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)
      )
      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 = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1])
          )
      else:
          (
              model_args,
              data_args,
              training_args,
          ) = parser.parse_args_into_dataclasses()
  
      if model_args.use_auth_token is not None:
          warnings.warn(
              "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
              FutureWarning,
          )
          if model_args.token is not None:
              raise ValueError(
                  "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
              )
          model_args.token = model_args.use_auth_token
  
      # 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_plm", model_args, data_args)
  
      # 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)],
      )
  
      if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()
  
      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: {training_args.parallel_mode.value == 'distributed'}, 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,
              token=model_args.token,
          )
          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,
                  token=model_args.token,
              )
              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,
                  token=model_args.token,
              )
      else:
          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]
          if extension == "txt":
              extension = "text"
          raw_datasets = load_dataset(
              extension,
              data_files=data_files,
              cache_dir=model_args.cache_dir,
          )
          # 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,
                  token=model_args.token,
              )
              raw_datasets["train"] = load_dataset(
                  extension,
                  data_files=data_files,
                  split=f"train[{data_args.validation_split_percentage}%:]",
                  cache_dir=model_args.cache_dir,
                  token=model_args.token,
              )
  
      # 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.
  
      # 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,
          "token": model_args.token,
      }
      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 = XLNetConfig()
          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,
          "token": model_args.token,
      }
      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:
          model = XLNetLMHeadModel.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,
              token=model_args.token,
              low_cpu_mem_usage=model_args.low_cpu_mem_usage,
          )
      else:
          logger.info("Training new model from scratch")
          model = XLNetLMHeadModel(config)
  
      # 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]
      )
  
      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
      )
  
      if data_args.line_by_line:
          # When using line_by_line, we just tokenize each nonempty line.
          padding = (
              "max_length" if data_args.pad_to_max_length else False
          )
  
          def tokenize_function(examples):
              # Remove empty lines
              examples["text"] = [
                  line
                  for line in examples["text"]
                  if len(line) > 0 and not line.isspace()
              ]
              return tokenizer(
                  examples["text"],
                  padding=padding,
                  truncation=True,
                  max_length=max_seq_length,
              )
  
          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=[text_column_name],
                  load_from_cache_file=not data_args.overwrite_cache,
                  desc="Running tokenizer on dataset line_by_line",
              )
      else:
          # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
          def tokenize_function(examples):
              return tokenizer(examples[text_column_name])
  
          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 every text in dataset",
              )
  
          # Main data processing function that will concatenate all texts from our dataset and generate chunks of
          # max_seq_length.
          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, and if the total_length < max_seq_length  we exclude this batch and return an empty dict.
              # We could add padding if the model supported it instead of this drop, you can customize this part to your needs.
              total_length = (
                  total_length // max_seq_length
              ) * max_seq_length
              # Split by chunks of max_len.
              result = {
                  k: [
                      t[i : i + max_seq_length]
                      for i in range(0, total_length, max_seq_length)
                  ]
                  for k, t in concatenated_examples.items()
              }
              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/process#map
  
          with training_args.main_process_first(
              desc="grouping texts together"
          ):
              tokenized_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 {max_seq_length}",
              )
  
      if training_args.do_train:
          if "train" not in tokenized_datasets:
              raise ValueError("--do_train requires a train dataset")
          train_dataset = tokenized_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 = tokenized_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)
              )
  
      # Data collator
      data_collator = DataCollatorForPermutationLanguageModeling(
          tokenizer=tokenizer,
          plm_probability=data_args.plm_probability,
          max_span_length=data_args.max_span_length,
      )
  
      # Initialize our Trainer
      trainer = Trainer(
          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,
      )
  
      # 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": "language-modeling",
      }
      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
  import warnings
  from dataclasses import dataclass, field
  from itertools import chain
  from typing import Optional
  import datasets
  from datasets import load_dataset
  import transformers
  from transformers import AutoConfig, AutoTokenizer, DataCollatorForPermutationLanguageModeling, HfArgumentParser, Trainer, TrainingArguments, XLNetConfig, XLNetLMHeadModel, set_seed
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version, send_example_telemetry
  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: tokenizer, tokenizer, result