code: def main():
      parser = argparse.ArgumentParser(description="Training")
      parser.add_argument(
          "--force",
          action="store_true",
          help="Overwrite dump_path if it already exists.",
      )
  
      parser.add_argument(
          "--dump_path",
          type=str,
          required=True,
          help="The output directory (log, checkpoints, parameters, etc.)",
      )
      parser.add_argument(
          "--data_file",
          type=str,
          required=True,
          help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.",
      )
  
      parser.add_argument(
          "--student_type",
          type=str,
          choices=["distilbert", "roberta", "gpt2"],
          required=True,
          help="The student type (DistilBERT, RoBERTa).",
      )
      parser.add_argument(
          "--student_config",
          type=str,
          required=True,
          help="Path to the student configuration.",
      )
      parser.add_argument(
          "--student_pretrained_weights",
          default=None,
          type=str,
          help="Load student initialization checkpoint.",
      )
  
      parser.add_argument(
          "--teacher_type",
          choices=["bert", "roberta", "gpt2"],
          required=True,
          help="Teacher type (BERT, RoBERTa).",
      )
      parser.add_argument(
          "--teacher_name",
          type=str,
          required=True,
          help="The teacher model.",
      )
  
      parser.add_argument(
          "--temperature",
          default=2.0,
          type=float,
          help="Temperature for the softmax temperature.",
      )
      parser.add_argument(
          "--alpha_ce",
          default=0.5,
          type=float,
          help="Linear weight for the distillation loss. Must be >=0.",
      )
      parser.add_argument(
          "--alpha_mlm",
          default=0.0,
          type=float,
          help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.",
      )
      parser.add_argument(
          "--alpha_clm",
          default=0.5,
          type=float,
          help="Linear weight for the CLM loss. Must be >=0.",
      )
      parser.add_argument(
          "--alpha_mse",
          default=0.0,
          type=float,
          help="Linear weight of the MSE loss. Must be >=0.",
      )
      parser.add_argument(
          "--alpha_cos",
          default=0.0,
          type=float,
          help="Linear weight of the cosine embedding loss. Must be >=0.",
      )
  
      parser.add_argument(
          "--mlm",
          action="store_true",
          help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.",
      )
      parser.add_argument(
          "--mlm_mask_prop",
          default=0.15,
          type=float,
          help="Proportion of tokens for which we need to make a prediction.",
      )
      parser.add_argument(
          "--word_mask",
          default=0.8,
          type=float,
          help="Proportion of tokens to mask out.",
      )
      parser.add_argument(
          "--word_keep",
          default=0.1,
          type=float,
          help="Proportion of tokens to keep.",
      )
      parser.add_argument(
          "--word_rand",
          default=0.1,
          type=float,
          help="Proportion of tokens to randomly replace.",
      )
      parser.add_argument(
          "--mlm_smoothing",
          default=0.7,
          type=float,
          help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).",
      )
      parser.add_argument(
          "--token_counts",
          type=str,
          help="The token counts in the data_file for MLM.",
      )
  
      parser.add_argument(
          "--restrict_ce_to_mask",
          action="store_true",
          help="If true, compute the distillation loss only the [MLM] prediction distribution.",
      )
      parser.add_argument(
          "--freeze_pos_embs",
          action="store_true",
          help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.",
      )
      parser.add_argument(
          "--freeze_token_type_embds",
          action="store_true",
          help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.",
      )
  
      parser.add_argument(
          "--n_epoch",
          type=int,
          default=3,
          help="Number of pass on the whole dataset.",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=5,
          help="Batch size (for each process).",
      )
      parser.add_argument(
          "--group_by_size",
          action="store_false",
          help="If true, group sequences that have similar length into the same batch. Default is true.",
      )
  
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=50,
          help="Gradient accumulation for larger training batches.",
      )
      parser.add_argument(
          "--warmup_prop",
          default=0.05,
          type=float,
          help="Linear warmup proportion.",
      )
      parser.add_argument(
          "--weight_decay",
          default=0.0,
          type=float,
          help="Weight decay if we apply some.",
      )
      parser.add_argument(
          "--learning_rate",
          default=5e-4,
          type=float,
          help="The initial learning rate for Adam.",
      )
      parser.add_argument(
          "--adam_epsilon",
          default=1e-6,
          type=float,
          help="Epsilon for Adam optimizer.",
      )
      parser.add_argument(
          "--max_grad_norm",
          default=5.0,
          type=float,
          help="Max gradient norm.",
      )
      parser.add_argument(
          "--initializer_range",
          default=0.02,
          type=float,
          help="Random initialization range.",
      )
  
      parser.add_argument(
          "--fp16",
          action="store_true",
          help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
      )
      parser.add_argument(
          "--fp16_opt_level",
          type=str,
          default="O1",
          help=(
              "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
              "See details at https://nvidia.github.io/apex/amp.html"
          ),
      )
      parser.add_argument(
          "--n_gpu",
          type=int,
          default=1,
          help="Number of GPUs in the node.",
      )
      parser.add_argument(
          "--local_rank",
          type=int,
          default=-1,
          help="Distributed training - Local rank",
      )
      parser.add_argument(
          "--seed", type=int, default=56, help="Random seed"
      )
  
      parser.add_argument(
          "--log_interval",
          type=int,
          default=500,
          help="Tensorboard logging interval.",
      )
      parser.add_argument(
          "--checkpoint_interval",
          type=int,
          default=4000,
          help="Checkpoint interval.",
      )
      args = parser.parse_args()
      sanity_checks(args)
  
      # ARGS #
      init_gpu_params(args)
      set_seed(args)
      if args.is_master:
          if os.path.exists(args.dump_path):
              if not args.force:
                  raise ValueError(
                      f"Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"
                      " itUse `--force` if you want to overwrite it"
                  )
              else:
                  shutil.rmtree(args.dump_path)
  
          if not os.path.exists(args.dump_path):
              os.makedirs(args.dump_path)
          logger.info(
              f"Experiment will be dumped and logged in {args.dump_path}"
          )
  
          # SAVE PARAMS #
          logger.info(f"Param: {args}")
          with open(
              os.path.join(args.dump_path, "parameters.json"), "w"
          ) as f:
              json.dump(vars(args), f, indent=4)
          git_log(args.dump_path)
  
      student_config_class, student_model_class, _ = MODEL_CLASSES[
          args.student_type
      ]
      (
          teacher_config_class,
          teacher_model_class,
          teacher_tokenizer_class,
      ) = MODEL_CLASSES[args.teacher_type]
  
      # TOKENIZER #
      tokenizer = teacher_tokenizer_class.from_pretrained(
          args.teacher_name
      )
      special_tok_ids = {}
      for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
          idx = tokenizer.all_special_tokens.index(tok_symbol)
          special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
      logger.info(f"Special tokens {special_tok_ids}")
      args.special_tok_ids = special_tok_ids
      args.max_model_input_size = tokenizer.max_model_input_sizes[
          args.teacher_name
      ]
  
      # DATA LOADER #
      logger.info(f"Loading data from {args.data_file}")
      with open(args.data_file, "rb") as fp:
          data = pickle.load(fp)
  
      if args.mlm:
          logger.info(
              f"Loading token counts from {args.token_counts} (already pre-computed)"
          )
          with open(args.token_counts, "rb") as fp:
              counts = pickle.load(fp)
  
          token_probs = np.maximum(counts, 1) ** -args.mlm_smoothing
          for idx in special_tok_ids.values():
              token_probs[idx] = 0.0  # do not predict special tokens
          token_probs = torch.from_numpy(token_probs)
      else:
          token_probs = None
  
      train_lm_seq_dataset = LmSeqsDataset(params=args, data=data)
      logger.info("Data loader created.")
  
      # STUDENT #
      logger.info(f"Loading student config from {args.student_config}")
      stu_architecture_config = student_config_class.from_pretrained(
          args.student_config
      )
      stu_architecture_config.output_hidden_states = True
  
      if args.student_pretrained_weights is not None:
          logger.info(
              f"Loading pretrained weights from {args.student_pretrained_weights}"
          )
          student = student_model_class.from_pretrained(
              args.student_pretrained_weights,
              config=stu_architecture_config,
          )
      else:
          student = student_model_class(stu_architecture_config)
  
      if args.n_gpu > 0:
          student.to(f"cuda:{args.local_rank}")
      logger.info("Student loaded.")
  
      # TEACHER #
      teacher = teacher_model_class.from_pretrained(
          args.teacher_name, output_hidden_states=True
      )
      if args.n_gpu > 0:
          teacher.to(f"cuda:{args.local_rank}")
      logger.info(f"Teacher loaded from {args.teacher_name}.")
  
      # FREEZING #
      if args.freeze_pos_embs:
          freeze_pos_embeddings(student, args)
      if args.freeze_token_type_embds:
          freeze_token_type_embeddings(student, args)
  
      # SANITY CHECKS #
      assert student.config.vocab_size == teacher.config.vocab_size
      assert student.config.hidden_size == teacher.config.hidden_size
      assert (
          student.config.max_position_embeddings
          == teacher.config.max_position_embeddings
      )
      if args.mlm:
          assert (
              token_probs.size(0) == stu_architecture_config.vocab_size
          )
  
      # DISTILLER #
      torch.cuda.empty_cache()
      distiller = Distiller(
          params=args,
          dataset=train_lm_seq_dataset,
          token_probs=token_probs,
          student=student,
          teacher=teacher,
      )
      distiller.train()
      logger.info("Let's go get some drinks.")
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/distillation/train.py
imports: import argparse
  import json
  import os
  import pickle
  import shutil
  import numpy
  import torch
  from distiller import Distiller
  from lm_seqs_dataset import LmSeqsDataset
  from transformers import BertConfig, BertForMaskedLM, BertTokenizer, DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer, GPT2Config, GPT2LMHeadModel, GPT2Tokenizer, RobertaConfig, RobertaForMaskedLM, RobertaTokenizer
  from utils import git_log, init_gpu_params, logger, set_seed
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types