usrbinenv python codingutf8 the huggingface team and the huggingface inc team 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 orglicenseslicense2 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 finetuning a transformers model on multiple choice relying on the accelerate library without using a trainer you can also adapt this script on your own multiple choice task pointers for this are left as comments will error if the minimal version of transformers is not installed remove at your own risks you should update this to your particular problem to have better documentation of modeltype data collator that will dynamically pad the inputs for multiple choice received args tokenizer pretrainedtokenizer or pretrainedtokenizerfast the tokenizer used for encoding the data padding bool str or utils paddingstrategy optional defaults to true select a strategy to pad the returned sequences according to the model s padding side and padding index among true or longest pad to the longest sequence in the batch or no padding if only a single sequence if provided maxlength pad to a maximum length specified with the argument maxlength or to the maximum acceptable input length for the model if that argument is not provided false or donotpad default no padding i e can output a batch with sequences of different lengths maxlength int optional maximum length of the returned list and optionally padding length see above padtomultipleof int optional if set will pad the sequence to a multiple of the provided value this is especially useful to enable the use of tensor cores on nvidia hardware with compute capability 7 5 volta unflatten add back labels 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 pythonpytorch versions 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 make one log on every process with the configuration for debugging if passed along set the training seed now handle the repository creation retrieve of infer reponame create repo and retrieve repoid clone repo locally get the datasets you can either provide your own csvjsontxt training and evaluation files see below or just provide the name of one of the public datasets available on the hub at https huggingface codatasets the dataset will be downloaded automatically from the datasets hub for csvjson files this script will use the column called text or the first column if no column called text is found you can easily tweak this behavior see below in distributed training the loaddataset function guarantee that only one local process can concurrently download the dataset downloading and loading a dataset from the hub trim a number of training examples see more about loading any type of standard or custom dataset from files python dict pandas dataframe etc at https huggingface codocsdatasetsloadingdatasets when using your own dataset or a different dataset from swag you will probably need to change this load pretrained model and tokenizer in distributed training the frompretrained methods guarantee that only one local process can concurrently download model vocab we resize the embeddings only when necessary to avoid index errors if you are creating a model from scratch on a small vocab and want a smaller embedding size remove this test preprocessing the datasets first we tokenize all the texts flatten out tokenize unflatten log a few random samples from the training set dataloaders creation if padding was already done ot max length we use the default data collator that will just convert everything to tensors otherwise datacollatorwithpadding will apply dynamic padding for us by padding to the maximum length of the samples passed when using mixed precision we add padtomultipleof8 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 optimizer split weights in two groups one with weight decay and the other not use the device given by the accelerator object scheduler and math around the number of training steps prepare everything with our accelerator we need to recalculate our total training steps as the size of the training dataloader may have changed afterwards we recalculate our number of training epochs figure out how many steps we should save the accelerator states we need to initialize the trackers we use and also store our configuration the trackers initializes automatically on the main process tensorboard cannot log enums need the raw value metrics train only show the progress bar once on each machine potentially load in the weights and states from a previous save get the most recent checkpoint extract epochi or stepi need to multiply gradientaccumulationsteps to reflect real steps update the progressbar if load from checkpoint we skip the first n batches in the dataloader when resuming from a checkpoint we keep track of the loss at each epoch checks if the accelerator has performed an optimization step behind the scenes usr bin env python coding utf 8 the huggingface team and the huggingface inc team 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 multiple choice relying on the accelerate library without using a trainer you can also adapt this script on your own multiple choice task pointers for this are left as comments will error if the minimal version of transformers is not installed remove at your own risks you should update this to your particular problem to have better documentation of model_type data collator that will dynamically pad the inputs for multiple choice received args tokenizer pretrainedtokenizer or pretrainedtokenizerfast the tokenizer used for encoding the data padding bool str or utils paddingstrategy optional defaults to true select a strategy to pad the returned sequences according to the model s padding side and padding index among true or longest pad to the longest sequence in the batch or no padding if only a single sequence if provided max_length pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided false or do_not_pad default no padding i e can output a batch with sequences of different lengths max_length int optional maximum length of the returned list and optionally padding length see above pad_to_multiple_of int optional if set will pad the sequence to a multiple of the provided value this is especially useful to enable the use of tensor cores on nvidia hardware with compute capability 7 5 volta un flatten add back labels 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 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 make one log on every process with the configuration for debugging if passed along set the training seed now handle the repository creation retrieve of infer repo_name create repo and retrieve repo_id clone repo locally get the datasets you can either provide your own csv json txt training and evaluation files see below or just provide the name of one of the public datasets available on the hub at https huggingface co datasets the dataset will be downloaded automatically from the datasets hub for csv json files this script will use the column called text or the first column if no column called text is found you can easily tweak this behavior see below in distributed training the load_dataset function guarantee that only one local process can concurrently download the dataset downloading and loading a dataset from the hub trim a number of training examples 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 when using your own dataset or a different dataset from swag you will probably need to change this load pretrained model and tokenizer in distributed training the from_pretrained methods guarantee that only one local process can concurrently download model vocab we resize the embeddings only when necessary to avoid index errors if you are creating a model from scratch on a small vocab and want a smaller embedding size remove this test preprocessing the datasets first we tokenize all the texts flatten out tokenize un flatten log a few random samples from the training set dataloaders creation if padding was already done ot max length we use the default data collator that will just convert everything to tensors otherwise datacollatorwithpadding 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 optimizer split weights in two groups one with weight decay and the other not use the device given by the accelerator object scheduler and math around the number of training steps prepare everything with our accelerator we need to recalculate our total training steps as the size of the training dataloader may have changed afterwards we recalculate our number of training epochs figure out how many steps we should save the accelerator states we need to initialize the trackers we use and also store our configuration the trackers initializes automatically on the main process tensorboard cannot log enums need the raw value metrics train only show the progress bar once on each machine potentially load in the weights and states from a previous save get the most recent checkpoint sorts folders by date modified most recent checkpoint is the last extract epoch_ i or step_ i need to multiply gradient_accumulation_steps to reflect real steps update the progress_bar if load from checkpoint we skip the first n batches in the dataloader when resuming from a checkpoint we keep track of the loss at each epoch checks if the accelerator has performed an optimization step behind the scenes
import argparse
import json
import logging
import math
import os
import random
from dataclasses import dataclass
from itertools import chain
from pathlib import Path
from typing import Optional, Union

import datasets
import evaluate
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import load_dataset
from huggingface_hub import Repository, create_repo
from torch.utils.data import DataLoader
from tqdm.auto import tqdm

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_MAPPING,
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    PreTrainedTokenizerBase,
    SchedulerType,
    default_data_collator,
    get_scheduler,
)
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry



check_min_version("4.36.0.dev0")

logger = get_logger(__name__)

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 multiple choice task")
    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(
        "--max_seq_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_lengh` 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=False,
    )
    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(
        "--use_slow_tokenizer",
        action="store_true",
        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
    )
    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(
        "--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(
        "--trust_remote_code",
        type=bool,
        default=False,
        help=(
            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
            "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
            "execute code present on the Hub on your local machine."
        ),
    )
    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",
        action="store_true",
        help="Whether to enable experiment trackers for logging.",
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="all",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
            ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
            "Only applicable when `--with_tracking` is passed."
        ),
    )
    args = parser.parse_args()

    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


@dataclass
class DataCollatorForMultipleChoice:
    

    tokenizer: PreTrainedTokenizerBase
    padding: Union[bool, str, PaddingStrategy] = True
    max_length: Optional[int] = None
    pad_to_multiple_of: Optional[int] = None

    def __call__(self, features):
        label_name = "label" if "label" in features[0].keys() else "labels"
        labels = [feature.pop(label_name) for feature in features]
        batch_size = len(features)
        num_choices = len(features[0]["input_ids"])
        flattened_features = [
            [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
        ]
        flattened_features = list(chain(*flattened_features))

        batch = self.tokenizer.pad(
            flattened_features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )

        
        batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
        
        batch["labels"] = torch.tensor(labels, dtype=torch.int64)
        return batch


def main():
    args = parse_args()

    
    
    send_example_telemetry("run_swag_no_trainer", args)

    
    
    
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)

    
    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 args.seed is not None:
        set_seed(args.seed)

    
    if accelerator.is_main_process:
        if args.push_to_hub:
            
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            
            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
            
            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)

            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()

    
    
    
    
    
    
    
    
    
    if args.dataset_name is not None:
        
        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)
    
    if args.debug:
        for split in raw_datasets.keys():
            raw_datasets[split] = raw_datasets[split].select(range(100))
    
    

    if raw_datasets["train"] is not None:
        column_names = raw_datasets["train"].column_names
    else:
        column_names = raw_datasets["validation"].column_names

    
    ending_names = [f"ending{i}" for i in range(4)]
    context_name = "sent1"
    question_header_name = "sent2"
    label_column_name = "label" if "label" in column_names else "labels"

    
    
    
    
    if args.config_name:
        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
    elif args.model_name_or_path:
        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
    else:
        config = CONFIG_MAPPING[args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")

    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
        )
    elif args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
        )
    else:
        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 args.model_name_or_path:
        model = AutoModelForMultipleChoice.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            trust_remote_code=args.trust_remote_code,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForMultipleChoice.from_config(config, trust_remote_code=args.trust_remote_code)

    
    
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))

    
    
    padding = "max_length" if args.pad_to_max_length else False

    def preprocess_function(examples):
        first_sentences = [[context] * 4 for context in examples[context_name]]
        question_headers = examples[question_header_name]
        second_sentences = [
            [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
        ]
        labels = examples[label_column_name]

        
        first_sentences = list(chain(*first_sentences))
        second_sentences = list(chain(*second_sentences))

        
        tokenized_examples = tokenizer(
            first_sentences,
            second_sentences,
            max_length=args.max_seq_length,
            padding=padding,
            truncation=True,
        )
        
        tokenized_inputs = {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
        tokenized_inputs["labels"] = labels
        return tokenized_inputs

    with accelerator.main_process_first():
        processed_datasets = raw_datasets.map(
            preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names
        )

    train_dataset = processed_datasets["train"]
    eval_dataset = processed_datasets["validation"]

    
    for index in random.sample(range(len(train_dataset)), 3):
        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")

    
    if args.pad_to_max_length:
        
        
        data_collator = default_data_collator
    else:
        
        
        
        data_collator = DataCollatorForMultipleChoice(
            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)

    
    
    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)

    
    device = accelerator.device
    model.to(device)

    
    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 * args.gradient_accumulation_steps,
        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
    )

    
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
    )

    
    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
    
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    
    checkpointing_steps = args.checkpointing_steps
    if checkpointing_steps is not None and checkpointing_steps.isdigit():
        checkpointing_steps = int(checkpointing_steps)

    
    
    if args.with_tracking:
        experiment_config = vars(args)
        
        experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
        accelerator.init_trackers("swag_no_trainer", experiment_config)

    
    metric = evaluate.load("accuracy")

    
    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}")
    
    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
    completed_steps = 0
    starting_epoch = 0

    
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            checkpoint_path = args.resume_from_checkpoint
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            
            dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
            dirs.sort(key=os.path.getctime)
            path = dirs[-1]  
            checkpoint_path = path
            path = os.path.basename(checkpoint_path)

        accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
        accelerator.load_state(checkpoint_path)
        
        training_difference = os.path.splitext(path)[0]

        if "epoch" in training_difference:
            starting_epoch = int(training_difference.replace("epoch_", "")) + 1
            resume_step = None
            completed_steps = starting_epoch * num_update_steps_per_epoch
        else:
            
            resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
            starting_epoch = resume_step // len(train_dataloader)
            completed_steps = resume_step // args.gradient_accumulation_steps
            resume_step -= starting_epoch * len(train_dataloader)

    
    progress_bar.update(completed_steps)

    for epoch in range(starting_epoch, args.num_train_epochs):
        model.train()
        if args.with_tracking:
            total_loss = 0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            
            active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
        else:
            active_dataloader = train_dataloader
        for step, batch in enumerate(active_dataloader):
            with accelerator.accumulate(model):
                outputs = model(**batch)
                loss = outputs.loss
                
                if args.with_tracking:
                    total_loss += loss.detach().float()
                accelerator.backward(loss)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            
            if accelerator.sync_gradients:
                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()
        for step, batch in enumerate(eval_dataloader):
            with torch.no_grad():
                outputs = model(**batch)
            predictions = outputs.logits.argmax(dim=-1)
            predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=predictions,
                references=references,
            )

        eval_metric = metric.compute()
        accelerator.print(f"epoch {epoch}: {eval_metric}")

        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": 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)

            all_results = {f"eval_{k}": v for k, v in eval_metric.items()}
            with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
                json.dump(all_results, f)


if __name__ == "__main__":
    main()