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,
          )
      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
          if data_args.test_file is not None:
              data_files["test"] = data_args.test_file
          extension = data_args.train_file.split(".")[-1]
          raw_datasets = load_dataset(
              extension,
              data_files=data_files,
              cache_dir=model_args.cache_dir,
          )
      # 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 training_args.do_train:
          column_names = raw_datasets["train"].column_names
          features = raw_datasets["train"].features
      else:
          column_names = raw_datasets["validation"].column_names
          features = raw_datasets["validation"].features
  
      if data_args.text_column_name is not None:
          text_column_name = data_args.text_column_name
      elif "tokens" in column_names:
          text_column_name = "tokens"
      else:
          text_column_name = column_names[0]
  
      if data_args.label_column_name is not None:
          label_column_name = data_args.label_column_name
      elif f"{data_args.task_name}_tags" in column_names:
          label_column_name = f"{data_args.task_name}_tags"
      else:
          label_column_name = column_names[1]
  
      # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
      # unique labels.
      def get_label_list(labels):
          unique_labels = set()
          for label in labels:
              unique_labels = unique_labels | set(label)
          label_list = list(unique_labels)
          label_list.sort()
          return label_list
  
      # If the labels are of type ClassLabel, they are already integers and we have the map stored somewhere.
      # Otherwise, we have to get the list of labels manually.
      labels_are_int = isinstance(
          features[label_column_name].feature, ClassLabel
      )
      if labels_are_int:
          label_list = features[label_column_name].feature.names
          label_to_id = {i: i for i in range(len(label_list))}
      else:
          label_list = get_label_list(
              raw_datasets["train"][label_column_name]
          )
          label_to_id = {l: i for i, l in enumerate(label_list)}
  
      num_labels = len(label_list)
  
      # 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,
          num_labels=num_labels,
          finetuning_task=data_args.task_name,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
  
      tokenizer_name_or_path = (
          model_args.tokenizer_name
          if model_args.tokenizer_name
          else model_args.model_name_or_path
      )
      if config.model_type in {"bloom", "gpt2", "roberta"}:
          tokenizer = AutoTokenizer.from_pretrained(
              tokenizer_name_or_path,
              cache_dir=model_args.cache_dir,
              use_fast=True,
              revision=model_args.model_revision,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
              add_prefix_space=True,
          )
      else:
          tokenizer = AutoTokenizer.from_pretrained(
              tokenizer_name_or_path,
              cache_dir=model_args.cache_dir,
              use_fast=True,
              revision=model_args.model_revision,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
  
      model = AutoModelForTokenClassification.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,
          ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
      )
  
      # Convert the model into an adapter model
      adapters.init(model)
  
      # Tokenizer check: this script requires a fast tokenizer.
      if not isinstance(tokenizer, PreTrainedTokenizerFast):
          raise ValueError(
              "This example script only works for models that have a fast tokenizer. Checkout the big table of models at"
              " https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet"
              " this requirement"
          )
  
      # Model has labels -> use them.
      if (
          model.config.label2id
          != PretrainedConfig(num_labels=num_labels).label2id
      ):
          if list(sorted(model.config.label2id.keys())) == list(
              sorted(label_list)
          ):
              # Reorganize `label_list` to match the ordering of the model.
              if labels_are_int:
                  label_to_id = {
                      i: int(model.config.label2id[l])
                      for i, l in enumerate(label_list)
                  }
                  label_list = [
                      model.config.id2label[i]
                      for i in range(num_labels)
                  ]
              else:
                  label_list = [
                      model.config.id2label[i]
                      for i in range(num_labels)
                  ]
                  label_to_id = {l: i for i, l in enumerate(label_list)}
          else:
              logger.warning(
                  "Your model seems to have been trained with labels, but they don't match the dataset: ",
                  f"model labels: {list(sorted(model.config.label2id.keys()))}, dataset labels:"
                  f" {list(sorted(label_list))}.\nIgnoring the model labels as a result.",
              )
  
      # Set the correspondences label/ID inside the model config
      model.config.label2id = {l: i for i, l in enumerate(label_list)}
      model.config.id2label = {i: l for i, l in enumerate(label_list)}
  
      # Map that sends B-Xxx label to its I-Xxx counterpart
      b_to_i_label = []
      for idx, label in enumerate(label_list):
          if (
              label.startswith("B-")
              and label.replace("B-", "I-") in label_list
          ):
              b_to_i_label.append(
                  label_list.index(label.replace("B-", "I-"))
              )
          else:
              b_to_i_label.append(idx)
  
      # Preprocessing the dataset
      # Padding strategy
      padding = "max_length" if data_args.pad_to_max_length else False
  
      # Tokenize all texts and align the labels with them.
      def tokenize_and_align_labels(examples):
          tokenized_inputs = tokenizer(
              examples[text_column_name],
              padding=padding,
              truncation=True,
              max_length=data_args.max_seq_length,
              # We use this argument because the texts in our dataset are lists of words (with a label for each word).
              is_split_into_words=True,
          )
          labels = []
          for i, label in enumerate(examples[label_column_name]):
              word_ids = tokenized_inputs.word_ids(batch_index=i)
              previous_word_idx = None
              label_ids = []
              for word_idx in word_ids:
                  # Special tokens have a word id that is None. We set the label to -100 so they are automatically
                  # ignored in the loss function.
                  if word_idx is None:
                      label_ids.append(-100)
                  # We set the label for the first token of each word.
                  elif word_idx != previous_word_idx:
                      label_ids.append(label_to_id[label[word_idx]])
                  # For the other tokens in a word, we set the label to either the current label or -100, depending on
                  # the label_all_tokens flag.
                  else:
                      if data_args.label_all_tokens:
                          label_ids.append(
                              b_to_i_label[label_to_id[label[word_idx]]]
                          )
                      else:
                          label_ids.append(-100)
                  previous_word_idx = word_idx
  
              labels.append(label_ids)
          tokenized_inputs["labels"] = labels
          return tokenized_inputs
  
      if training_args.do_train:
          if "train" not in raw_datasets:
              raise ValueError("--do_train requires a train dataset")
          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(
                  tokenize_and_align_labels,
                  batched=True,
                  num_proc=data_args.preprocessing_num_workers,
                  load_from_cache_file=not data_args.overwrite_cache,
                  desc="Running tokenizer on train dataset",
              )
  
      if training_args.do_eval:
          if "validation" not in raw_datasets:
              raise ValueError(
                  "--do_eval requires a validation dataset"
              )
          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(
                  tokenize_and_align_labels,
                  batched=True,
                  num_proc=data_args.preprocessing_num_workers,
                  load_from_cache_file=not data_args.overwrite_cache,
                  desc="Running tokenizer on validation dataset",
              )
  
      if training_args.do_predict:
          if "test" not in raw_datasets:
              raise ValueError("--do_predict requires a test dataset")
          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(
                  tokenize_and_align_labels,
                  batched=True,
                  num_proc=data_args.preprocessing_num_workers,
                  load_from_cache_file=not data_args.overwrite_cache,
                  desc="Running tokenizer on prediction dataset",
              )
  
      # Data collator
      data_collator = DataCollatorForTokenClassification(
          tokenizer,
          pad_to_multiple_of=8 if training_args.fp16 else None,
      )
  
      # Metrics
      metric = evaluate.load("seqeval")
  
      def compute_metrics(p):
          predictions, labels = p
          predictions = np.argmax(predictions, axis=2)
  
          # Remove ignored index (special tokens)
          true_predictions = [
              [
                  label_list[p]
                  for (p, l) in zip(prediction, label)
                  if l != -100
              ]
              for prediction, label in zip(predictions, labels)
          ]
          true_labels = [
              [
                  label_list[l]
                  for (p, l) in zip(prediction, label)
                  if l != -100
              ]
              for prediction, label in zip(predictions, labels)
          ]
  
          results = metric.compute(
              predictions=true_predictions, references=true_labels
          )
          if data_args.return_entity_level_metrics:
              # Unpack nested dictionaries
              final_results = {}
              for key, value in results.items():
                  if isinstance(value, dict):
                      for n, v in value.items():
                          final_results[f"{key}_{n}"] = v
                  else:
                      final_results[key] = value
              return final_results
          else:
              return {
                  "precision": results["overall_precision"],
                  "recall": results["overall_recall"],
                  "f1": results["overall_f1"],
                  "accuracy": results["overall_accuracy"],
              }
  
      # Setup adapters
      setup_adapter_training(
          model, adapter_args, data_args.dataset_name or "ner"
      )
      # 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=data_collator,
          compute_metrics=compute_metrics,
      )
  
      # 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
          )
          metrics = train_result.metrics
          trainer.save_model()  # Saves the tokenizer too for easy upload
  
          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)
          )
  
          trainer.log_metrics("eval", metrics)
          trainer.save_metrics("eval", metrics)
  
      # Predict
      if training_args.do_predict:
          logger.info("*** Predict ***")
  
          predictions, labels, metrics = trainer.predict(
              predict_dataset, metric_key_prefix="predict"
          )
          predictions = np.argmax(predictions, axis=2)
  
          # Remove ignored index (special tokens)
          true_predictions = [
              [
                  label_list[p]
                  for (p, l) in zip(prediction, label)
                  if l != -100
              ]
              for prediction, label in zip(predictions, labels)
          ]
  
          trainer.log_metrics("predict", metrics)
          trainer.save_metrics("predict", metrics)
  
          # Save predictions
          output_predictions_file = os.path.join(
              training_args.output_dir, "predictions.txt"
          )
          if trainer.is_world_process_zero():
              with open(output_predictions_file, "w") as writer:
                  for prediction in true_predictions:
                      writer.write(" ".join(prediction) + "\n")
  
      kwargs = {
          "finetuned_from": model_args.model_name_or_path,
          "tasks": "token-classification",
      }
      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 os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  import datasets
  import numpy
  from datasets import ClassLabel, load_dataset
  import adapters
  import evaluate
  import transformers
  from adapters import AdapterArguments, AdapterTrainer, setup_adapter_training
  from transformers import AutoConfig, AutoModelForTokenClassification, AutoTokenizer, DataCollatorForTokenClassification, HfArgumentParser, PretrainedConfig, PreTrainedTokenizerFast, Trainer, TrainingArguments, set_seed
  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: label_list, tokenized_inputs, final_results, dict