code: 
  
  
  
  
  
  import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  
  import datasets
  import torch
  from accelerate import Accelerator, DistributedType
  from accelerate.utils import ProjectConfiguration
  from arguments import TrainingArguments
  from datasets import load_dataset
  from huggingface_hub import Repository
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  
  import transformers
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
       def __init__(		self    :    List[Any]					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    Union[str, Any]					,			lowerCamelCase    :    Union[str, Any]=False					,			lowerCamelCase    :    Tuple=10_24					,			lowerCamelCase    :    int=10_24					,			lowerCamelCase    :    Any=3.6					,			lowerCamelCase    :    Dict=False					,			):
         lowerCamelCase_  :    str   	=			tokenizer
         lowerCamelCase_  :    str   	=			tokenizer.bos_token_id
         lowerCamelCase_  :    Dict   	=			dataset
         lowerCamelCase_  :    Dict   	=			seq_length
         lowerCamelCase_  :    Optional[int]   	=			0
         lowerCamelCase_  :    Union[str, Any]   	=			infinite
         lowerCamelCase_  :    Optional[int]   	=			0
         lowerCamelCase_  :    str   	=			tokenized
  
         if self.tokenized:
           lowerCamelCase_  :    int   	=			seq_length * num_of_sequences
           lowerCamelCase_  :    List[str]   	=			'input_ids'
         else:
           lowerCamelCase_  :    str   	=			seq_length * chars_per_token * num_of_sequences
           lowerCamelCase_  :    int   	=			'content'
  
  
  
       def __iter__(		self    :    Optional[Any]	):
         lowerCamelCase_  :    Tuple   	=			iter(self.dataset	)
         lowerCamelCase_  :    Any   	=			True
         while more_examples:
           lowerCamelCase_  :    str   	=			[], 0
           while True:
             if buffer_len >= self.max_buffer_size:
               break
             try:
               buffer.append(next(lowerCamelCase	)[self.content_field]	)
               buffer_len += len(buffer[-1]	)
             except StopIteration:
               if self.infinite:
                 lowerCamelCase_  :    Any   	=			iter(self.dataset	)
                 self.epoch += 1
                 logger.info(F"Dataset epoch: {self.epoch}"	)
               else:
                 lowerCamelCase_  :    List[str]   	=			False
                 break
           if self.tokenized:
             lowerCamelCase_  :    Optional[Any]   	=			buffer
           else:
             lowerCamelCase_  :    Union[str, Any]   	=			self.tokenizer(lowerCamelCase					,			truncation=lowerCamelCase	)['input_ids']
           lowerCamelCase_  :    Union[str, Any]   	=			[]
           for tokenized_input in tokenized_inputs:
             all_token_ids.extend(tokenized_input + [self.concat_token_id]	)
           for i in range(0					,			len(lowerCamelCase	)					,			self.seq_length	):
             lowerCamelCase_  :    Optional[int]   	=			all_token_ids[i : i + self.seq_length]
             if len(lowerCamelCase	) == self.seq_length:
               self.current_size += 1
               yield torch.tensor(lowerCamelCase	)
  
  
  
  
  
       def      __a    (		self    :    Dict					,			lowerCamelCase    :    Optional[int]=10_00	):
         return ShufflerIterDataPipe(self					,			buffer_size=lowerCamelCase	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
    lowerCamelCase_  :    Any   	=			args.model_ckpt.split('/'	)[-1]
    lowerCamelCase_  :    List[Any]   	=			logging.getLogger(__name__	)
    lowerCamelCase_  :    str   	=			Path(args.save_dir	) / 'log/'
    log_dir.mkdir(exist_ok=lowerCAmelCase__	)
    lowerCamelCase_  :    Dict   	=			F"debug_{accelerator.process_index}.log"
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'       ,datefmt='%m/%d/%Y %H:%M:%S'       ,level=logging.INFO       ,handlers=[logging.FileHandler(log_dir / filename	), logging.StreamHandler()]       ,)
    if accelerator.is_main_process:  # we only want to setup logging once
      accelerator.init_trackers(lowerCAmelCase__       ,vars(lowerCAmelCase__	)	)
      lowerCamelCase_  :    Optional[Any]   	=			accelerator.trackers[0].run.name
      logger.setLevel(logging.INFO	)
      datasets.utils.logging.set_verbosity_info()
      transformers.utils.logging.set_verbosity_info()
    else:
      lowerCamelCase_  :    Optional[Any]   	=			''
      logger.setLevel(logging.ERROR	)
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
    return logger, run_name
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
    lowerCamelCase_  :    Optional[Any]   	=			{'streaming': True}
    lowerCamelCase_  :    str   	=			load_dataset(args.dataset_name_train       ,split='train'       ,**lowerCAmelCase__	)
    lowerCamelCase_  :    Any   	=			train_data.shuffle(buffer_size=args.shuffle_buffer       ,seed=args.seed	)
    lowerCamelCase_  :    str   	=			load_dataset(args.dataset_name_valid       ,split='train'       ,**lowerCAmelCase__	)
    lowerCamelCase_  :    Tuple   	=			ConstantLengthDataset(
        lowerCAmelCase__       ,lowerCAmelCase__       ,infinite=lowerCAmelCase__       ,seq_length=args.seq_length       ,tokenized=args.tokenized	)
    lowerCamelCase_  :    str   	=			ConstantLengthDataset(
        lowerCAmelCase__       ,lowerCAmelCase__       ,infinite=lowerCAmelCase__       ,seq_length=args.seq_length       ,tokenized=args.tokenized	)
    lowerCamelCase_  :    str   	=			train_dataset.shuffle(buffer_size=args.shuffle_buffer	)
    lowerCamelCase_  :    List[Any]   	=			DataLoader(lowerCAmelCase__       ,batch_size=args.train_batch_size       ,shuffle=lowerCAmelCase__	)
    lowerCamelCase_  :    List[Any]   	=			DataLoader(lowerCAmelCase__       ,batch_size=args.valid_batch_size	)
    return train_dataloader, eval_dataloader
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	):
    lowerCamelCase_  :    Union[str, Any]   	=			[], []
    for n, p in model.named_parameters():
      if any(nd in n for nd in no_decay	):
        params_without_wd.append(lowerCAmelCase__	)
      else:
        params_with_wd.append(lowerCAmelCase__	)
    return [
        {"params": params_with_wd, "weight_decay": args.weight_decay},
        {"params": params_without_wd, "weight_decay": 0.0},
    ]
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
    logger.info(F"Step {step}: {metrics}"	)
    if accelerator.is_main_process:
      accelerator.log(lowerCAmelCase__       ,lowerCAmelCase__	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
    # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
    lowerCamelCase_  :    List[Any]   	=			accelerator.unwrap_model(lowerCAmelCase__	).config
    lowerCamelCase_  :    Dict   	=			4 if args.gradient_checkpointing else 3
    lowerCamelCase_  :    Any   	=			args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    lowerCamelCase_  :    List[Any]   	=			24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    lowerCamelCase_  :    Union[str, Any]   	=			factor * (
        1.0
        + (args.seq_length / (6.0 * config_model.n_embd))
        + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
    )
    lowerCamelCase_  :    int   	=			flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
    model.eval()
    lowerCamelCase_  :    str   	=			[]
    for step, batch in enumerate(lowerCAmelCase__	):
      with torch.no_grad():
        lowerCamelCase_  :    List[str]   	=			model(lowerCAmelCase__       ,labels=lowerCAmelCase__	)
      lowerCamelCase_  :    int   	=			outputs.loss.repeat(args.valid_batch_size	)
      losses.append(accelerator.gather(lowerCAmelCase__	)	)
      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
        break
    lowerCamelCase_  :    Dict   	=			torch.cat(lowerCAmelCase__	)
    lowerCamelCase_  :    Dict   	=			losses[: eval_dataloader.dataset.current_size].mean()
    try:
      lowerCamelCase_  :    Optional[int]   	=			torch.exp(lowerCAmelCase__	)
    except OverflowError:
      lowerCamelCase_  :    Any   	=			float('inf'	)
    return loss.item(), perplexity.item()
  
  
  # Settings
  _lowercase		:     Dict 					=HfArgumentParser(TrainingArguments)
  _lowercase		:     str 					=parser.parse_args()
  
  # Accelerator
  _lowercase		:     List[str] 					=ProjectConfiguration(project_dir=args.save_dir, logging_dir="""log""")
  _lowercase		:     Optional[int] 					=Accelerator(log_with=["""wandb""", """tensorboard"""], project_config=config)
  _lowercase		:     Any 					={str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  _lowercase		:     str 					=Namespace(**vars(args), **acc_state)
  _lowercase		:     Any 					=accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
    _lowercase		:     Any 					=Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  _lowercase		:     Optional[int] 					=setup_logging(args)
  logger.info(accelerator.state)
  
  # Checkout new branch on repo
  if accelerator.is_main_process:
    hf_repo.git_checkout(run_name, create_branch_ok=True)
  
  # Load model and tokenizer
  _lowercase		:     int 					=AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
    model.gradient_checkpointing_enable()
  _lowercase		:     Tuple 					=AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  _lowercase		:     Dict 					=create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  _lowercase		:     List[str] 					=AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  _lowercase		:     str 					=get_scheduler(
      name=args.lr_scheduler_type,
      optimizer=optimizer,
      num_warmup_steps=args.num_warmup_steps,
      num_training_steps=args.max_train_steps,
  )
  accelerator.register_for_checkpointing(lr_scheduler)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
    return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  _lowercase		:     str 					=accelerator.prepare(
      model, optimizer, train_dataloader, eval_dataloader
  )
  
  # load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
      accelerator.load_state(args.resume_from_checkpoint)
      _lowercase		:     Optional[Any] 					=os.path.basename(args.resume_from_checkpoint)
    else:
      # Get the most recent checkpoint
      _lowercase		:     int 					=[f.name for f in os.scandir(args.save_dir) if f.is_dir() and """step""" in str(f)]
      dirs.sort(key=os.path.getctime)
      _lowercase		:     Optional[Any] 					=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract the step of the checkpoint to continue from there
    _lowercase		:     Optional[int] 					=os.path.splitext(path)[0]
    _lowercase		:     Union[str, Any] 					=int(training_difference.replace("""step_""", """"""))
  
  # Train model
  model.train()
  _lowercase		:     Dict 					=0
  _lowercase		:     Union[str, Any] 					=time.time()
  _lowercase		:     Dict 					=0
  for step, batch in enumerate(train_dataloader, start=1):
    if args.resume_from_checkpoint and step < resume_step:
      continue  # we need to skip steps until we reach the resumed step
    _lowercase		:     int 					=model(batch, labels=batch, use_cache=False).loss
    _lowercase		:     int 					=accelerator.gather(loss.repeat(args.train_batch_size)).mean()
    loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
    log_metrics(step, {"""samples""": step * samples_per_step, """loss_per_step/train""": loss.item()})
    _lowercase		:     Any 					=loss / args.gradient_accumulation_steps
    if step % args.gradient_accumulation_steps != 0:
      # Prevent backward from doing gradient all_reduce in every step
      if accelerator.distributed_type == DistributedType.MULTI_GPU:
        with model.no_sync():
          accelerator.backward(loss)
      else:
        accelerator.backward(loss)
    else:
      _lowercase		:     Tuple 					=get_lr()
      accelerator.backward(loss)
      accelerator.clip_grad_norm_(model.parameters(), 1.0)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      _lowercase		:     Dict 					=time.time() - t_start
      _lowercase		:     Union[str, Any] 					=compute_tflops(elapsed_time, accelerator, args)
      log_metrics(
          step,
          {
              """steps""": completed_steps,
              """loss/train""": loss_tracking,
              """lr""": lr,
              """tflops""": tflops,
              """time_per_iteration""": elapsed_time,
          },
      )
      _lowercase		:     Tuple 					=time.time()
      _lowercase		:     Dict 					=0
      completed_steps += 1
    if step % args.save_checkpoint_steps == 0:
      logger.info("""Evaluating and saving model checkpoint""")
      _lowercase		:     Dict 					=evaluate(args)
      log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
      accelerator.wait_for_everyone()
      _lowercase		:     Optional[Any] 					=os.path.join(args.save_dir, F'''step_{step}''')
      accelerator.save_state(save_dir)
      if accelerator.is_main_process:
        hf_repo.push_to_hub(commit_message=F'''step {step}''')
      model.train()
    if completed_steps >= args.max_train_steps:
      break
  
  # Evaluate and save the last checkpoint
  logger.info("""Evaluating and saving model after training""")
  _lowercase		:     Union[str, Any] 					=evaluate(args)
  log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
  accelerator.wait_for_everyone()
  _lowercase		:     str 					=accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  _lowercase		:     Tuple 					=os.path.join(args.save_dir, F'''step_{step}''')
  accelerator.save_state(save_dir)
  if accelerator.is_main_process:
    hf_repo.push_to_hub(commit_message="""final model""")
  
style_context: 
  
  
  
  
  
  import os
  import unittest
  
  import pytest
  from parameterized import parameterized
  
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  
  
  # skipping in unittest tests
  
  _lowercase		:     Optional[Any] 					=[(1,)]
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    Union[str, Any]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
  		if run_slow:
  				assert True
  		else:
  				assert False, "should have been skipped"
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    List[str]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
  		if run_slow and torch_device == "cuda":
  				assert True
  		else:
  				assert False, "should have been skipped"
  
  
  
  
  
  
  @require_torch
  class 	UpperCamelCase_     (     unittest.TestCase       ):
  
  
  
  					@slow
  					@require_torch_gpu
  					def      __a    (		self    :    Tuple	):
  							check_slow_torch_cuda()
  
  
  
  					@require_torch_gpu
  					@slow
  					def      __a    (		self    :    Optional[int]	):
  							check_slow_torch_cuda()
  
  
  
  
  
  					@parameterized.expand(lowerCamelCase	)
  					@slow
  					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    Optional[Any]=None	):
  							check_slow()
  
  
  @slow
  @require_torch_gpu
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		check_slow_torch_cuda()
  
  
  @require_torch_gpu
  @slow
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		check_slow_torch_cuda()
  
  
  @slow
  @pytest.mark.parametrize('param'       ,[1]	)
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		check_slow()
  
  
  
  
  
  @pytest.mark.parametrize('param'       ,[1]	)
  @slow
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		check_slow()
  