code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Finetune a transformers model on a text classification 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_names",
          nargs="+",
          type=str,
          required=True,
          help="The configuration names of the dataset to use (via the datasets library).",
      )
      parser.add_argument(
          "--dataset_split_names",
          nargs="+",
          type=str,
          required=True,
          help="The names of the training data set splits to use (via the datasets library).",
      )
      parser.add_argument(
          "--preprocessing_num_workers",
          type=int,
          default=None,
          help="The number of processes to use for the preprocessing.",
      )
      parser.add_argument(
          "--overwrite_cache",
          action="store_true",
          help="Overwrite the cached training and evaluation sets",
      )
      parser.add_argument(
          "--preprocessing_only",
          action="store_true",
          help="Only run the preprocessing script to be cached for future use",
      )
      parser.add_argument(
          "--cache_dir",
          type=str,
          default=None,
          help="Where do you want to store the pretrained models downloaded from huggingface.co",
      )
      parser.add_argument(
          "--validation_split_percentage",
          type=int,
          default=1,
          help="Percentage of training data that should be used for validation if no validation is present in dataset.",
      )
      parser.add_argument(
          "--logging_steps",
          type=int,
          default=500,
          help="Number of steps between each logging",
      )
      parser.add_argument(
          "--saving_steps",
          type=int,
          default=500,
          help="Number of steps between each logging",
      )
      parser.add_argument(
          "--audio_column_name",
          type=str,
          default="audio",
          help="Column in the dataset that contains speech file path. Defaults to 'audio'",
      )
      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(
          "--train_cache_file_name",
          type=str,
          default=None,
          help="Path to the train cached file name",
      )
      parser.add_argument(
          "--validation_cache_file_name",
          type=str,
          default=None,
          help="Path to the validation cached file 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(
          "--gradient_checkpointing",
          action="store_true",
          help="If True, use gradient checkpointing to save memory at the expense of slower backward 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=0,
          help="A seed for reproducible training.",
      )
      parser.add_argument(
          "--max_gumbel_temperature",
          type=float,
          default=2.0,
          help="Maximum temperature for gumbel softmax.",
      )
      parser.add_argument(
          "--min_gumbel_temperature",
          type=float,
          default=0.5,
          help="Minimum temperature for gumbel softmax.",
      )
      parser.add_argument(
          "--gumbel_temperature_decay",
          type=float,
          default=0.999995,
          help="Decay of gumbel temperature during training.",
      )
      parser.add_argument(
          "--max_duration_in_seconds",
          type=float,
          default=5.0,
          help="Filter out audio files that are longer than `max_duration_in_seconds` seconds",
      )
      parser.add_argument(
          "--min_duration_in_seconds",
          type=float,
          default=3.0,
          help="Filter out audio files that are shorter than `min_duration_in_seconds` seconds",
      )
      parser.add_argument(
          "--pad_to_multiple_of",
          type=int,
          default=None,
          help=(
              "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)."
          ),
      )
      parser.add_argument(
          "--adam_beta1",
          type=float,
          default=0.9,
          help="Beta1 for AdamW optimizer",
      )
      parser.add_argument(
          "--adam_beta2",
          type=float,
          default=0.999,
          help="Beta2 for AdamW optimizer",
      )
      parser.add_argument(
          "--adam_epsilon",
          type=float,
          default=1e-8,
          help="Epsilon for AdamW optimizer",
      )
      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()
  
      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."
  
      if args.output_dir is not None:
          os.makedirs(args.output_dir, exist_ok=True)
  
      return args
  
imports: import argparse
  import math
  import os
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Dict, List, Optional, Union
  import datasets
  import torch
  from datasets import DatasetDict, concatenate_datasets, load_dataset
  from torch.utils.data.dataloader import DataLoader
  from tqdm.auto import tqdm
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from huggingface_hub import Repository
  from transformers import AdamW, SchedulerType, Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2ForPreTraining, get_scheduler, is_wandb_available, set_seed
  from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices, _sample_negative_indices
  from transformers.utils import get_full_repo_name, send_example_telemetry
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args