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, ORTTrainingArguments)
      )
      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.",
              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_clm", 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,
              streaming=data_args.streaming,
          )
          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,
                  streaming=data_args.streaming,
              )
              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,
                  streaming=data_args.streaming,
              )
      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,
              token=model_args.token,
              **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,
                  token=model_args.token,
                  **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,
                  token=model_args.token,
                  **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,
          "token": model_args.token,
          "trust_remote_code": model_args.trust_remote_code,
      }
      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,
          "token": model_args.token,
          "trust_remote_code": model_args.trust_remote_code,
      }
      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,
              token=model_args.token,
              trust_remote_code=model_args.trust_remote_code,
              torch_dtype=torch_dtype,
              low_cpu_mem_usage=model_args.low_cpu_mem_usage,
          )
      else:
          model = AutoModelForCausalLM.from_config(
              config, trust_remote_code=model_args.trust_remote_code
          )
          n_params = sum(
              {
                  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"
          )
  
      # 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 = list(raw_datasets["train"].features)
      else:
          column_names = list(raw_datasets["validation"].features)
      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"
      ):
          if not data_args.streaming:
              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",
              )
          else:
              tokenized_datasets = raw_datasets.map(
                  tokenize_function,
                  batched=True,
                  remove_columns=column_names,
              )
  
      if hasattr(config, "max_position_embeddings"):
          max_pos_embeddings = config.max_position_embeddings
          if max_pos_embeddings < 0:
              max_pos_embeddings = 1024
      else:
          # Define a default value if the attribute is missing in the config.
          max_pos_embeddings = 1024
  
      if data_args.block_size is None:
          block_size = tokenizer.model_max_length
          if block_size > max_pos_embeddings:
              logger.warning(
                  f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
                  f"Using block_size={min(1024, max_pos_embeddings)} instead. You can change that default value by passing --block_size xxx."
              )
              block_size = min(1024, max_pos_embeddings)
      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, and if the total_length < block_size  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 // 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/process#map
  
      with training_args.main_process_first(
          desc="grouping texts together"
      ):
          if not data_args.streaming:
              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}",
              )
          else:
              lm_datasets = tokenized_datasets.map(
                  group_texts,
                  batched=True,
              )
  
      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
              )
  
      # Initialize our Trainer
      trainer = ORTTrainer(
          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
  import warnings
  from dataclasses import dataclass, field
  from itertools import chain
  from typing import Optional
  import datasets
  import evaluate
  import torch
  import transformers
  from datasets import load_dataset
  from transformers import CONFIG_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, 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, send_example_telemetry
  from transformers.utils.versions import require_version
  from optimum.onnxruntime import ORTTrainer, ORTTrainingArguments
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: output, result, logits, metric