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,
              ORTSeq2SeqTrainingArguments,
          )
      )
      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_summarization", 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}")
  
      if (
          data_args.source_prefix is None
          and model_args.model_name_or_path
          in [
              "t5-small",
              "t5-base",
              "t5-large",
              "t5-3b",
              "t5-11b",
          ]
      ):
          logger.warning(
              "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
              "`--source_prefix 'summarize: ' `"
          )
  
      # 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 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 first column for the full texts and the second column for the
      # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
      #
      # 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,
          )
      else:
          data_files = {}
          if data_args.train_file is not None:
              data_files["train"] = data_args.train_file
              extension = data_args.train_file.split(".")[-1]
          if data_args.validation_file is not None:
              data_files["validation"] = data_args.validation_file
              extension = data_args.validation_file.split(".")[-1]
          if data_args.test_file is not None:
              data_files["test"] = data_args.test_file
              extension = data_args.test_file.split(".")[-1]
          raw_datasets = load_dataset(
              extension,
              data_files=data_files,
              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.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,
          token=model_args.token,
          trust_remote_code=model_args.trust_remote_code,
      )
      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,
          token=model_args.token,
          trust_remote_code=model_args.trust_remote_code,
      )
      model = AutoModelForSeq2SeqLM.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,
      )
  
      if model.config.decoder_start_token_id is None and isinstance(
          tokenizer, (MBartTokenizer, MBartTokenizerFast)
      ):
          if isinstance(tokenizer, MBartTokenizer):
              model.config.decoder_start_token_id = (
                  tokenizer.lang_code_to_id[data_args.lang]
              )
          else:
              model.config.decoder_start_token_id = (
                  tokenizer.convert_tokens_to_ids(data_args.lang)
              )
  
      if model.config.decoder_start_token_id is None:
          raise ValueError(
              "Make sure that `config.decoder_start_token_id` is correctly defined"
          )
  
      if (
          hasattr(model.config, "max_position_embeddings")
          and model.config.max_position_embeddings
          < data_args.max_source_length
      ):
          if model_args.resize_position_embeddings is None:
              logger.warning(
                  "Increasing the model's number of position embedding vectors from"
                  f" {model.config.max_position_embeddings} to {data_args.max_source_length}."
              )
              model.resize_position_embeddings(
                  data_args.max_source_length
              )
          elif model_args.resize_position_embeddings:
              model.resize_position_embeddings(
                  data_args.max_source_length
              )
          else:
              raise ValueError(
                  f"`--max_source_length` is set to {data_args.max_source_length}, but the model only has"
                  f" {model.config.max_position_embeddings} position encodings. Consider either reducing"
                  f" `--max_source_length` to {model.config.max_position_embeddings} or to automatically resize the"
                  " model's position encodings by passing `--resize_position_embeddings`."
              )
  
      prefix = (
          data_args.source_prefix
          if data_args.source_prefix is not None
          else ""
      )
  
      # Preprocessing the datasets.
      # We need to tokenize inputs and targets.
      if training_args.do_train:
          if "train" not in raw_datasets:
              raise ValueError("--do_train requires a train dataset")
          column_names = raw_datasets["train"].column_names
      elif training_args.do_eval:
          if "validation" not in raw_datasets:
              raise ValueError(
                  "--do_eval requires a validation dataset"
              )
          column_names = raw_datasets["validation"].column_names
      elif training_args.do_predict:
          if "test" not in raw_datasets:
              raise ValueError("--do_predict requires a test dataset")
          column_names = raw_datasets["test"].column_names
      else:
          logger.info(
              "There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`."
          )
          return
  
      if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
          assert (
              data_args.lang is not None
          ), f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --lang argument"
  
          tokenizer.src_lang = data_args.lang
          tokenizer.tgt_lang = data_args.lang
  
          # For multilingual translation models like mBART-50 and M2M100 we need to force the target language token
          # as the first generated token. We ask the user to explicitly provide this as --forced_bos_token argument.
          forced_bos_token_id = (
              tokenizer.lang_code_to_id[data_args.forced_bos_token]
              if data_args.forced_bos_token is not None
              else None
          )
          model.config.forced_bos_token_id = forced_bos_token_id
  
      # Get the column names for input/target.
      dataset_columns = summarization_name_mapping.get(
          data_args.dataset_name, None
      )
      if data_args.text_column is None:
          text_column = (
              dataset_columns[0]
              if dataset_columns is not None
              else column_names[0]
          )
      else:
          text_column = data_args.text_column
          if text_column not in column_names:
              raise ValueError(
                  f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
              )
      if data_args.summary_column is None:
          summary_column = (
              dataset_columns[1]
              if dataset_columns is not None
              else column_names[1]
          )
      else:
          summary_column = data_args.summary_column
          if summary_column not in column_names:
              raise ValueError(
                  f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
              )
  
      # Temporarily set max_target_length for training.
      max_target_length = data_args.max_target_length
      padding = "max_length" if data_args.pad_to_max_length else False
  
      if training_args.label_smoothing_factor > 0 and not hasattr(
          model, "prepare_decoder_input_ids_from_labels"
      ):
          logger.warning(
              "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for "
              f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
          )
  
      def preprocess_function(examples):
          # remove pairs where at least one record is None
  
          inputs, targets = [], []
          for i in range(len(examples[text_column])):
              if (
                  examples[text_column][i]
                  and examples[summary_column][i]
              ):
                  inputs.append(examples[text_column][i])
                  targets.append(examples[summary_column][i])
  
          inputs = [prefix + inp for inp in inputs]
          model_inputs = tokenizer(
              inputs,
              max_length=data_args.max_source_length,
              padding=padding,
              truncation=True,
          )
  
          # Tokenize targets with the `text_target` keyword argument
          labels = tokenizer(
              text_target=targets,
              max_length=max_target_length,
              padding=padding,
              truncation=True,
          )
  
          # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
          # padding in the loss.
          if (
              padding == "max_length"
              and data_args.ignore_pad_token_for_loss
          ):
              labels["input_ids"] = [
                  [
                      (l if l != tokenizer.pad_token_id else -100)
                      for l in label
                  ]
                  for label in labels["input_ids"]
              ]
  
          model_inputs["labels"] = labels["input_ids"]
          return model_inputs
  
      if training_args.do_train:
          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,
                  remove_columns=column_names,
                  load_from_cache_file=not data_args.overwrite_cache,
                  desc="Running tokenizer on train dataset",
              )
  
      if training_args.do_eval:
          max_target_length = data_args.val_max_target_length
          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,
                  remove_columns=column_names,
                  load_from_cache_file=not data_args.overwrite_cache,
                  desc="Running tokenizer on validation dataset",
              )
  
      if training_args.do_predict:
          max_target_length = data_args.val_max_target_length
          predict_dataset = raw_datasets["test"]
          if data_args.max_predict_samples is not None:
              max_predict_samples = min(
                  len(predict_dataset), data_args.max_predict_samples
              )
              predict_dataset = predict_dataset.select(
                  range(max_predict_samples)
              )
          with training_args.main_process_first(
              desc="prediction dataset map pre-processing"
          ):
              predict_dataset = predict_dataset.map(
                  preprocess_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 prediction dataset",
              )
  
      # Data collator
      label_pad_token_id = (
          -100
          if data_args.ignore_pad_token_for_loss
          else tokenizer.pad_token_id
      )
      data_collator = DataCollatorForSeq2Seq(
          tokenizer,
          model=model,
          label_pad_token_id=label_pad_token_id,
          pad_to_multiple_of=8 if training_args.fp16 else None,
      )
  
      # Metric
      metric = evaluate.load("rouge")
  
      def postprocess_text(preds, labels):
          preds = [pred.strip() for pred in preds]
          labels = [label.strip() for label in labels]
  
          # rougeLSum expects newline after each sentence
          preds = [
              "\n".join(nltk.sent_tokenize(pred)) for pred in preds
          ]
          labels = [
              "\n".join(nltk.sent_tokenize(label)) for label in labels
          ]
  
          return preds, labels
  
      def compute_metrics(eval_preds):
          preds, labels = eval_preds
          if isinstance(preds, tuple):
              preds = preds[0]
          # Replace -100s used for padding as we can't decode them
          preds = np.where(preds != -100, preds, tokenizer.pad_token_id)
          decoded_preds = tokenizer.batch_decode(
              preds, skip_special_tokens=True
          )
          labels = np.where(
              labels != -100, labels, tokenizer.pad_token_id
          )
          decoded_labels = tokenizer.batch_decode(
              labels, skip_special_tokens=True
          )
  
          # Some simple post-processing
          decoded_preds, decoded_labels = postprocess_text(
              decoded_preds, decoded_labels
          )
  
          result = metric.compute(
              predictions=decoded_preds,
              references=decoded_labels,
              use_stemmer=True,
          )
          result = {k: round(v * 100, 4) for k, v in result.items()}
          prediction_lens = [
              np.count_nonzero(pred != tokenizer.pad_token_id)
              for pred in preds
          ]
          result["gen_len"] = np.mean(prediction_lens)
          return result
  
      # Override the decoding parameters of Seq2SeqTrainer
      training_args.generation_max_length = (
          training_args.generation_max_length
          if training_args.generation_max_length is not None
          else data_args.val_max_target_length
      )
      training_args.generation_num_beams = (
          data_args.num_beams
          if data_args.num_beams is not None
          else training_args.generation_num_beams
      )
  
      # Initialize our Trainer
      trainer = ORTSeq2SeqTrainer(
          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
          if training_args.predict_with_generate
          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
      results = {}
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
          if isinstance(eval_dataset, dict):
              metrics = {}
              for eval_ds_name, eval_ds in eval_dataset.items():
                  dataset_metrics = trainer.evaluate(
                      eval_dataset=eval_ds,
                      metric_key_prefix=f"eval_{eval_ds_name}",
                  )
                  metrics.update(dataset_metrics)
          else:
              metrics = trainer.evaluate(metric_key_prefix="eval")
          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)
  
      if training_args.do_predict:
          logger.info("*** Predict ***")
  
          predict_results = trainer.predict(
              predict_dataset, metric_key_prefix="predict"
          )
          metrics = predict_results.metrics
          max_predict_samples = (
              data_args.max_predict_samples
              if data_args.max_predict_samples is not None
              else len(predict_dataset)
          )
          metrics["predict_samples"] = min(
              max_predict_samples, len(predict_dataset)
          )
  
          trainer.log_metrics("predict", metrics)
          trainer.save_metrics("predict", metrics)
  
          if trainer.is_world_process_zero():
              if training_args.predict_with_generate:
                  predictions = predict_results.predictions
                  predictions = np.where(
                      predictions != -100,
                      predictions,
                      tokenizer.pad_token_id,
                  )
                  predictions = tokenizer.batch_decode(
                      predictions,
                      skip_special_tokens=True,
                      clean_up_tokenization_spaces=True,
                  )
                  predictions = [pred.strip() for pred in predictions]
                  output_prediction_file = os.path.join(
                      training_args.output_dir,
                      "generated_predictions.txt",
                  )
                  with open(output_prediction_file, "w") as writer:
                      writer.write("\n".join(predictions))
  
      kwargs = {
          "finetuned_from": model_args.model_name_or_path,
          "tasks": "summarization",
      }
      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 data_args.lang is not None:
          kwargs["language"] = data_args.lang
  
      if training_args.push_to_hub:
          trainer.push_to_hub(**kwargs)
      else:
          trainer.create_model_card(**kwargs)
  
      return results
  
imports: import logging
  import os
  import sys
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional
  import datasets
  import evaluate
  import nltk
  import numpy
  import transformers
  from datasets import load_dataset
  from filelock import FileLock
  from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, DataCollatorForSeq2Seq, HfArgumentParser, MBart50Tokenizer, MBart50TokenizerFast, MBartTokenizer, MBartTokenizerFast, set_seed
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version, is_offline_mode, send_example_telemetry
  from transformers.utils.versions import require_version
  from optimum.onnxruntime import ORTSeq2SeqTrainer, ORTSeq2SeqTrainingArguments
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: results, model_inputs, preds,  labels, result