code: def main():
      args = parse_args()
  
      # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
      handler = DistributedDataParallelKwargs(
          find_unused_parameters=True
      )
      accelerator = Accelerator(kwargs_handlers=[handler])
      # 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)
  
      # Setup logging, we only want one process per machine to log things on the screen.
      # accelerator.is_local_main_process is only True for one process per machine.
      logger.setLevel(
          logging.INFO
          if accelerator.is_local_main_process
          else logging.ERROR
      )
      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)
          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 isinstance(features[label_column_name].feature, ClassLabel):
          label_list = features[label_column_name].feature.names
          # No need to convert the labels since they are already ints.
      else:
          label_list = get_label_list(
              raw_datasets["train"][label_column_name]
          )
      num_labels = len(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)
  
      # 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 = LukeConfig.from_pretrained(
              args.config_name, num_labels=num_labels
          )
      elif args.model_name_or_path:
          config = LukeConfig.from_pretrained(
              args.model_name_or_path, num_labels=num_labels
          )
      else:
          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."
          )
  
      tokenizer = LukeTokenizer.from_pretrained(
          tokenizer_name_or_path,
          use_fast=False,
          task="entity_span_classification",
          max_entity_length=args.max_entity_length,
          max_mention_length=args.max_mention_length,
      )
  
      if args.model_name_or_path:
          model = LukeForEntitySpanClassification.from_pretrained(
              args.model_name_or_path,
              from_tf=bool(".ckpt" in args.model_name_or_path),
              config=config,
          )
      else:
          logger.info("Training new model from scratch")
          model = LukeForEntitySpanClassification.from_config(config)
  
      model.resize_token_embeddings(len(tokenizer))
  
      # Preprocessing the datasets.
      # First we tokenize all the texts.
      padding = "max_length" if args.pad_to_max_length else False
  
      def compute_sentence_boundaries_for_luke(examples):
          sentence_boundaries = []
  
          for tokens in examples[text_column_name]:
              sentence_boundaries.append([0, len(tokens)])
  
          examples["sentence_boundaries"] = sentence_boundaries
  
          return examples
  
      def compute_entity_spans_for_luke(examples):
          all_entity_spans = []
          texts = []
          all_labels_entity_spans = []
          all_original_entity_spans = []
  
          for labels, tokens, sentence_boundaries in zip(
              examples[label_column_name],
              examples[text_column_name],
              examples["sentence_boundaries"],
          ):
              subword_lengths = [
                  len(tokenizer.tokenize(token)) for token in tokens
              ]
              total_subword_length = sum(subword_lengths)
              _, context_end = sentence_boundaries
  
              if total_subword_length > args.max_length - 2:
                  cur_length = sum(subword_lengths[:context_end])
                  idx = context_end - 1
  
                  while cur_length > args.max_length - 2:
                      cur_length -= subword_lengths[idx]
                      context_end -= 1
                      idx -= 1
  
              text = ""
              sentence_words = tokens[:context_end]
              sentence_subword_lengths = subword_lengths[:context_end]
              word_start_char_positions = []
              word_end_char_positions = []
              labels_positions = {}
  
              for word, label in zip(sentence_words, labels):
                  if word[0] == "'" or (
                      len(word) == 1 and is_punctuation(word)
                  ):
                      text = text.rstrip()
  
                  word_start_char_positions.append(len(text))
                  text += word
                  word_end_char_positions.append(len(text))
                  text += " "
                  labels_positions[
                      (
                          word_start_char_positions[-1],
                          word_end_char_positions[-1],
                      )
                  ] = label
  
              text = text.rstrip()
              texts.append(text)
              entity_spans = []
              labels_entity_spans = []
              original_entity_spans = []
  
              for word_start in range(len(sentence_words)):
                  for word_end in range(
                      word_start, len(sentence_words)
                  ):
                      if (
                          sum(
                              sentence_subword_lengths[
                                  word_start:word_end
                              ]
                          )
                          <= tokenizer.max_mention_length
                          and len(entity_spans)
                          < tokenizer.max_entity_length
                      ):
                          entity_spans.append(
                              (
                                  word_start_char_positions[word_start],
                                  word_end_char_positions[word_end],
                              )
                          )
                          original_entity_spans.append(
                              (word_start, word_end + 1)
                          )
                          if (
                              word_start_char_positions[word_start],
                              word_end_char_positions[word_end],
                          ) in labels_positions:
                              labels_entity_spans.append(
                                  labels_positions[
                                      (
                                          word_start_char_positions[
                                              word_start
                                          ],
                                          word_end_char_positions[
                                              word_end
                                          ],
                                      )
                                  ]
                              )
                          else:
                              labels_entity_spans.append(0)
  
              all_entity_spans.append(entity_spans)
              all_labels_entity_spans.append(labels_entity_spans)
              all_original_entity_spans.append(original_entity_spans)
  
          examples["entity_spans"] = all_entity_spans
          examples["text"] = texts
          examples["labels_entity_spans"] = all_labels_entity_spans
          examples["original_entity_spans"] = all_original_entity_spans
  
          return examples
  
      def tokenize_and_align_labels(examples):
          entity_spans = []
  
          for v in examples["entity_spans"]:
              entity_spans.append(list(map(tuple, v)))
  
          tokenized_inputs = tokenizer(
              examples["text"],
              entity_spans=entity_spans,
              max_length=args.max_length,
              padding=padding,
              truncation=True,
          )
  
          if padding == "max_length":
              tokenized_inputs["labels"] = padding_tensor(
                  examples["labels_entity_spans"],
                  -100,
                  tokenizer.padding_side,
                  tokenizer.max_entity_length,
              )
              tokenized_inputs[
                  "original_entity_spans"
              ] = padding_tensor(
                  examples["original_entity_spans"],
                  (-1, -1),
                  tokenizer.padding_side,
                  tokenizer.max_entity_length,
              )
              tokenized_inputs[label_column_name] = padding_tensor(
                  examples[label_column_name],
                  -1,
                  tokenizer.padding_side,
                  tokenizer.max_entity_length,
              )
          else:
              tokenized_inputs["labels"] = [
                  ex[: tokenizer.max_entity_length]
                  for ex in examples["labels_entity_spans"]
              ]
              tokenized_inputs["original_entity_spans"] = [
                  ex[: tokenizer.max_entity_length]
                  for ex in examples["original_entity_spans"]
              ]
              tokenized_inputs[label_column_name] = [
                  ex[: tokenizer.max_entity_length]
                  for ex in examples[label_column_name]
              ]
  
          return tokenized_inputs
  
      with accelerator.main_process_first():
          raw_datasets = raw_datasets.map(
              compute_sentence_boundaries_for_luke,
              batched=True,
              desc="Adding sentence boundaries",
          )
          raw_datasets = raw_datasets.map(
              compute_entity_spans_for_luke,
              batched=True,
              desc="Adding sentence spans",
          )
  
          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 = DataCollatorForLukeTokenClassification(
              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 = AdamW(
          optimizer_grouped_parameters, lr=args.learning_rate
      )
  
      # Use the device given by the `accelerator` object.
      device = accelerator.device
      model.to(device)
  
      # Prepare everything with our `accelerator`.
      (
          model,
          optimizer,
          train_dataloader,
          eval_dataloader,
      ) = accelerator.prepare(
          model, optimizer, train_dataloader, eval_dataloader
      )
  
      # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
      # shorter in multiprocess)
  
      # Scheduler and math around the number of training steps.
      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
          )
      else:
          args.num_train_epochs = math.ceil(
              args.max_train_steps / num_update_steps_per_epoch
          )
  
      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,
      )
  
      # Metrics
      metric = load_metric("seqeval")
  
      def get_luke_labels(outputs, ner_tags, original_entity_spans):
          true_predictions = []
          true_labels = []
  
          for output, original_spans, tags in zip(
              outputs.logits, original_entity_spans, ner_tags
          ):
              true_tags = [val for val in tags if val != -1]
              true_original_spans = [
                  val for val in original_spans if val != (-1, -1)
              ]
              max_indices = torch.argmax(output, axis=1)
              max_logits = torch.max(output, axis=1).values
              predictions = []
  
              for logit, index, span in zip(
                  max_logits, max_indices, true_original_spans
              ):
                  if index != 0:
                      predictions.append(
                          (logit, span, label_list[index])
                      )
  
              predicted_sequence = [label_list[0]] * len(true_tags)
  
              for _, span, label in sorted(
                  predictions, key=lambda o: o[0], reverse=True
              ):
                  if all(
                      [
                          o == label_list[0]
                          for o in predicted_sequence[span[0] : span[1]]
                      ]
                  ):
                      predicted_sequence[span[0]] = label
                      if span[1] - span[0] > 1:
                          predicted_sequence[span[0] + 1 : span[1]] = [
                              label
                          ] * (span[1] - span[0] - 1)
  
              true_predictions.append(predicted_sequence)
              true_labels.append(
                  [label_list[tag_id] for tag_id in true_tags]
              )
  
          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
  
      for epoch in range(args.num_train_epochs):
          model.train()
          for step, batch in enumerate(train_dataloader):
              _ = batch.pop("original_entity_spans")
              outputs = model(**batch)
              loss = outputs.loss
              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 completed_steps >= args.max_train_steps:
                  break
  
          model.eval()
          for step, batch in enumerate(eval_dataloader):
              original_entity_spans = batch.pop("original_entity_spans")
              with torch.no_grad():
                  outputs = model(**batch)
  
              preds, refs = get_luke_labels(
                  outputs,
                  batch[label_column_name],
                  original_entity_spans,
              )
  
              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.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, 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.output_dir is not None:
          accelerator.wait_for_everyone()
          unwrapped_model = accelerator.unwrap_model(model)
          unwrapped_model.save_pretrained(
              args.output_dir, 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,
                  )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/luke/run_luke_ner_no_trainer.py
imports: import argparse
  import logging
  import math
  import os
  import random
  from pathlib import Path
  import datasets
  import torch
  from datasets import ClassLabel, load_dataset, load_metric
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  import transformers
  from accelerate import Accelerator, DistributedDataParallelKwargs
  from huggingface_hub import Repository
  from luke_utils import DataCollatorForLukeTokenClassification, is_punctuation, padding_tensor
  from transformers import AdamW, LukeConfig, LukeForEntitySpanClassification, LukeTokenizer, SchedulerType, default_data_collator, get_scheduler, set_seed
  from transformers.file_utils import get_full_repo_name
  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, examples, examples, tokenized_inputs, true_predictions,  true_labels, final_results, dict