#!/usr/bin/env python
# coding=utf-8
# Copyright 2022 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 (m)LUKE 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 accelerate import Accelerator, DistributedDataParallelKwargs
from datasets import ClassLabel, load_dataset, load_metric
from huggingface_hub import Repository, create_repo
from luke_utils import DataCollatorForLukeTokenClassification, is_punctuation, padding_tensor
from torch.utils.data import DataLoader
from tqdm.auto import tqdm

import transformers
from transformers import (
    AdamW,
    LukeConfig,
    LukeForEntitySpanClassification,
    LukeTokenizer,
    SchedulerType,
    default_data_collator,
    get_scheduler,
    set_seed,
)
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")


def parse_args():
    parser = argparse.ArgumentParser(
        description="Finetune (m)LUKE on a token classification task (such as NER) with the 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(
        "--max_entity_length",
        type=int,
        default=32,
        help=(
            "The maximum total input entity length after tokenization (Used only for (M)Luke models). Sequences longer"
            " than this will be truncated, sequences shorter will be padded if `--pad_to_max_length` is passed."
        ),
    )
    parser.add_argument(
        "--max_mention_length",
        type=int,
        default=30,
        help=(
            "The maximum total input mention length after tokenization (Used only for (M)Luke models). 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(
        "--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.")
    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.
    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:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
            # Clone repo locally
            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
        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
            extension = args.train_file.split(".")[-1]
        if args.validation_file is not None:
            data_files["validation"] = args.validation_file
            extension = args.validation_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.

    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).
        # For fp8, we pad to multiple of 16.
        if accelerator.mixed_precision == "fp8":
            pad_to_multiple_of = 16
        elif accelerator.mixed_precision != "no":
            pad_to_multiple_of = 8
        else:
            pad_to_multiple_of = None
        data_collator = DataCollatorForLukeTokenClassification(tokenizer, pad_to_multiple_of=pad_to_multiple_of)

    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)


if __name__ == "__main__":
    main()
