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		,       __a		,       __a		,       __a=False		,       __a=1024		,       __a=1024		,       __a=3.6		,       __a=False		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[int]      					=						tokenizer
  						__a    : Union[str, Any]      					=						tokenizer.bos_token_id
  						__a    : Any      					=						dataset
  						__a    : str      					=						seq_length
  						__a    : int      					=						0
  						__a    : Tuple      					=						infinite
  						__a    : Dict      					=						0
  						__a    : Tuple      					=						tokenized
  
  						if self.tokenized:
  											__a    : List[str]      					=						seq_length * num_of_sequences
  											__a    : Dict      					=						'input_ids'
  						else:
  											__a    : Optional[Any]      					=						seq_length * chars_per_token * num_of_sequences
  											__a    : List[str]      					=						'content'
  
  
  
  
  	def __iter__(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : int      					=						iter(self.dataset							)
  						__a    : List[Any]      					=						True
  						while more_examples:
  											__a					,       __a    : List[str]      					=						[], 0
  											while True:
  																if buffer_len >= self.max_buffer_size:
  																					break
  																try:
  																					buffer.append(next(__a							)[self.content_field]							)
  																					buffer_len += len(buffer[-1]							)
  																except StopIteration:
  																					if self.infinite:
  																										__a    : Dict      					=						iter(self.dataset							)
  																										self.epoch += 1
  																										logger.info(f"""Dataset epoch: {self.epoch}"""							)
  																					else:
  																										__a    : List[str]      					=						False
  																										break
  											if self.tokenized:
  																__a    : List[Any]      					=						buffer
  											else:
  																__a    : Optional[int]      					=						self.tokenizer(__a		,       truncation=__a							)['input_ids']
  											__a    : Optional[Any]      					=						[]
  											for tokenized_input in tokenized_inputs:
  																all_token_ids.extend(tokenized_input + [self.concat_token_id]							)
  											for i in range(0		,       len(__a							)		,       self.seq_length							):
  																__a    : Optional[int]      					=						all_token_ids[i : i + self.seq_length]
  																if len(__a							) == self.seq_length:
  																					self.current_size += 1
  																					yield torch.tensor(__a							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a=1000							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return ShufflerIterDataPipe(self		,       buffer_size=__a							)
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any     ):
  					__a    : Dict      					=						args.model_ckpt.split('/'     )[-1]
  					__a    : Union[str, Any]      					=						logging.getLogger(__name__     )
  					__a    : List[Any]      					=						Path(args.save_dir     ) / 'log/'
  					log_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE     )
  					__a    : Union[str, Any]      					=						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(_SCREAMING_SNAKE_CASE		,   vars(_SCREAMING_SNAKE_CASE     )     )
  										__a    : str      					=						accelerator.trackers[0].run.name
  										logger.setLevel(logging.INFO     )
  										datasets.utils.logging.set_verbosity_info()
  										transformers.utils.logging.set_verbosity_info()
  					else:
  										__a    : str      					=						''
  										logger.setLevel(logging.ERROR     )
  										datasets.utils.logging.set_verbosity_error()
  										transformers.utils.logging.set_verbosity_error()
  					return logger, run_name
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict     ):
  					__a    : int      					=						{'streaming': True}
  					__a    : Union[str, Any]      					=						load_dataset(args.dataset_name_train		,   split='train'		,   **_SCREAMING_SNAKE_CASE     )
  					__a    : str      					=						train_data.shuffle(buffer_size=args.shuffle_buffer		,   seed=args.seed     )
  					__a    : Optional[Any]      					=						load_dataset(args.dataset_name_valid		,   split='train'		,   **_SCREAMING_SNAKE_CASE     )
  					__a    : Any      					=						ConstantLengthDataset(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   infinite=_SCREAMING_SNAKE_CASE		,   seq_length=args.seq_length		,   tokenized=args.tokenized     )
  					__a    : Union[str, Any]      					=						ConstantLengthDataset(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   infinite=_SCREAMING_SNAKE_CASE		,   seq_length=args.seq_length		,   tokenized=args.tokenized     )
  					__a    : Dict      					=						train_dataset.shuffle(buffer_size=args.shuffle_buffer     )
  					__a    : int      					=						DataLoader(_SCREAMING_SNAKE_CASE		,   batch_size=args.train_batch_size		,   shuffle=_SCREAMING_SNAKE_CASE     )
  					__a    : List[str]      					=						DataLoader(_SCREAMING_SNAKE_CASE		,   batch_size=args.valid_batch_size     )
  					return train_dataloader, eval_dataloader
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     List[str]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]     ):
  					__a					,       __a    : Any      					=						[], []
  					for n, p in model.named_parameters():
  										if any(nd in n for nd in no_decay     ):
  															params_without_wd.append(_SCREAMING_SNAKE_CASE     )
  										else:
  															params_with_wd.append(_SCREAMING_SNAKE_CASE     )
  					return [
  					    {"params": params_with_wd, "weight_decay": args.weight_decay},
  					    {"params": params_without_wd, "weight_decay": 0.0},
  					]
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     List[Any]     ):
  					logger.info(F"""Step {step}: {metrics}"""     )
  					if accelerator.is_main_process:
  										accelerator.log(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     str     ):
  					# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
  					__a    : Dict      					=						accelerator.unwrap_model(_SCREAMING_SNAKE_CASE     ).config
  					__a    : List[str]      					=						4 if args.gradient_checkpointing else 3
  					__a    : Optional[Any]      					=						args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
  					__a    : List[str]      					=						24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
  					__a    : str      					=						factor * (
  					    1.0
  					    + (args.seq_length / (6.0 * config_model.n_embd))
  					    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
  					)
  					__a    : Optional[Any]      					=						flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
  					return tflops
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]     ):
  					model.eval()
  					__a    : Optional[Any]      					=						[]
  					for step, batch in enumerate(_SCREAMING_SNAKE_CASE     ):
  										with torch.no_grad():
  															__a    : List[Any]      					=						model(_SCREAMING_SNAKE_CASE		,   labels=_SCREAMING_SNAKE_CASE     )
  										__a    : Tuple      					=						outputs.loss.repeat(args.valid_batch_size     )
  										losses.append(accelerator.gather(_SCREAMING_SNAKE_CASE     )     )
  										if args.max_eval_steps > 0 and step >= args.max_eval_steps:
  															break
  					__a    : Dict      					=						torch.cat(_SCREAMING_SNAKE_CASE     )
  					__a    : Tuple      					=						losses[: eval_dataloader.dataset.current_size].mean()
  					try:
  										__a    : List[Any]      					=						torch.exp(_SCREAMING_SNAKE_CASE     )
  					except OverflowError:
  										__a    : int      					=						float('inf'     )
  					return loss.item(), perplexity.item()
  
  
  # Settings
  __lowercase			:		str =			HfArgumentParser(TrainingArguments)
  __lowercase			:		List[Any] =			parser.parse_args()
  
  # Accelerator
  __lowercase			:		Dict =			ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
  __lowercase			:		Optional[Any] =			Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
  __lowercase			:		Union[str, Any] =			{str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  __lowercase			:		List[str] =			Namespace(**vars(args), **acc_state)
  __lowercase			:		Tuple =			accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
  	__lowercase			:		int =			Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  __lowercase ,   __lowercase			:		Optional[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
  __lowercase			:		Tuple =			AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
  	model.gradient_checkpointing_enable()
  __lowercase			:		Union[str, Any] =			AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  __lowercase ,   __lowercase			:		Dict =			create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  __lowercase			:		str =			AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  __lowercase			:		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							():
  					return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  __lowercase ,   __lowercase ,   __lowercase ,   __lowercase			:		Dict =			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			:		Union[str, Any] =			[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			:		int =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  	# Extract the step of the checkpoint to continue from there
  	__lowercase			:		List[Any] =			os.path.splitext(path)[0]
  	__lowercase			:		Optional[int] =			int(training_difference.replace('step_', ''))
  
  # Train model
  model.train()
  __lowercase			:		Optional[int] =			0
  __lowercase			:		Dict =			time.time()
  __lowercase			:		str =			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			:		Union[str, Any] =			model(batch, labels=batch, use_cache=False).loss
  	__lowercase			:		Optional[Any] =			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			:		Optional[int] =			get_lr()
  		accelerator.backward(loss)
  		accelerator.clip_grad_norm_(model.parameters(), 1.0)
  		optimizer.step()
  		lr_scheduler.step()
  		optimizer.zero_grad()
  		__lowercase			:		Any =			time.time() - t_start
  		__lowercase			:		Optional[int] =			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			:		List[str] =			time.time()
  		__lowercase			:		Tuple =			0
  		completed_steps += 1
  	if step % args.save_checkpoint_steps == 0:
  		logger.info('Evaluating and saving model checkpoint')
  		__lowercase ,   __lowercase			:		int =			evaluate(args)
  		log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
  		accelerator.wait_for_everyone()
  		__lowercase			:		List[str] =			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 ,   __lowercase			:		List[Any] =			evaluate(args)
  log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
  accelerator.wait_for_everyone()
  __lowercase			:		List[str] =			accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  __lowercase			:		List[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: 
  
  
  '''simple docstring'''
  
  
  
  import itertools
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 1_000_000     ):
  					__a    : str      					=						[1]
  
  					for i in itertools.count(len(_SCREAMING_SNAKE_CASE     )     ):
  										__a    : Dict      					=						0
  										for j in itertools.count(1     ):
  															__a    : int      					=						-1 if j % 2 == 0 else +1
  															__a    : Optional[int]      					=						(j * j * 3 - j) // 2
  															if index > i:
  																				break
  															item += partitions[i - index] * sign
  															item %= number
  															index += j
  															if index > i:
  																				break
  															item += partitions[i - index] * sign
  															item %= number
  
  										if item == 0:
  															return i
  										partitions.append(_SCREAMING_SNAKE_CASE     )
  
  					return 0
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  	print(f'''{solution() = }''')
  
  
  
  