code: 
  
  
  
  
  
  
  '''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 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase=False  ,	_lowerCAmelCase=1024  ,	_lowerCAmelCase=1024  ,	_lowerCAmelCase=3.6  ,	_lowerCAmelCase=False  ,	):
            UpperCAmelCase__			:     Union[str, Any]										= tokenizer
            UpperCAmelCase__			:     Tuple										= tokenizer.bos_token_id
            UpperCAmelCase__			:     List[str]										= dataset
            UpperCAmelCase__			:     Optional[int]										= seq_length
            UpperCAmelCase__			:     List[Any]										= 0
            UpperCAmelCase__			:     int										= infinite
            UpperCAmelCase__			:     int										= 0
            UpperCAmelCase__			:     str										= tokenized
  
            if self.tokenized:
                 UpperCAmelCase__			:     Optional[int]										= seq_length * num_of_sequences
                 UpperCAmelCase__			:     Optional[Any]										= """input_ids"""
            else:
                 UpperCAmelCase__			:     List[Any]										= seq_length * chars_per_token * num_of_sequences
                 UpperCAmelCase__			:     Dict										= """content"""
  
  
  
  
       def __iter__(							self		):
            UpperCAmelCase__			:     Union[str, Any]										= iter(self.dataset		)
            UpperCAmelCase__			:     Optional[int]										= True
            while more_examples:
                 UpperCAmelCase__			:     Optional[Any]										= [], 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__			:     Dict										= iter(self.dataset		)
                                self.epoch += 1
                                logger.info(f"Dataset epoch: {self.epoch}"		)
                           else:
                                UpperCAmelCase__			:     Tuple										= False
                                break
                 if self.tokenized:
                      UpperCAmelCase__			:     Union[str, Any]										= buffer
                 else:
                      UpperCAmelCase__			:     List[str]										= self.tokenizer(_lowerCAmelCase  ,	truncation=_lowerCAmelCase		)["""input_ids"""]
                 UpperCAmelCase__			:     List[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		):
                      UpperCAmelCase__			:     Tuple										= all_token_ids[i : i + self.seq_length]
                      if len(_lowerCAmelCase		) == self.seq_length:
                           self.current_size += 1
                           yield torch.tensor(_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase=1000		):
            return ShufflerIterDataPipe(self  ,	buffer_size=_lowerCAmelCase		)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> List[str]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     str										= args.model_ckpt.split("""/"""					)[-1]
       UpperCAmelCase__			:     int										= logging.getLogger(__name__					)
       UpperCAmelCase__			:     int										= Path(args.save_dir					) / """log/"""
       log_dir.mkdir(exist_ok=__lowerCamelCase					)
       UpperCAmelCase__			:     int										= 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					)					)
            UpperCAmelCase__			:     List[Any]										= accelerator.trackers[0].run.name
            logger.setLevel(logging.INFO					)
            datasets.utils.logging.set_verbosity_info()
            transformers.utils.logging.set_verbosity_info()
       else:
            UpperCAmelCase__			:     Tuple										= """"""
            logger.setLevel(logging.ERROR					)
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
       return logger, run_name
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict:
  
       '''simple docstring'''
       UpperCAmelCase__			:     int										= {"""streaming""": True}
       UpperCAmelCase__			:     str										= load_dataset(args.dataset_name_train   ,       split="""train"""   ,       **__lowerCamelCase					)
       UpperCAmelCase__			:     Union[str, Any]										= train_data.shuffle(buffer_size=args.shuffle_buffer   ,       seed=args.seed					)
       UpperCAmelCase__			:     int										= load_dataset(args.dataset_name_valid   ,       split="""train"""   ,       **__lowerCamelCase					)
       UpperCAmelCase__			:     Any										= ConstantLengthDataset(
           __lowerCamelCase   ,       __lowerCamelCase   ,       infinite=__lowerCamelCase   ,       seq_length=args.seq_length   ,       tokenized=args.tokenized					)
       UpperCAmelCase__			:     List[str]										= ConstantLengthDataset(
           __lowerCamelCase   ,       __lowerCamelCase   ,       infinite=__lowerCamelCase   ,       seq_length=args.seq_length   ,       tokenized=args.tokenized					)
       UpperCAmelCase__			:     Any										= train_dataset.shuffle(buffer_size=args.shuffle_buffer					)
       UpperCAmelCase__			:     Optional[Any]										= DataLoader(__lowerCamelCase   ,       batch_size=args.train_batch_size   ,       shuffle=__lowerCamelCase					)
       UpperCAmelCase__			:     Any										= DataLoader(__lowerCamelCase   ,       batch_size=args.valid_batch_size					)
       return train_dataloader, eval_dataloader
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]					)   -> Optional[int]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:
  
       '''simple docstring'''
       logger.info(F"Step {step}: {metrics}"					)
       if accelerator.is_main_process:
            accelerator.log(__lowerCamelCase   ,       __lowerCamelCase					)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Optional[int]										= accelerator.unwrap_model(__lowerCamelCase					).config
       UpperCAmelCase__			:     int										= 4 if args.gradient_checkpointing else 3
       UpperCAmelCase__			:     Optional[int]										= args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
       UpperCAmelCase__			:     int										= 24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
       UpperCAmelCase__			:     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))
       )
       UpperCAmelCase__			:     Optional[Any]										= flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
       return tflops
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict:
  
       '''simple docstring'''
       model.eval()
       UpperCAmelCase__			:     Tuple										= []
       for step, batch in enumerate(__lowerCamelCase					):
            with torch.no_grad():
                 UpperCAmelCase__			:     int										= model(__lowerCamelCase   ,       labels=__lowerCamelCase					)
            UpperCAmelCase__			:     Tuple										= 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
       UpperCAmelCase__			:     Union[str, Any]										= torch.cat(__lowerCamelCase					)
       UpperCAmelCase__			:     List[Any]										= losses[: eval_dataloader.dataset.current_size].mean()
       try:
            UpperCAmelCase__			:     Any										= torch.exp(__lowerCamelCase					)
       except OverflowError:
            UpperCAmelCase__			:     List[str]										= float("""inf"""					)
       return loss.item(), perplexity.item()
  
  
  # Settings
  SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							HfArgumentParser(TrainingArguments)
  SCREAMING_SNAKE_CASE__		:       int   							=							parser.parse_args()
  
  # Accelerator
  SCREAMING_SNAKE_CASE__		:       Dict   							=							ProjectConfiguration(project_dir=args.save_dir, logging_dir="""log""")
  SCREAMING_SNAKE_CASE__		:       List[Any]   							=							Accelerator(log_with=["""wandb""", """tensorboard"""], project_config=config)
  SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							{str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  SCREAMING_SNAKE_CASE__		:       int   							=							Namespace(**vars(args), **acc_state)
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
        SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  SCREAMING_SNAKE_CASE__		:       Any   							=							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__		:       Any   							=							AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
        model.gradient_checkpointing_enable()
  SCREAMING_SNAKE_CASE__		:       Any   							=							AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  SCREAMING_SNAKE_CASE__		:       Tuple   							=							create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  SCREAMING_SNAKE_CASE__		:       str   							=							AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  SCREAMING_SNAKE_CASE__		:       Any   							=							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    _lowerCamelCase	(	)   -> str:
  
       '''simple docstring'''
       return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  SCREAMING_SNAKE_CASE__		:       Any   							=							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__		:       Optional[Any]   							=							os.path.basename(args.resume_from_checkpoint)
        else:
              # Get the most recent checkpoint
              SCREAMING_SNAKE_CASE__		:       Tuple   							=							[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__		:       Union[str, Any]   							=							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__		:       Dict   							=							os.path.splitext(path)[0]
        SCREAMING_SNAKE_CASE__		:       List[str]   							=							int(training_difference.replace("""step_""", """"""))
  
  # Train model
  model.train()
  SCREAMING_SNAKE_CASE__		:       Any   							=							0
  SCREAMING_SNAKE_CASE__		:       List[str]   							=							time.time()
  SCREAMING_SNAKE_CASE__		:       List[Any]   							=							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__		:       Union[str, Any]   							=							model(batch, labels=batch, use_cache=False).loss
        SCREAMING_SNAKE_CASE__		:       Optional[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()})
        SCREAMING_SNAKE_CASE__		:       List[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:
              SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							get_lr()
              accelerator.backward(loss)
              accelerator.clip_grad_norm_(model.parameters(), 1.0)
              optimizer.step()
              lr_scheduler.step()
              optimizer.zero_grad()
              SCREAMING_SNAKE_CASE__		:       List[str]   							=							time.time() - t_start
              SCREAMING_SNAKE_CASE__		:       List[str]   							=							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__		:       List[Any]   							=							time.time()
              SCREAMING_SNAKE_CASE__		:       int   							=							0
              completed_steps += 1
        if step % args.save_checkpoint_steps == 0:
              logger.info("""Evaluating and saving model checkpoint""")
              SCREAMING_SNAKE_CASE__		:       Dict   							=							evaluate(args)
              log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
              accelerator.wait_for_everyone()
              SCREAMING_SNAKE_CASE__		:       int   							=							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__		:       str   							=							evaluate(args)
  log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
  accelerator.wait_for_everyone()
  SCREAMING_SNAKE_CASE__		:       Dict   							=							accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  SCREAMING_SNAKE_CASE__		:       Union[str, 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="""final model""")
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import random
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     Union[str, Any]										= x
            UpperCAmelCase__			:     Union[str, Any]										= y
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            return (self.x**2 + self.y**2) <= 1
  
  
  
  
       @classmethod
       def      __UpperCAmelCase     (							cls		):
            return cls(x=random.random()  ,	y=random.random()		)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> float:
  
       '''simple docstring'''
       if number_of_simulations < 1:
            raise ValueError("""At least one simulation is necessary to estimate PI."""					)
  
       UpperCAmelCase__			:     Dict										= 0
       for _ in range(__lowerCamelCase					):
            UpperCAmelCase__			:     Union[str, Any]										= Point.random_unit_square()
  
            if random_point.is_in_unit_circle():
                 number_in_unit_circle += 1
  
       return 4 * number_in_unit_circle / number_of_simulations
  
  
  if __name__ == "__main__":
        # import doctest
  
        # doctest.testmod()
        from math import pi
  
        SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""Please enter the desired number of Monte Carlo simulations: """
        SCREAMING_SNAKE_CASE__		:       int   							=							estimate_pi(int(input(prompt).strip()))
        print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')
  
  
  
  
  
  
  