code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Finetune a transformers model on a text classification task"
      )
      parser.add_argument(
          "--task_name",
          type=str,
          default=None,
          help="The name of the glue task to train on.",
          choices=list(task_to_keys.keys()),
      )
      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_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=True,
      )
      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=64,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--per_device_eval_batch_size",
          type=int,
          default=32,
          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=0.01,
          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_epochs",
          type=float,
          default=0,
          help="Number of epochs 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.",
      )
  
      #############deepspeed, compression, and knowledage distillation#########
      parser.add_argument(
          "--deepspeed",
          action="store_true",
          help="use deepspeed or not",
      )
      parser.add_argument(
          "--deepspeed_config",
          type=str,
          default=None,
          help="deepspeed config",
      )
      parser.add_argument(
          "--save_best_model",
          action="store_true",
          help="save best checkpoint model",
      )
      parser.add_argument(
          "--clean_best_model",
          action="store_true",
          help="clean the  model",
      )
      parser.add_argument(
          "--distill_method",
          type=str,
          default=None,
          help="knowledage distillation",
      )
      parser.add_argument(
          "--local_rank",
          type=int,
          default=-1,
          help="local_rank for distributed training on gpus",
      )
      parser.add_argument(
          "--model_name_or_path_teacher",
          default=None,
          type=str,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
      )
      parser.add_argument(
          "--pretrained_dir_student",
          type=str,
          default=None,
          help="Where to load the student pretrained model.",
      )
      parser.add_argument(
          "--pretrained_dir_teacher",
          type=str,
          default=None,
          help="Where to load the teacher pretrained model.",
      )
      parser.add_argument(
          "--eval_step",
          type=int,
          default=1000,
          help="when to eval the model.",
      )
  
      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."
  
      return args
  
imports: import argparse
  import logging
  import math
  import time
  import os
  import random
  import json
  import numpy
  from pathlib import Path
  import datasets
  from datasets import load_dataset, load_metric
  import torch
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from tqdm.auto import tqdm
  import transformers
  from huggingface_hub import Repository
  from transformers import AdamW, AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, 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 huggingface_transformer.modeling_bert import BertForSequenceClassification
  import deepspeed
  from deepspeed.compression.compress import init_compression, redundancy_clean
  from util import *
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args