usrbinenv python codingutf8 2021 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 token classification tasks ner pos chunks relying on the accelerate library without using a trainer 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 sanity checks 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 for token classification task 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 tokens or the first column if no column called tokens 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 in the event the labels are not a sequenceclasslabel we will need to go through the dataset to get the unique labels 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 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 model has labels use them reorganize labellist to match the ordering of the model set the correspondences labelid inside the model config map that sends bxxx label to its ixxx counterpart preprocessing the datasets first we tokenize all the texts tokenize all texts and align the labels with them we use this argument because the texts in our dataset are lists of words with a label for each word special tokens have a word id that is none we set the label to 100 so they are automatically ignored in the loss function we set the label for the first token of each word for the other tokens in a word we set the label to either the current label or 100 depending on the labelalltokens flag 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 datacollatorfortokenclassification 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 transform predictions and references tensos to numpy arrays remove ignored index special tokens unpack nested dictionaries 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 if we are in a multiprocess environment the last batch has duplicates convert all float64 int64 type numbers to float int for json serialization usr bin env python coding utf 8 2021 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 token classification tasks ner pos chunks relying on the accelerate library without using a trainer 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 sanity checks 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 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 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 in the event the labels are not a sequence classlabel we will need to go through the dataset to get the unique labels 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 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 model has labels use them reorganize label_list to match the ordering of the model set the correspondences label id inside the model config map that sends b xxx label to its i xxx counterpart preprocessing the datasets first we tokenize all the texts tokenize all texts and align the labels with them we use this argument because the texts in our dataset are lists of words with a label for each word special tokens have a word id that is none we set the label to 100 so they are automatically ignored in the loss function we set the label for the first token of each word 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 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 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 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 transform predictions and references tensos to numpy arrays remove ignored index special tokens unpack nested dictionaries 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 necessary to pad predictions and labels for being gathered if we are in a multiprocess environment the last batch has duplicates predictions and preferences are expected to be a nested list of labels not label_ids convert all float64 int64 type numbers to float int for json serialization
import argparse
import json
import logging
import math
import os
import random
from pathlib import Path

import datasets
import evaluate
import numpy as np
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import ClassLabel, 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,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorForTokenClassification,
    PretrainedConfig,
    SchedulerType,
    default_data_collator,
    get_scheduler,
)
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version



check_min_version("4.36.0.dev0")

logger = get_logger(__name__)
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")


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=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(
        "--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(
        "--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."
        ),
    )
    parser.add_argument(
        "--ignore_mismatched_sizes",
        action="store_true",
        help="Whether or not to enable to load a pretrained model whose head dimensions are different.",
    )
    args = parser.parse_args()

    
    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()

    
    
    send_example_telemetry("run_ner_no_trainer", args)

    
    
    
    accelerator = (
        Accelerator(log_with=args.report_to, project_dir=args.output_dir) if args.with_tracking else Accelerator()
    )
    
    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
        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]

    
    
    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

    
    
    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)

    
    
    
    
    if args.config_name:
        config = AutoConfig.from_pretrained(
            args.config_name, num_labels=num_labels, trust_remote_code=args.trust_remote_code
        )
    elif args.model_name_or_path:
        config = AutoConfig.from_pretrained(
            args.model_name_or_path, num_labels=num_labels, 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.")

    tokenizer_name_or_path = args.tokenizer_name if args.tokenizer_name else args.model_name_or_path
    if not tokenizer_name_or_path:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if config.model_type in {"bloom", "gpt2", "roberta"}:
        tokenizer = AutoTokenizer.from_pretrained(
            tokenizer_name_or_path, use_fast=True, add_prefix_space=True, trust_remote_code=args.trust_remote_code
        )
    else:
        tokenizer = AutoTokenizer.from_pretrained(
            tokenizer_name_or_path, use_fast=True, trust_remote_code=args.trust_remote_code
        )

    if args.model_name_or_path:
        model = AutoModelForTokenClassification.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            ignore_mismatched_sizes=args.ignore_mismatched_sizes,
            trust_remote_code=args.trust_remote_code,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForTokenClassification.from_config(config, trust_remote_code=args.trust_remote_code)

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

    
    if model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id:
        if sorted(model.config.label2id.keys()) == sorted(label_list):
            
            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: {sorted(model.config.label2id.keys())}, dataset labels:"
                f" {sorted(label_list)}.\nIgnoring the model labels as a result.",
            )

    
    model.config.label2id = {l: i for i, l in enumerate(label_list)}
    model.config.id2label = dict(enumerate(label_list))

    
    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)

    
    
    padding = "max_length" if args.pad_to_max_length else False

    

    def tokenize_and_align_labels(examples):
        tokenized_inputs = tokenizer(
            examples[text_column_name],
            max_length=args.max_length,
            padding=padding,
            truncation=True,
            
            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:
                
                
                if word_idx is None:
                    label_ids.append(-100)
                
                elif word_idx != previous_word_idx:
                    label_ids.append(label_to_id[label[word_idx]])
                
                
                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"]

    
    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 = 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)

    
    
    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,
        num_training_steps=args.max_train_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("ner_no_trainer", experiment_config)

    
    metric = evaluate.load("seqeval")

    def get_labels(predictions, references):
        
        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()

        
        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:
            
            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"],
            }

    
    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):
            outputs = model(**batch)
            loss = outputs.loss
            
            if args.with_tracking:
                total_loss += loss.detach().float()
            loss = loss / args.gradient_accumulation_steps
            accelerator.backward(loss)
            if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
                progress_bar.update(1)
                completed_steps += 1

            if isinstance(checkpointing_steps, int):
                if completed_steps % checkpointing_steps == 0:
                    output_dir = f"step_{completed_steps}"
                    if args.output_dir is not None:
                        output_dir = os.path.join(args.output_dir, output_dir)
                    accelerator.save_state(output_dir)

            if completed_steps >= args.max_train_steps:
                break

        model.eval()
        samples_seen = 0
        for step, batch in enumerate(eval_dataloader):
            with torch.no_grad():
                outputs = model(**batch)
            predictions = outputs.logits.argmax(dim=-1)
            labels = batch["labels"]
            if not args.pad_to_max_length:  
                predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100)
                labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)
            predictions_gathered, labels_gathered = accelerator.gather((predictions, labels))
            
            if accelerator.num_processes > 1:
                if step == len(eval_dataloader) - 1:
                    predictions_gathered = predictions_gathered[: len(eval_dataloader.dataset) - samples_seen]
                    labels_gathered = labels_gathered[: len(eval_dataloader.dataset) - samples_seen]
                else:
                    samples_seen += labels_gathered.shape[0]
            preds, refs = get_labels(predictions_gathered, labels_gathered)
            metric.add_batch(
                predictions=preds,
                references=refs,
            )  

        eval_metric = compute_metrics()
        accelerator.print(f"epoch {epoch}:", eval_metric)
        if args.with_tracking:
            accelerator.log(
                {
                    "seqeval": eval_metric,
                    "train_loss": total_loss.item() / len(train_dataloader),
                    "epoch": epoch,
                    "step": completed_steps,
                },
                step=completed_steps,
            )

        if args.push_to_hub and epoch < args.num_train_epochs - 1:
            accelerator.wait_for_everyone()
            unwrapped_model = accelerator.unwrap_model(model)
            unwrapped_model.save_pretrained(
                args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
            )
            if accelerator.is_main_process:
                tokenizer.save_pretrained(args.output_dir)
                repo.push_to_hub(
                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
                )

        if args.checkpointing_steps == "epoch":
            output_dir = f"epoch_{epoch}"
            if args.output_dir is not None:
                output_dir = os.path.join(args.output_dir, output_dir)
            accelerator.save_state(output_dir)

    if args.with_tracking:
        accelerator.end_training()

    if args.output_dir is not None:
        accelerator.wait_for_everyone()
        unwrapped_model = accelerator.unwrap_model(model)
        unwrapped_model.save_pretrained(
            args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
        )
        if accelerator.is_main_process:
            tokenizer.save_pretrained(args.output_dir)
            if args.push_to_hub:
                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)

            all_results = {f"eval_{k}": v for k, v in eval_metric.items()}
            if args.with_tracking:
                all_results.update({"train_loss": total_loss.item() / len(train_dataloader)})
            with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
                
                for key, value in all_results.items():
                    if isinstance(value, np.float64):
                        all_results[key] = float(value)
                    elif isinstance(value, np.int64):
                        all_results[key] = int(value)
                json.dump(all_results, f)


if __name__ == "__main__":
    main()