run_ner_no_trainer.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning a 🤗 Transformers model on token classification tasks (NER, POS, CHUNKS) relying on the accelerate library
without using a Trainer.
"""

import argparse
import logging,multiprocessing
import math
import os
import random,string
from pathlib import Path

import datasets
import torch
from datasets import ClassLabel, load_dataset, load_metric
from torch.utils.data import DataLoader
from tqdm.auto import tqdm

import transformers
from accelerate import Accelerator
from huggingface_hub import Repository
from transformers import (
    CONFIG_MAPPING,
    MODEL_MAPPING,
    AdamW,
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorForTokenClassification,
    PretrainedConfig,
    SchedulerType,
    default_data_collator,
    get_scheduler,
    set_seed,
)
from transformers.file_utils import get_full_repo_name
from transformers.utils.versions import require_version
# from utils.process_func import * 
# from utils.crf_bert import * 
import pandas as pd 
logger = logging.getLogger(__name__)
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")

# You should update this to your particular problem to have better documentation of `model_type`
MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

# cache path for your cached model, tokens, files etc...
# automatically downloaded files are saved in this path
CACHE_DIR = "/scratch/w/wluyliu/yananc/cache"

# labelled samples from Prodigy 
INPUT_SAMPLES_DIR = "./datasets/sentence_level_tokens.json"

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(
        "--debug_cnt",
        type=int,
        default=-1,
    )

    parser.add_argument(
        "--k",
        type=int,
        default=0,
    )

    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(
        "--local_files_only",
        action="store_true",
    )
    parser.add_argument(
        "--da",
        type=int, default=-1,
    )
    
    parser.add_argument(
        "--da_ver",
        type=str,
        help="Only used for fewnerd dataset for da experiments",
    )

    # parser.add_argument(
    #     "--crf",
    #     action="store_true",
    # )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=1e-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(
        "--binomial",
        type=float,
        default=1
    )
    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.")
    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.
    accelerator = Accelerator()
    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state)

    # Setup logging, we only want one process per machine to log things on the screen.
    # accelerator.is_local_main_process is only True for one process per machine.
    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            if args.hub_model_id is None:
                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
            else:
                repo_name = args.hub_model_id
            repo = Repository(args.output_dir, clone_from=repo_name)
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets for token classification task available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'tokens' or the first column if no column called
    # 'tokens' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    # if args.dataset_name is not None and args.dataset_name != 'few_nerd_local':
    #     # Downloading and loading a dataset from the hub.
    #     raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name, \
    #         cache_dir='/scratch/w/wluyliu/yananc/cache')


    def tqi_replacement(example):

        if list(set(example['tags'])) == ['O']:
            return example

        tokens_ = []
        tags_ = []

        i = 0
        while i < len(example['tokens']):

            # print(i, token, tag)
            if example['tags'][i] != 'O':
                df_candidates_mention = df_tags.loc[df_tags['tag'] == example['tags'][i]]
                candidate = df_candidates_mention.sample(1)['span'].tolist()[0]
                candidate_tokens = candidate.split()
                for j in range(i, len(example['tokens'])):
                    if example['tags'][j] == 'O':
                        break 
                tokens_.extend(candidate_tokens)
                tags_.extend(len(candidate_tokens) * [example['tags'][i]])
                # print(example['tags'][i] , '==>',i)
                if example['tags'][j] == 'O':
                    i = j 
                else:
                    i = j+1

            else:
                tokens_.append(example['tokens'][i])
                tags_.append(example['tags'][i])
                i += 1

                # print('O==>',i)
        # for  token, tag in zip(tokens_, tags_):
        #     print(token, tag)
        assert len(tokens_) == len(tags_)
        return {'id':example['id'], 'tokens':tokens_, 'tags':tags_}
    



    if args.dataset_name == 'tqi':
        # load dict
        # df_tags = pd.read_csv("/home/w/wluyliu/yananc/nlp4quantumpapers/datasets/QI-NERs.csv")
        # ixl = {i:j for i,j in enumerate(df_tags['tag'].drop_duplicates().tolist()) }
        # ixl_rev = {j:i for i,j in enumerate(df_tags['tag'].drop_duplicates().tolist()) }

        file_list={}
        file_list['train_test'] = INPUT_SAMPLES_DIR 
        raw_datasets = datasets.load_dataset('json', data_files=file_list, \
                cache_dir=CACHE_DIR)

        ids = list(set([ii['id'] for ii in raw_datasets['train_test']]))
        tags = set()
        for ii in raw_datasets['train_test']:
            tags.update(ii['tags'])
        print("TQI tags set:", tags)

        # assert set(list(ixl.values())+ ['O'])  == tags


        # for ii in range(len(raw_datasets['train_test'])):
        #     print(ii)
        #     example = raw_datasets['train_test'][ii]
        #     example_ = tqi_replacement(example)

        # randomly split the labelling samples into train and test split
        random.shuffle(ids)
        split_ix = int(len(ids)*0.8)
        print("ids:{} split_ix:{}".format(len(ids), split_ix))
        # if args.debug_cnt > 0: 
        #     assert args.debug_cnt < split_ix
        #     ids_train = ids[:args.debug_cnt]
        # else:
        ids_train = ids[:split_ix]
        ids_test = ids[split_ix:]

        train_syn_ll = []
        if args.k > 0: 
            for _ in range(args.k): # 
                train_syn_ll.append(raw_datasets\
                                    .filter(lambda example: example['id'] in ids_train, num_proc= multiprocessing.cpu_count())['train_test']\
                                    .map(lambda example: tqi_replacement(example)))
        
        train_syn_ll.append(raw_datasets\
                                .filter(lambda example: example['id'] in ids_train, num_proc= multiprocessing.cpu_count())['train_test'])
        raw_datasets['train'] = datasets.concatenate_datasets(train_syn_ll)
            
        raw_datasets['test'] = raw_datasets\
                                .filter(lambda example: example['id'] in ids_test, num_proc= multiprocessing.cpu_count())['train_test']


    # this branch is not for TQI, ignore it
    elif args.dataset_name == 'few_nerd_local':
        file_list = {}
        for dsn in ['dev','test','train']:
            file_list[dsn] = '/gpfs/fs0/scratch/w/wluyliu/yananc/few_nerd_supervised/{}.json'.format(dsn)
        file_list['da'] = '/scratch/w/wluyliu/yananc/fewnerd_augmented/{}.json'.format(args.da_ver)   
        raw_datasets_ = datasets.load_dataset('json', data_files=file_list, \
                cache_dir=CACHE_DIR)

        # file_list = {}
        # file_list['da'] = '/gpfs/fs0/scratch/w/wluyliu/yananc/few_nerd_supervised/da_coarse_binomal_{}.json'.format(args.binomial)        
             
        # raw_datasets_['da'] = datasets.load_dataset('json', data_files=file_list, cache_dir='/scratch/w/wluyliu/yananc/cache')['da']#.rename_column("tags_coarse", "tags")
        def map_func(example):
            # tag_fine_ix = []
            # tag_coarse_ix = []
            tags_coarse = []
            for tag in example['tags']:
                # tag_fine_ix.append(tag_map_fine[tag])
                if tag != 'O':
                    # tag_coarse_ix.append(tag_map_coarse[tag.split('-')[0]])
                    tags_coarse.append(tag.split('-')[0])
                else:
                    # tag_coarse_ix.append(tag_map_coarse[tag])
                    tags_coarse.append(tag)
            example['tags_coarse'] = tags_coarse
            example['tags_fine'] = example['tags']
            # example['tag_fine_ix'] = tag_fine_ix 
            # example['tag_coarse_ix'] = tag_coarse_ix

            for ii, jj in example.items():
                if ii == 'id':
                    continue
                assert len(jj) == len(example['tokens']) 

            return example
    
        raw_datasets = raw_datasets_.map(map_func, 
                batched=False,
                num_proc= multiprocessing.cpu_count() ,
                load_from_cache_file= False, remove_columns=['tags'],
                desc = "Running ix mapping ==>")


        if args.debug_cnt > 0:     
            # raw_datasets['train_dev'] = datasets.concatenate_datasets([raw_datasets["train"], raw_datasets["dev"]])
            
            random_ids = random.sample(raw_datasets['train']['id'], args.debug_cnt)
            raw_datasets['train'] = raw_datasets['train'].filter(lambda example: example['id'] in random_ids, num_proc= multiprocessing.cpu_count())
            raw_datasets['da'] = raw_datasets['da'].filter(lambda example: example['id'] in random_ids, num_proc= multiprocessing.cpu_count())

        if args.da:
            raw_datasets['train'] = datasets.concatenate_datasets([raw_datasets["train"], raw_datasets["da"]]).shuffle()


    if 'dev' in raw_datasets.keys():
        raw_datasets['test'] = datasets.concatenate_datasets([raw_datasets["test"], raw_datasets["dev"]])

    column_names = raw_datasets["train"].column_names
    features = raw_datasets["train"].features


    # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
    # unique labels.
    def get_label_list(labels):
        unique_labels = set()
        for label in labels:
            unique_labels = unique_labels | set(label)
        label_list = list(unique_labels)
        label_list.sort()
        return label_list

    # If the labels are of type ClassLabel, they are already integers and we have the map stored somewhere.
    # Otherwise, we have to get the list of labels manually.
    labels_are_int = isinstance(features[args.label_column_name].feature, ClassLabel)
    if labels_are_int:
        label_list = features[args.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"][args.label_column_name])
        label_to_id = {l: i for i, l in enumerate(label_list)}

    num_labels = len(label_list)

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

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

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

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

    if args.model_name_or_path:
        
        # if args.crf and not args.lstm:
        #     model = BertCRF.from_pretrained(args.model_name_or_path, num_labels=num_labels, \
        #                 cache_dir="/scratch/w/wluyliu/yananc/cache", local_files_only=args.local_files_only)
        # elif args.crf and args.lstm:
        #     model = BertLstmCRF.from_pretrained(args.model_name_or_path, num_labels=num_labels, \
        #                 cache_dir="/scratch/w/wluyliu/yananc/cache", local_files_only=args.local_files_only)

        # else:
        model = AutoModelForTokenClassification.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config, \
            cache_dir=CACHE_DIR, local_files_only=args.local_files_only
    )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForTokenClassification.from_config(config)

    model.resize_token_embeddings(len(tokenizer))

    # Model has labels -> use them.
    if model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id:
        if list(sorted(model.config.label2id.keys())) == list(sorted(label_list)):
            # Reorganize `label_list` to match the ordering of the model.
            if labels_are_int:
                label_to_id = {i: int(model.config.label2id[l]) for i, l in enumerate(label_list)}
                label_list = [model.config.id2label[i] for i in range(num_labels)]
            else:
                label_list = [model.config.id2label[i] for i in range(num_labels)]
                label_to_id = {l: i for i, l in enumerate(label_list)}
        else:
            logger.warning(
                "Your model seems to have been trained with labels, but they don't match the dataset: ",
                f"model labels: {list(sorted(model.config.label2id.keys()))}, dataset labels: {list(sorted(label_list))}."
                "\nIgnoring the model labels as a result.",
            )

    # Set the correspondences label/ID inside the model config
    model.config.label2id = {l: i for i, l in enumerate(label_list)}
    model.config.id2label = {i: l for i, l in enumerate(label_list)}

    # Map that sends B-Xxx label to its I-Xxx counterpart
    b_to_i_label = []
    for idx, label in enumerate(label_list):
        if label.startswith("B-") and label.replace("B-", "I-") in label_list:
            b_to_i_label.append(label_list.index(label.replace("B-", "I-")))
        else:
            b_to_i_label.append(idx)

    # Preprocessing the datasets.
    # First we tokenize all the texts.
    padding = "max_length" if args.pad_to_max_length else False

    # Tokenize all texts and align the labels with them.

    def tokenize_and_align_labels(examples):
        tokenized_inputs = tokenizer(
            examples[args.text_column_name],
            max_length=args.max_length,
            padding=padding,
            truncation=True,
            # We use this argument because the texts in our dataset are lists of words (with a label for each word).
            is_split_into_words=True,
        )

        labels = []
        for i, label in enumerate(examples[args.label_column_name]):
            word_ids = tokenized_inputs.word_ids(batch_index=i)
            previous_word_idx = None
            label_ids = []
            for word_idx in word_ids:
                # Special tokens have a word id that is None. We set the label to -100 so they are automatically
                # ignored in the loss function.
                if word_idx is None:
                    label_ids.append(-100)
                # We set the label for the first token of each word.
                elif word_idx != previous_word_idx:
                    label_ids.append(label_to_id[label[word_idx]])
                # For the other tokens in a word, we set the label to either the current label or -100, depending on
                # the label_all_tokens flag.
                else:
                    if args.label_all_tokens:
                        label_ids.append(b_to_i_label[label_to_id[label[word_idx]]])
                    else:
                        label_ids.append(-100)
                previous_word_idx = word_idx

            labels.append(label_ids)
        tokenized_inputs["labels"] = labels
        return tokenized_inputs

    with accelerator.main_process_first():
        processed_raw_datasets = raw_datasets.map(
            tokenize_and_align_labels,
            batched=True,
            remove_columns=raw_datasets["train"].column_names,
            desc="Running tokenizer on dataset",
        )

    train_dataset = processed_raw_datasets['train']
    test_dataset = processed_raw_datasets["test"]

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

    # DataLoaders creation:
    if args.pad_to_max_length:
        # If padding was already done ot max length, we use the default data collator that will just convert everything
        # to tensors.
        data_collator = default_data_collator
    else:
        # Otherwise, `DataCollatorForTokenClassification` will apply dynamic padding for us (by padding to the maximum length of
        # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
        # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
        data_collator = DataCollatorForTokenClassification(
            tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)
        )

    train_dataloader = DataLoader(
        train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
    )
    eval_dataloader = DataLoader(test_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 = datasets.load_metric("seqeval", cache_dir=CACHE_DIR, experiment_id=''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(8)))
    # https://github.com/huggingface/datasets/blob/master/metrics/seqeval/seqeval.py
    def get_labels(predictions, references):
        # Transform predictions and references tensos to numpy arrays
        if device.type == "cpu":
            y_pred = predictions.detach().clone().numpy()
            y_true = references.detach().clone().numpy()
        else:
            y_pred = predictions.detach().cpu().clone().numpy()
            y_true = references.detach().cpu().clone().numpy()

        # Remove ignored index (special tokens)
        true_predictions = [
            [label_list[p] for (p, l) in zip(pred, gold_label) if l != -100]
            for pred, gold_label in zip(y_pred, y_true)
        ]
        true_labels = [
            [label_list[l] for (p, l) in zip(pred, gold_label) if l != -100]
            for pred, gold_label in zip(y_pred, y_true)
        ]
        return true_predictions, true_labels
    
    # to get the result in terms of the metrics to evaluate the model's performance
    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": round(results["overall_precision"], 4),
                "recall": round(results["overall_recall"], 4),
                "f1": round(results["overall_f1"],4)
            }

    # 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

    best_f1 = 0
    for epoch in range(args.num_train_epochs):
        model.train()
        for step, batch in enumerate(train_dataloader):
            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):
            with torch.no_grad():
                outputs = model(**batch)
            predictions = outputs.logits.argmax(dim=-1)
            labels = batch["labels"]

            


            if not args.pad_to_max_length:  # necessary to pad predictions and labels for being gathered
                predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100)
                labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)

            predictions_gathered = accelerator.gather(predictions)
            labels_gathered = accelerator.gather(labels)
            preds, refs = get_labels(predictions_gathered, labels_gathered)
            metric.add_batch(
                predictions=preds,
                references=refs,
            )  # predictions and preferences are expected to be a nested list of labels, not label_ids

        # eval_metric = metric.compute()
        eval_metric = compute_metrics()
        # accelerator.print(f"epoch {epoch}:", args.label_column_name, args.debug_cnt, eval_metric)
        print("roberta_ner_report ==>", 'epoch:', epoch,  eval_metric)
        if eval_metric['f1'] > best_f1:
            best_f1 = eval_metric['f1']

        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)
    print("roberta_ner_report_final ==>",  best_f1)

if __name__ == "__main__":
    main()