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 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_,					lowercase_,					lowercase_=False,					lowercase_=1024,					lowercase_=1024,					lowercase_=3.6,					lowercase_=False,					)							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=tokenizer
       a__						=tokenizer.bos_token_id
       a__						=dataset
       a__						=seq_length
       a__						=0
       a__						=infinite
       a__						=0
       a__						=tokenized
  
       if self.tokenized:
         a__						=seq_length * num_of_sequences
         a__						='''input_ids'''
       else:
         a__						=seq_length * chars_per_token * num_of_sequences
         a__						='''content'''
  
  
  
     def __iter__(       self )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=iter(self.dataset )
       a__						=True
       while more_examples:
         a__,   a__						=[], 0
         while True:
           if buffer_len >= self.max_buffer_size:
             break
           try:
             buffer.append(next(lowercase_ )[self.content_field] )
             buffer_len += len(buffer[-1] )
           except StopIteration:
             if self.infinite:
               a__						=iter(self.dataset )
               self.epoch += 1
               logger.info(F"""Dataset epoch: {self.epoch}""" )
             else:
               a__						=False
               break
         if self.tokenized:
           a__						=buffer
         else:
           a__						=self.tokenizer(lowercase_,					truncation=lowercase_ )['''input_ids''']
         a__						=[]
         for tokenized_input in tokenized_inputs:
           all_token_ids.extend(tokenized_input + [self.concat_token_id] )
         for i in range(0,					len(lowercase_ ),					self.seq_length ):
           a__						=all_token_ids[i : i + self.seq_length]
           if len(lowercase_ ) == self.seq_length:
             self.current_size += 1
             yield torch.tensor(lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_=1000 )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return ShufflerIterDataPipe(self,					buffer_size=lowercase_ )
  
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=args.model_ckpt.split('''/'''							)[-1]
    a__						=logging.getLogger(__name__							)
    a__						=Path(args.save_dir							) / '''log/'''
    log_dir.mkdir(exist_ok=_A							)
    a__						=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(_A			,   vars(_A							)							)
      a__						=accelerator.trackers[0].run.name
      logger.setLevel(logging.INFO							)
      datasets.utils.logging.set_verbosity_info()
      transformers.utils.logging.set_verbosity_info()
    else:
      a__						=''''''
      logger.setLevel(logging.ERROR							)
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
    return logger, run_name
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						={'''streaming''': True}
    a__						=load_dataset(args.dataset_name_train			,   split='''train'''			,   **_A							)
    a__						=train_data.shuffle(buffer_size=args.shuffle_buffer			,   seed=args.seed							)
    a__						=load_dataset(args.dataset_name_valid			,   split='''train'''			,   **_A							)
    a__						=ConstantLengthDataset(
        _A			,   _A			,   infinite=_A			,   seq_length=args.seq_length			,   tokenized=args.tokenized							)
    a__						=ConstantLengthDataset(
        _A			,   _A			,   infinite=_A			,   seq_length=args.seq_length			,   tokenized=args.tokenized							)
    a__						=train_dataset.shuffle(buffer_size=args.shuffle_buffer							)
    a__						=DataLoader(_A			,   batch_size=args.train_batch_size			,   shuffle=_A							)
    a__						=DataLoader(_A			,   batch_size=args.valid_batch_size							)
    return train_dataloader, eval_dataloader
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Dict			,   _A   :				List[Any]			,   _A   :				str=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__,   a__						=[], []
    for n, p in model.named_parameters():
      if any(nd in n for nd in no_decay							):
        params_without_wd.append(_A							)
      else:
        params_with_wd.append(_A							)
    return [
        {"params": params_with_wd, "weight_decay": args.weight_decay},
        {"params": params_without_wd, "weight_decay": 0.0},
    ]
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				Optional[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    logger.info(F"""Step {step}: {metrics}"""							)
    if accelerator.is_main_process:
      accelerator.log(_A			,   _A							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				str			,   _A   :				List[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=accelerator.unwrap_model(_A							).config
    a__						=4 if args.gradient_checkpointing else 3
    a__						=args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    a__						=24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    a__						=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))
    )
    a__						=flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				int							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    model.eval()
    a__						=[]
    for step, batch in enumerate(_A							):
      with torch.no_grad():
        a__						=model(_A			,   labels=_A							)
      a__						=outputs.loss.repeat(args.valid_batch_size							)
      losses.append(accelerator.gather(_A							)							)
      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
        break
    a__						=torch.cat(_A							)
    a__						=losses[: eval_dataloader.dataset.current_size].mean()
    try:
      a__						=torch.exp(_A							)
    except OverflowError:
      a__						=float('''inf'''							)
    return loss.item(), perplexity.item()
  
  
  # Settings
  lowerCamelCase     =      HfArgumentParser(TrainingArguments)
  lowerCamelCase     =      parser.parse_args()
  
  # Accelerator
  lowerCamelCase     =      ProjectConfiguration(project_dir=args.save_dir, logging_dir='''log''')
  lowerCamelCase     =      Accelerator(log_with=['''wandb''', '''tensorboard'''], project_config=config)
  lowerCamelCase     =      {str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  lowerCamelCase     =      Namespace(**vars(args), **acc_state)
  lowerCamelCase     =      accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
         lowerCamelCase     =      Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  lowerCamelCase  , lowerCamelCase     =      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
  lowerCamelCase     =      AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
         model.gradient_checkpointing_enable()
  lowerCamelCase     =      AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  lowerCamelCase  , lowerCamelCase     =      create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  lowerCamelCase     =      AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  lowerCamelCase     =      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     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  lowerCamelCase  , lowerCamelCase  , lowerCamelCase  , lowerCamelCase     =      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)
                lowerCamelCase     =      os.path.basename(args.resume_from_checkpoint)
         else:
                # Get the most recent checkpoint
                lowerCamelCase     =      [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)
                lowerCamelCase     =      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
         # Extract the step of the checkpoint to continue from there
         lowerCamelCase     =      os.path.splitext(path)[0]
         lowerCamelCase     =      int(training_difference.replace('''step_''', ''''''))
  
  # Train model
  model.train()
  lowerCamelCase     =      0
  lowerCamelCase     =      time.time()
  lowerCamelCase     =      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
         lowerCamelCase     =      model(batch, labels=batch, use_cache=False).loss
         lowerCamelCase     =      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()})
         lowerCamelCase     =      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:
                lowerCamelCase     =      get_lr()
                accelerator.backward(loss)
                accelerator.clip_grad_norm_(model.parameters(), 1.0)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
                lowerCamelCase     =      time.time() - t_start
                lowerCamelCase     =      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,
                    },
                )
                lowerCamelCase     =      time.time()
                lowerCamelCase     =      0
                completed_steps += 1
         if step % args.save_checkpoint_steps == 0:
                logger.info('''Evaluating and saving model checkpoint''')
                lowerCamelCase  , lowerCamelCase     =      evaluate(args)
                log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
                accelerator.wait_for_everyone()
                lowerCamelCase     =      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''')
  lowerCamelCase  , lowerCamelCase     =      evaluate(args)
  log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
  accelerator.wait_for_everyone()
  lowerCamelCase     =      accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  lowerCamelCase     =      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
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
         from .tokenization_xglm import XGLMTokenizer
  else:
         lowerCamelCase     =      None
  
  
  lowerCamelCase     =      logging.get_logger(__name__)
  
  lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  lowerCamelCase     =      {
      '''vocab_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
      },
      '''tokenizer_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
      },
  }
  
  lowerCamelCase     =      {
      '''facebook/xglm-564M''': 2048,
  }
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
     lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
     lowerCamelCase__				:			Any												=     XGLMTokenizer
  
  
  
     def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=7
       a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
  
       a__						=kwargs.get('''additional_special_tokens''',					[] )
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]
  
       super().__init__(
           lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)
  
       a__						=vocab_file
       a__						=False if not self.vocab_file else True
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
       a__						=[self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[self.sep_token_id]
  
       if token_ids_a is None:
         return len(sep + token_ids_a ) * [0]
       return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.''' )
  
       if not os.path.isdir(lowercase_ ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
         return
       a__						=os.path.join(
           lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
  
       if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
         copyfile(self.vocab_file,					lowercase_ )
  
       return (out_vocab_file,)
  