code: 
  """simple docstring"""
  
  
  
  
  
  
  
  from __future__ import annotations
  def 			__UpperCAmelCase		(		a_			:		list[int]				)     ->       list[int]:
     if not list_of_ints:
        return []
  
     if min(a_				) < 0:
        raise ValueError("All numbers must be positive"				)
  
     _UpperCAmelCase   	=					max(len(bin(a_				)[2:]				) for x in list_of_ints				)
     return _msd_radix_sort(a_    ,  a_				)
  def 			__UpperCAmelCase		(		a_			:		list[int]    ,  a_			:		int				)     ->       list[int]:
     if bit_position == 0 or len(a_				) in [0, 1]:
        return list_of_ints
  
     _UpperCAmelCase   	=					[]
     _UpperCAmelCase   	=					[]
     # Split numbers based on bit at bit_position from the right
     for number in list_of_ints:
        if (number >> (bit_position - 1)) & 1:
           # number has a one at bit bit_position
           ones.append(a_				)
        else:
           # number has a zero at bit bit_position
           zeros.append(a_				)
  
        # recursively split both lists further
     _UpperCAmelCase   	=					_msd_radix_sort(a_    ,  bit_position - 1				)
     _UpperCAmelCase   	=					_msd_radix_sort(a_    ,  bit_position - 1				)
  
     # recombine lists
     _UpperCAmelCase   	=					zeros
     res.extend(a_				)
  
     return res
  def 			__UpperCAmelCase		(		a_			:		list[int]				)     ->       int:
     _UpperCAmelCase   	=					len(a_				)
     if not list_of_ints or length == 1:
        return
  
     if min(a_				) < 0:
        raise ValueError("All numbers must be positive"				)
  
     _UpperCAmelCase   	=					max(len(bin(a_				)[2:]				) for x in list_of_ints				)
     _msd_radix_sort_inplace(a_    ,  a_    ,  0    ,  a_				)
  def 			__UpperCAmelCase		(		a_			:		list[int]    ,  a_			:		int    ,  a_			:		int    ,  a_			:		int				)     ->       Union[str, Any]:
     if bit_position == 0 or end_index - begin_index <= 1:
        return
  
     bit_position -= 1
  
     _UpperCAmelCase   	=					begin_index
     _UpperCAmelCase   	=					end_index - 1
     while i <= j:
        _UpperCAmelCase   	=					False
        if not (list_of_ints[i] >> bit_position) & 1:
           # found zero at the beginning
           i += 1
           _UpperCAmelCase   	=					True
        if (list_of_ints[j] >> bit_position) & 1:
           # found one at the end
           j -= 1
           _UpperCAmelCase   	=					True
  
        if changed:
           continue
  
        _UpperCAmelCase   	=					list_of_ints[j], list_of_ints[i]
        j -= 1
        if j != i:
           i += 1
  
     _msd_radix_sort_inplace(a_    ,  a_    ,  a_    ,  a_				)
     _msd_radix_sort_inplace(a_    ,  a_    ,  a_    ,  a_				)
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  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     __a			(					lowerCAmelCase_			):
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_=False							,lowerCamelCase_=1024							,lowerCamelCase_=1024							,lowerCamelCase_=3.6							,lowerCamelCase_=False							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					tokenizer
            _UpperCAmelCase   	=					tokenizer.bos_token_id
            _UpperCAmelCase   	=					dataset
            _UpperCAmelCase   	=					seq_length
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					infinite
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					tokenized
  
            if self.tokenized:
               _UpperCAmelCase   	=					seq_length * num_of_sequences
               _UpperCAmelCase   	=					"input_ids"
            else:
               _UpperCAmelCase   	=					seq_length * chars_per_token * num_of_sequences
               _UpperCAmelCase   	=					"content"
  
  
  
  
  
  
  
         def __iter__(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					iter(self.dataset		)
            _UpperCAmelCase   	=					True
            while more_examples:
               _UpperCAmelCase	,							_UpperCAmelCase   	=					[], 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:
                        _UpperCAmelCase   	=					iter(self.dataset		)
                        self.epoch += 1
                        logger.info(f'''Dataset epoch: {self.epoch}'''		)
                     else:
                        _UpperCAmelCase   	=					False
                        break
               if self.tokenized:
                  _UpperCAmelCase   	=					buffer
               else:
                  _UpperCAmelCase   	=					self.tokenizer(lowerCamelCase_							,truncation=lowerCamelCase_		)["input_ids"]
               _UpperCAmelCase   	=					[]
               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		):
                  _UpperCAmelCase   	=					all_token_ids[i : i + self.seq_length]
                  if len(lowerCamelCase_		) == self.seq_length:
                     self.current_size += 1
                     yield torch.tensor(lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_=1000		):
  
  
  
            '''simple docstring'''
  
            return ShufflerIterDataPipe(self							,buffer_size=lowerCamelCase_		)
  
  
  
  def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Dict:
     _UpperCAmelCase   	=					args.model_ckpt.split("/"				)[-1]
     _UpperCAmelCase   	=					logging.getLogger(__name__				)
     _UpperCAmelCase   	=					Path(args.save_dir				) / "log/"
     log_dir.mkdir(exist_ok=a_				)
     _UpperCAmelCase   	=					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_				)				)
        _UpperCAmelCase   	=					accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO				)
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
     else:
        _UpperCAmelCase   	=					""
        logger.setLevel(logging.ERROR				)
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
     return logger, run_name
  def 			__UpperCAmelCase		(		a_			:		Tuple				)     ->       Dict:
     _UpperCAmelCase   	=					{"streaming": True}
     _UpperCAmelCase   	=					load_dataset(args.dataset_name_train    ,  split="train"    ,  **a_				)
     _UpperCAmelCase   	=					train_data.shuffle(buffer_size=args.shuffle_buffer    ,  seed=args.seed				)
     _UpperCAmelCase   	=					load_dataset(args.dataset_name_valid    ,  split="train"    ,  **a_				)
     _UpperCAmelCase   	=					ConstantLengthDataset(
         a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
     _UpperCAmelCase   	=					ConstantLengthDataset(
         a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
     _UpperCAmelCase   	=					train_dataset.shuffle(buffer_size=args.shuffle_buffer				)
     _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.train_batch_size    ,  shuffle=a_				)
     _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.valid_batch_size				)
     return train_dataloader, eval_dataloader
  def 			__UpperCAmelCase		(		a_			:		List[Any]    ,  a_			:		List[Any]    ,  a_			:		Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				)     ->       Dict:
     _UpperCAmelCase	,							_UpperCAmelCase   	=					[], []
     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_			:		List[str]				)     ->       Optional[Any]:
     logger.info(F'''Step {step}: {metrics}'''				)
     if accelerator.is_main_process:
        accelerator.log(a_    ,  a_				)
  def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[Any]    ,  a_			:		List[str]				)     ->       int:
     # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
     _UpperCAmelCase   	=					accelerator.unwrap_model(a_				).config
     _UpperCAmelCase   	=					4 if args.gradient_checkpointing else 3
     _UpperCAmelCase   	=					args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
     _UpperCAmelCase   	=					24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
     _UpperCAmelCase   	=					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))
     )
     _UpperCAmelCase   	=					flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
     return tflops
  def 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
     model.eval()
     _UpperCAmelCase   	=					[]
     for step, batch in enumerate(a_				):
        with torch.no_grad():
           _UpperCAmelCase   	=					model(a_    ,  labels=a_				)
        _UpperCAmelCase   	=					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
     _UpperCAmelCase   	=					torch.cat(a_				)
     _UpperCAmelCase   	=					losses[: eval_dataloader.dataset.current_size].mean()
     try:
        _UpperCAmelCase   	=					torch.exp(a_				)
     except OverflowError:
        _UpperCAmelCase   	=					float("inf"				)
     return loss.item(), perplexity.item()
  
  
  # Settings
  SCREAMING_SNAKE_CASE_     				=   HfArgumentParser(TrainingArguments)
  SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
  
  # Accelerator
  SCREAMING_SNAKE_CASE_     				=   ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
  SCREAMING_SNAKE_CASE_     				=   Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
  SCREAMING_SNAKE_CASE_     				=   {str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  SCREAMING_SNAKE_CASE_     				=   Namespace(**vars(args), **acc_state)
  SCREAMING_SNAKE_CASE_     				=   accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
    SCREAMING_SNAKE_CASE_     				=   Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   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
  SCREAMING_SNAKE_CASE_     				=   AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
    model.gradient_checkpointing_enable()
  SCREAMING_SNAKE_CASE_     				=   AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  SCREAMING_SNAKE_CASE_     				=   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  SCREAMING_SNAKE_CASE_     				=   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		(		)     ->       int:
     return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   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)
      SCREAMING_SNAKE_CASE_     				=   os.path.basename(args.resume_from_checkpoint)
    else:
      # Get the most recent checkpoint
      SCREAMING_SNAKE_CASE_     				=   [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)
      SCREAMING_SNAKE_CASE_     				=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract the step of the checkpoint to continue from there
    SCREAMING_SNAKE_CASE_     				=   os.path.splitext(path)[0]
    SCREAMING_SNAKE_CASE_     				=   int(training_difference.replace('step_', ''))
  
  # Train model
  model.train()
  SCREAMING_SNAKE_CASE_     				=   0
  SCREAMING_SNAKE_CASE_     				=   time.time()
  SCREAMING_SNAKE_CASE_     				=   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
    SCREAMING_SNAKE_CASE_     				=   model(batch, labels=batch, use_cache=False).loss
    SCREAMING_SNAKE_CASE_     				=   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()})
    SCREAMING_SNAKE_CASE_     				=   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:
      SCREAMING_SNAKE_CASE_     				=   get_lr()
      accelerator.backward(loss)
      accelerator.clip_grad_norm_(model.parameters(), 1.0)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      SCREAMING_SNAKE_CASE_     				=   time.time() - t_start
      SCREAMING_SNAKE_CASE_     				=   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,
          },
      )
      SCREAMING_SNAKE_CASE_     				=   time.time()
      SCREAMING_SNAKE_CASE_     				=   0
      completed_steps += 1
    if step % args.save_checkpoint_steps == 0:
      logger.info('Evaluating and saving model checkpoint')
      SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   evaluate(args)
      log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
      accelerator.wait_for_everyone()
      SCREAMING_SNAKE_CASE_     				=   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')
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   evaluate(args)
  log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
  accelerator.wait_for_everyone()
  SCREAMING_SNAKE_CASE_     				=   accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  SCREAMING_SNAKE_CASE_     				=   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')
  
  