text: #!/usr/bin/env python
  # coding=utf-8
  # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """
  Fine-tuning a 🤗 Transformers model on token classification tasks (NER, POS, CHUNKS) relying on the accelerate library
  without using a Trainer.
  """
  
  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
  from huggingface_hub import Repository
  from transformers import (
      CONFIG_MAPPING,
      MODEL_MAPPING,
      AdamW,
      AutoConfig,
      AutoModelForTokenClassification,
      AutoTokenizer,
      DataCollatorForTokenClassification,
      PretrainedConfig,
      SchedulerType,
      default_data_collator,
      get_scheduler,
      set_seed,
  )
  from transformers.utils import get_full_repo_name
  from transformers.utils.versions import require_version
  
  
  logger = logging.getLogger(__name__)
  require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
  
  # You should update this to your particular problem to have better documentation of `model_type`
  MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
  MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
  
  
  def parse_args():
      parser = argparse.ArgumentParser(
          description="Finetune a transformers model on a text classification task (NER) with accelerate library"
      )
      parser.add_argument(
          "--dataset_name",
          type=str,
          default=None,
          help="The name of the dataset to use (via the datasets library).",
      )
      parser.add_argument(
          "--dataset_config_name",
          type=str,
          default=None,
          help="The configuration name of the dataset to use (via the datasets library).",
      )
      parser.add_argument(
          "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
      )
      parser.add_argument(
          "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
      )
      parser.add_argument(
          "--text_column_name",
          type=str,
          default=None,
          help="The column name of text to input in the file (a csv or JSON file).",
      )
      parser.add_argument(
          "--label_column_name",
          type=str,
          default=None,
          help="The column name of label to input in the file (a csv or JSON file).",
      )
      parser.add_argument(
          "--max_length",
          type=int,
          default=128,
          help=(
              "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
              " sequences shorter will be padded if `--pad_to_max_length` is passed."
          ),
      )
      parser.add_argument(
          "--pad_to_max_length",
          action="store_true",
          help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
          required=True,
      )
      parser.add_argument(
          "--config_name",
          type=str,
          default=None,
          help="Pretrained config name or path if not the same as model_name",
      )
      parser.add_argument(
          "--tokenizer_name",
          type=str,
          default=None,
          help="Pretrained tokenizer name or path if not the same as model_name",
      )
      parser.add_argument(
          "--per_device_train_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--per_device_eval_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the evaluation dataloader.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=5e-5,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
      parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
      parser.add_argument(
          "--max_train_steps",
          type=int,
          default=None,
          help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
      )
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="Number of updates steps to accumulate before performing a backward/update pass.",
      )
      parser.add_argument(
          "--lr_scheduler_type",
          type=SchedulerType,
          default="linear",
          help="The scheduler type to use.",
          choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
      )
      parser.add_argument(
          "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
      )
      parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
      parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
      parser.add_argument(
          "--model_type",
          type=str,
          default=None,
          help="Model type to use if training from scratch.",
          choices=MODEL_TYPES,
      )
      parser.add_argument(
          "--label_all_tokens",
          action="store_true",
          help="Setting labels of all special tokens to -100 and thus PyTorch will ignore them.",
      )
      parser.add_argument(
          "--return_entity_level_metrics",
          action="store_true",
          help="Indication whether entity level metrics are to be returner.",
      )
      parser.add_argument(
          "--task_name",
          type=str,
          default="ner",
          choices=["ner", "pos", "chunk"],
          help="The name of the task.",
      )
      parser.add_argument(
          "--debug",
          action="store_true",
          help="Activate debug mode and run training only with a subset of data.",
      )
      parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
      parser.add_argument(
          "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
      )
      parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
      parser.add_argument(
          "--checkpointing_steps",
          type=str,
          default=None,
          help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
      )
      parser.add_argument(
          "--resume_from_checkpoint",
          type=str,
          default=None,
          help="If the training should continue from a checkpoint folder.",
      )
      parser.add_argument(
          "--with_tracking",
          required=False,
          help="Whether to load in all available experiment trackers from the environment and use them for logging.",
      )
      args = parser.parse_args()
  
      # Sanity checks
      if args.task_name is None and args.train_file is None and args.validation_file is None:
          raise ValueError("Need either a task name or a training/validation file.")
      else:
          if args.train_file is not None:
              extension = args.train_file.split(".")[-1]
              assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
          if args.validation_file is not None:
              extension = args.validation_file.split(".")[-1]
              assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
  
      if args.push_to_hub:
          assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
  
      return args
  
  
  def main():
      args = parse_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 pick up all supported trackers in the environment
      accelerator = Accelerator(log_with="all") 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)
  
      # 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 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 {"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,
          )
      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: {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 = 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.
      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,
      )
  
      # Prepare everything with our `accelerator`.
      model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
          model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
      )
  
      # Figure out how many steps we should save the Accelerator states
      if hasattr(args.checkpointing_steps, "isdigit"):
          checkpointing_steps = args.checkpointing_steps
          if args.checkpointing_steps.isdigit():
              checkpointing_steps = int(args.checkpointing_steps)
      else:
          checkpointing_steps = None
  
      # We need to initialize the trackers we use, and also store our configuration
      if args.with_tracking:
          accelerator.init_trackers("clm_no_trainer", args)
  
      # Metrics
      metric = load_metric("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
      # 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)
              resume_step = None
              path = 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
          if "epoch" in path:
              args.num_train_epochs -= int(path.replace("epoch_", ""))
          else:
              resume_step = int(path.replace("step_", ""))
              args.num_train_epochs -= resume_step // len(train_dataloader)
              resume_step = (args.num_train_epochs * len(train_dataloader)) - resume_step
  
      for epoch in range(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 == 0 and step < resume_step:
                  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:
                      accelerator.save_state(f"step_{completed_steps}")
  
              if completed_steps >= args.max_train_steps:
                  break
  
          model.eval()
          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 = accelerator.gather(predictions)
              labels_gathered = accelerator.gather(labels)
              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 = metric.compute()
          eval_metric = compute_metrics()
          accelerator.print(f"epoch {epoch}:", eval_metric)
          if args.with_tracking:
              accelerator.log(
                  {
                      "seqeval": eval_metric,
                      "train_loss": total_loss,
                      "epoch": epoch,
                  },
                  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, 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":
              accelerator.save_state(f"epoch_{epoch}")
  
      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)
  
  
  if __name__ == "__main__":
      main()
  