code: def main():
      args = parse_args()
  
      # 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_ner_no_trainer", args)
  
      # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
      # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
      # in the environment
      accelerator = (
          Accelerator(
              log_with=args.report_to, logging_dir=args.output_dir
          )
          if args.with_tracking
          else Accelerator()
      )
      # Make one log on every process with the configuration for debugging.
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      logger.info(accelerator.state, main_process_only=False)
      if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_info()
      else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
  
      # If passed along, set the training seed now.
      if args.seed is not None:
          set_seed(args.seed)
  
      # Handle the repository creation
      if accelerator.is_main_process:
          if args.push_to_hub:
              if args.hub_model_id is None:
                  repo_name = get_full_repo_name(
                      Path(args.output_dir).name, token=args.hub_token
                  )
              else:
                  repo_name = args.hub_model_id
              repo = Repository(args.output_dir, clone_from=repo_name)
  
              with open(
                  os.path.join(args.output_dir, ".gitignore"), "w+"
              ) as gitignore:
                  if "step_*" not in gitignore:
                      gitignore.write("step_*\n")
                  if "epoch_*" not in gitignore:
                      gitignore.write("epoch_*\n")
          elif args.output_dir is not None:
              os.makedirs(args.output_dir, exist_ok=True)
      accelerator.wait_for_everyone()
  
      # 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 for token classification task 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 'tokens' or the first column if no column called
      # 'tokens' 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 args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          raw_datasets = load_dataset(
              args.dataset_name, args.dataset_config_name
          )
      else:
          data_files = {}
          if args.train_file is not None:
              data_files["train"] = args.train_file
          if args.validation_file is not None:
              data_files["validation"] = args.validation_file
          extension = args.train_file.split(".")[-1]
          raw_datasets = load_dataset(extension, data_files=data_files)
      # Trim a number of training examples
      if args.debug:
          for split in raw_datasets.keys():
              raw_datasets[split] = raw_datasets[split].select(
                  range(100)
              )
      # 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 raw_datasets["train"] is not None:
          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 args.text_column_name is not None:
          text_column_name = args.text_column_name
      elif "tokens" in column_names:
          text_column_name = "tokens"
      else:
          text_column_name = column_names[0]
  
      if args.label_column_name is not None:
          label_column_name = args.label_column_name
      elif f"{args.task_name}_tags" in column_names:
          label_column_name = f"{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
      #
      # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      if args.config_name:
          config = AutoConfig.from_pretrained(
              args.config_name, num_labels=num_labels
          )
      elif args.model_name_or_path:
          config = AutoConfig.from_pretrained(
              args.model_name_or_path, num_labels=num_labels
          )
      else:
          config = CONFIG_MAPPING[args.model_type]()
          logger.warning(
              "You are instantiating a new config instance from scratch."
          )
  
      tokenizer_name_or_path = (
          args.tokenizer_name
          if args.tokenizer_name
          else args.model_name_or_path
      )
      if not tokenizer_name_or_path:
          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 config.model_type in {"bloom", "gpt2", "roberta"}:
          tokenizer = AutoTokenizer.from_pretrained(
              tokenizer_name_or_path,
              use_fast=True,
              add_prefix_space=True,
          )
      else:
          tokenizer = AutoTokenizer.from_pretrained(
              tokenizer_name_or_path, use_fast=True
          )
  
      if args.model_name_or_path:
          model = AutoModelForTokenClassification.from_pretrained(
              args.model_name_or_path,
              from_tf=bool(".ckpt" in args.model_name_or_path),
              config=config,
              ignore_mismatched_sizes=args.ignore_mismatched_sizes,
          )
      else:
          logger.info("Training new model from scratch")
          model = AutoModelForTokenClassification.from_config(config)
  
      model.resize_token_embeddings(len(tokenizer))
  
      # 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 datasets.
      # First we tokenize all the texts.
      padding = "max_length" if 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],
              max_length=args.max_length,
              padding=padding,
              truncation=True,
              # 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 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
  
      with accelerator.main_process_first():
          processed_raw_datasets = raw_datasets.map(
              tokenize_and_align_labels,
              batched=True,
              remove_columns=raw_datasets["train"].column_names,
              desc="Running tokenizer on dataset",
          )
  
      train_dataset = processed_raw_datasets["train"]
      eval_dataset = processed_raw_datasets["validation"]
  
      # Log a few random samples from the training set:
      for index in random.sample(range(len(train_dataset)), 3):
          logger.info(
              f"Sample {index} of the training set: {train_dataset[index]}."
          )
  
      # DataLoaders creation:
      if args.pad_to_max_length:
          # If padding was already done ot max length, we use the default data collator that will just convert everything
          # to tensors.
          data_collator = default_data_collator
      else:
          # Otherwise, `DataCollatorForTokenClassification` will apply dynamic padding for us (by padding to the maximum length of
          # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
          # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
          data_collator = DataCollatorForTokenClassification(
              tokenizer,
              pad_to_multiple_of=(8 if accelerator.use_fp16 else None),
          )
  
      train_dataloader = DataLoader(
          train_dataset,
          shuffle=True,
          collate_fn=data_collator,
          batch_size=args.per_device_train_batch_size,
      )
      eval_dataloader = DataLoader(
          eval_dataset,
          collate_fn=data_collator,
          batch_size=args.per_device_eval_batch_size,
      )
  
      # Optimizer
      # Split weights in two groups, one with weight decay and the other not.
      no_decay = ["bias", "LayerNorm.weight"]
      optimizer_grouped_parameters = [
          {
              "params": [
                  p
                  for n, p in model.named_parameters()
                  if not any(nd in n for nd in no_decay)
              ],
              "weight_decay": args.weight_decay,
          },
          {
              "params": [
                  p
                  for n, p in model.named_parameters()
                  if any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.0,
          },
      ]
      optimizer = torch.optim.AdamW(
          optimizer_grouped_parameters, lr=args.learning_rate
      )
  
      # Use the device given by the `accelerator` object.
      device = accelerator.device
      model.to(device)
  
      # Scheduler and math around the number of training steps.
      overrode_max_train_steps = False
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if args.max_train_steps is None:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
          overrode_max_train_steps = True
  
      lr_scheduler = get_scheduler(
          name=args.lr_scheduler_type,
          optimizer=optimizer,
          num_warmup_steps=args.num_warmup_steps,
          num_training_steps=args.max_train_steps,
      )
  
      # Prepare everything with our `accelerator`.
      (
          model,
          optimizer,
          train_dataloader,
          eval_dataloader,
          lr_scheduler,
      ) = accelerator.prepare(
          model,
          optimizer,
          train_dataloader,
          eval_dataloader,
          lr_scheduler,
      )
  
      # We need to recalculate our total training steps as the size of the training dataloader may have changed.
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if overrode_max_train_steps:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
      # Afterwards we recalculate our number of training epochs
      args.num_train_epochs = math.ceil(
          args.max_train_steps / num_update_steps_per_epoch
      )
  
      # Figure out how many steps we should save the Accelerator states
      checkpointing_steps = args.checkpointing_steps
      if (
          checkpointing_steps is not None
          and checkpointing_steps.isdigit()
      ):
          checkpointing_steps = int(checkpointing_steps)
  
      # We need to initialize the trackers we use, and also store our configuration.
      # The trackers initializes automatically on the main process.
      if args.with_tracking:
          experiment_config = vars(args)
          # TensorBoard cannot log Enums, need the raw value
          experiment_config["lr_scheduler_type"] = experiment_config[
              "lr_scheduler_type"
          ].value
          accelerator.init_trackers("ner_no_trainer", experiment_config)
  
      # Metrics
      metric = evaluate.load("seqeval")
  
      def get_labels(predictions, references):
          # Transform predictions and references tensos to numpy arrays
          if device.type == "cpu":
              y_pred = predictions.detach().clone().numpy()
              y_true = references.detach().clone().numpy()
          else:
              y_pred = predictions.detach().cpu().clone().numpy()
              y_true = references.detach().cpu().clone().numpy()
  
          # Remove ignored index (special tokens)
          true_predictions = [
              [
                  label_list[p]
                  for (p, l) in zip(pred, gold_label)
                  if l != -100
              ]
              for pred, gold_label in zip(y_pred, y_true)
          ]
          true_labels = [
              [
                  label_list[l]
                  for (p, l) in zip(pred, gold_label)
                  if l != -100
              ]
              for pred, gold_label in zip(y_pred, y_true)
          ]
          return true_predictions, true_labels
  
      def compute_metrics():
          results = metric.compute()
          if 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"],
              }
  
      # Train!
      total_batch_size = (
          args.per_device_train_batch_size
          * accelerator.num_processes
          * args.gradient_accumulation_steps
      )
  
      logger.info("***** Running training *****")
      logger.info(f"  Num examples = {len(train_dataset)}")
      logger.info(f"  Num Epochs = {args.num_train_epochs}")
      logger.info(
          f"  Instantaneous batch size per device = {args.per_device_train_batch_size}"
      )
      logger.info(
          f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
      )
      logger.info(
          f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}"
      )
      logger.info(
          f"  Total optimization steps = {args.max_train_steps}"
      )
      # Only show the progress bar once on each machine.
      progress_bar = tqdm(
          range(args.max_train_steps),
          disable=not accelerator.is_local_main_process,
      )
      completed_steps = 0
      starting_epoch = 0
      # Potentially load in the weights and states from a previous save
      if args.resume_from_checkpoint:
          if (
              args.resume_from_checkpoint is not None
              or args.resume_from_checkpoint != ""
          ):
              accelerator.print(
                  f"Resumed from checkpoint: {args.resume_from_checkpoint}"
              )
              accelerator.load_state(args.resume_from_checkpoint)
              path = os.path.basename(args.resume_from_checkpoint)
          else:
              # Get the most recent checkpoint
              dirs = [
                  f.name for f in os.scandir(os.getcwd()) if f.is_dir()
              ]
              dirs.sort(key=os.path.getctime)
              path = dirs[
                  -1
              ]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          training_difference = os.path.splitext(path)[0]
  
          if "epoch" in training_difference:
              starting_epoch = (
                  int(training_difference.replace("epoch_", "")) + 1
              )
              resume_step = None
          else:
              resume_step = int(
                  training_difference.replace("step_", "")
              )
              starting_epoch = resume_step // len(train_dataloader)
              resume_step -= starting_epoch * len(train_dataloader)
  
      for epoch in range(starting_epoch, args.num_train_epochs):
          model.train()
          if args.with_tracking:
              total_loss = 0
          for step, batch in enumerate(train_dataloader):
              # We need to skip steps until we reach the resumed step
              if (
                  args.resume_from_checkpoint
                  and epoch == starting_epoch
              ):
                  if resume_step is not None and step < resume_step:
                      completed_steps += 1
                      continue
              outputs = model(**batch)
              loss = outputs.loss
              # We keep track of the loss at each epoch
              if args.with_tracking:
                  total_loss += loss.detach().float()
              loss = loss / args.gradient_accumulation_steps
              accelerator.backward(loss)
              if (
                  step % args.gradient_accumulation_steps == 0
                  or step == len(train_dataloader) - 1
              ):
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
                  progress_bar.update(1)
                  completed_steps += 1
  
              if isinstance(checkpointing_steps, int):
                  if completed_steps % checkpointing_steps == 0:
                      output_dir = f"step_{completed_steps }"
                      if args.output_dir is not None:
                          output_dir = os.path.join(
                              args.output_dir, output_dir
                          )
                      accelerator.save_state(output_dir)
  
              if completed_steps >= args.max_train_steps:
                  break
  
          model.eval()
          samples_seen = 0
          for step, batch in enumerate(eval_dataloader):
              with torch.no_grad():
                  outputs = model(**batch)
              predictions = outputs.logits.argmax(dim=-1)
              labels = batch["labels"]
              if (
                  not args.pad_to_max_length
              ):  # necessary to pad predictions and labels for being gathered
                  predictions = accelerator.pad_across_processes(
                      predictions, dim=1, pad_index=-100
                  )
                  labels = accelerator.pad_across_processes(
                      labels, dim=1, pad_index=-100
                  )
              (
                  predictions_gathered,
                  labels_gathered,
              ) = accelerator.gather((predictions, labels))
              # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.num_processes > 1:
                  if step == len(eval_dataloader) - 1:
                      predictions_gathered = predictions_gathered[
                          : len(eval_dataloader.dataset) - samples_seen
                      ]
                      labels_gathered = labels_gathered[
                          : len(eval_dataloader.dataset) - samples_seen
                      ]
                  else:
                      samples_seen += labels_gathered.shape[0]
              preds, refs = get_labels(
                  predictions_gathered, labels_gathered
              )
              metric.add_batch(
                  predictions=preds,
                  references=refs,
              )  # predictions and preferences are expected to be a nested list of labels, not label_ids
  
          eval_metric = compute_metrics()
          accelerator.print(f"epoch {epoch}:", eval_metric)
          if args.with_tracking:
              accelerator.log(
                  {
                      "seqeval": eval_metric,
                      "train_loss": total_loss.item()
                      / len(train_dataloader),
                      "epoch": epoch,
                      "step": completed_steps,
                  },
                  step=completed_steps,
              )
  
          if args.push_to_hub and epoch < args.num_train_epochs - 1:
              accelerator.wait_for_everyone()
              unwrapped_model = accelerator.unwrap_model(model)
              unwrapped_model.save_pretrained(
                  args.output_dir,
                  is_main_process=accelerator.is_main_process,
                  save_function=accelerator.save,
              )
              if accelerator.is_main_process:
                  tokenizer.save_pretrained(args.output_dir)
                  repo.push_to_hub(
                      commit_message=f"Training in progress epoch {epoch}",
                      blocking=False,
                      auto_lfs_prune=True,
                  )
  
          if args.checkpointing_steps == "epoch":
              output_dir = f"epoch_{epoch}"
              if args.output_dir is not None:
                  output_dir = os.path.join(args.output_dir, output_dir)
              accelerator.save_state(output_dir)
  
      if args.with_tracking:
          accelerator.end_training()
  
      if args.output_dir is not None:
          accelerator.wait_for_everyone()
          unwrapped_model = accelerator.unwrap_model(model)
          unwrapped_model.save_pretrained(
              args.output_dir,
              is_main_process=accelerator.is_main_process,
              save_function=accelerator.save,
          )
          if accelerator.is_main_process:
              tokenizer.save_pretrained(args.output_dir)
              if args.push_to_hub:
                  repo.push_to_hub(
                      commit_message="End of training",
                      auto_lfs_prune=True,
                  )
  
          with open(
              os.path.join(args.output_dir, "all_results.json"), "w"
          ) as f:
              json.dump(
                  {
                      "eval_accuracy": eval_metric["accuracy"],
                      "train_loss": total_loss.item()
                      / len(train_dataloader),
                  },
                  f,
              )
  
imports: import argparse
  import json
  import logging
  import math
  import os
  import random
  from pathlib import Path
  import datasets
  import torch
  from datasets import ClassLabel, load_dataset
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  import evaluate
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from huggingface_hub import Repository
  from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForTokenClassification, AutoTokenizer, DataCollatorForTokenClassification, PretrainedConfig, SchedulerType, default_data_collator, get_scheduler
  from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
  from transformers.utils.versions import require_version
label_desc: Use image node_id 2 for calling a global function with example usage: main() and returns: label_list, tokenized_inputs, true_predictions,  true_labels, final_results, dict