p in model.parameters()
              ).values()
          )
          logger.info(
              f"Training new model from scratch - Total size={n_params/2**20:.2f}M params"
          )
  
      # Convert the model into an adapter model
      adapters.init(model)
  
      # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
      # on a small vocab and want a smaller embedding size, remove this test.
      embedding_size = model.get_input_embeddings().weight.shape[0]
      if len(tokenizer) > embedding_size:
          model.resize_token_embeddings(len(tokenizer))
  
      # Preprocessing the datasets.
      # First we tokenize all the texts.
      if training_args.do_train:
          column_names = raw_datasets["train"].column_names
      else:
          column_names = raw_datasets["validation"].column_names
      text_column_name = (
          "text" if "text" in column_names else column_names[0]
      )
  
      # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
      tok_logger = transformers.utils.logging.get_logger(
          "transformers.tokenization_utils_base"
      )
  
      def tokenize_function(examples):
          with CaptureLogger(tok_logger) as cl:
              output = tokenizer(examples[text_column_name])
          # clm input could be much much longer than block_size
          if (
              "Token indices sequence length is longer than the"
              in cl.out
          ):
              tok_logger.warning(
                  "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
                  " before being passed to the model."
              )
          return output
  
      with training_args.main_process_first(
          desc="dataset map tokenization"
      ):
          tokenized_datasets = raw_datasets.map(
              tokenize_function,
              batched=True,
              num_proc=data_args.preprocessing_num_workers,
              remove_columns=column_names,
              load_from_cache_file=not data_args.overwrite_cache,
              desc="Running tokenizer on dataset",
          )
  
      if data_args.block_size is None:
          block_size = tokenizer.model_max_length
          if block_size > 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 --block_size xxx."
              )
              block_size = 1024
      else:
          if data_args.block_size > tokenizer.model_max_length:
              logger.warning(
                  f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
                  f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
              )
          block_size = min(
              data_args.block_size, tokenizer.model_max_length
          )
  
      # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
      def group_texts(examples):
          # Concatenate all texts.
          concatenated_examples = {
              k: list(chain(*examples[k])) for k in examples.keys()
          }
          total_length = len(
              concatenated_examples[list(examples.keys())[0]]
          )
          # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
          # customize this part to your needs.
          if total_length >= block_size:
              total_length = (total_length // block_size) * block_size
          # Split by chunks of max_len.
          result = {
              k: [
                  t[i : i + block_size]
                  for i in range(0, total_length, block_size)
              ]
              for k, t in concatenated_examples.items()
          }
          result["labels"] = result["input_ids"].copy()
          return result
  
      # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
      # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
      # to preprocess.
      #
      # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
      # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
  
      with training_args.main_process_first(
          desc="grouping texts together"
      ):
          lm_datasets = tokenized_datasets.map(
              group_texts,
              batched=True,
              num_proc=data_args.preprocessing_num_workers,
              load_from_cache_file=not data_args.overwrite_cache,
              desc=f"Grouping texts in chunks of {block_size}",
          )
  
      if training_args.do_train:
          if "train" not in tokenized_datasets:
              raise ValueError("--do_train requires a train dataset")
          train_dataset = lm_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)
              )
  
      if training_args.do_eval:
          if "validation" not in tokenized_datasets:
              raise ValueError(
                  "--do_eval requires a validation dataset"
              )
          eval_dataset = lm_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)
              )
  
          def preprocess_logits_for_metrics(logits, labels):
              if isinstance(logits, tuple):
                  # Depending on the model and config, logits may contain extra tensors,
                  # like past_key_values, but logits always come first
                  logits = logits[0]
              return logits.argmax(dim=-1)
  
          metric = evaluate.load("accuracy")
  
          def compute_metrics(eval_preds):
              preds, labels = eval_preds
              # preds have the same shape as the labels, after the argmax(-1) has been calculated
              # by preprocess_logits_for_metrics but we need to shift the labels
              labels = labels[:, 1:].reshape(-1)
              preds = preds[:, :-1].reshape(-1)
              return metric.compute(
                  predictions=preds, references=labels
              )
  
      # Setup adapters
      setup_adapter_training(
          model, adapter_args, data_args.dataset_name or "clm"
      )
      # 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 will default to DataCollatorWithPadding, so we change it.
          data_collator=default_data_collator,
          compute_metrics=compute_metrics
          if training_args.do_eval and not is_torch_tpu_available()
          else None,
          preprocess_logits_for_metrics=preprocess_logits_for_metrics
          if training_args.do_eval and not is_torch_tpu_available()
          else None,
      )
  
      # 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)
          )
          try:
              perplexity = math.exp(metrics["eval_loss"])
          except OverflowError:
              perplexity = float("inf")
          metrics["perplexity"] = perplexity
  
          trainer.log_metrics("eval", metrics)
          trainer.save_metrics("eval", metrics)
  
      kwargs = {
          "finetuned_from": model_args.model_name_or_path,
          "tasks": "text-generation",
      }
      if data_args.dataset_name is not None:
          kwargs["dataset_tags"] = data_args.dataset_name
          if data_args.dataset_config_name is not None:
              kwargs["dataset_args"] = data_args.dataset_config_name
              kwargs[
                  "dataset"
              ] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
          else:
              kwargs["dataset"] = data_args.dataset_name
  
      if training_args.push_to_hub:
          trainer.push_to_hub(**kwargs)
      else:
          trainer.create_model_card(**kwargs)
  
imports: import logging
  import math
  import os
  import sys
  from dataclasses import dataclass, field
  from itertools import chain
  from typing import Optional
  import datasets
  import torch
  from datasets import load_dataset
  import adapters
  import evaluate
  import transformers
  from adapters import AdapterArguments, AdapterTrainer, setup_adapter_training
  from transformers import CONFIG_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, is_torch_tpu_available, set_seed
  from transformers.testing_utils import CaptureLogger
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version
  from transformers.utils.versions import require_version
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: output, result, logits, metric