



'''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__						(		_SCREAMING_SNAKE_CASE			):



					def __init__(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase=False      ,					UpperCamelCase=1024      ,					UpperCamelCase=1024      ,					UpperCamelCase=3.6      ,					UpperCamelCase=False      ,					)  ->      List[str]:
						__lowerCAmelCase       				=	tokenizer
						__lowerCAmelCase       				=	tokenizer.bos_token_id
						__lowerCAmelCase       				=	dataset
						__lowerCAmelCase       				=	seq_length
						__lowerCAmelCase       				=	0
						__lowerCAmelCase       				=	infinite
						__lowerCAmelCase       				=	0
						__lowerCAmelCase       				=	tokenized

						if self.tokenized:
							__lowerCAmelCase       				=	seq_length * num_of_sequences
							__lowerCAmelCase       				=	"input_ids"
						else:
							__lowerCAmelCase       				=	seq_length * chars_per_token * num_of_sequences
							__lowerCAmelCase       				=	"content"



					def __iter__(     self  )  ->      List[str]:
						__lowerCAmelCase       				=	iter(self.dataset  )
						__lowerCAmelCase       				=	True
						while more_examples:
							__lowerCAmelCase				,							__lowerCAmelCase       				=	[], 0
							while True:
								if buffer_len >= self.max_buffer_size:
									break
								try:
									buffer.append(next(UpperCamelCase  )[self.content_field]  )
									buffer_len += len(buffer[-1]  )
								except StopIteration:
									if self.infinite:
										__lowerCAmelCase       				=	iter(self.dataset  )
										self.epoch += 1
										logger.info(F'''Dataset epoch: {self.epoch}'''  )
									else:
										__lowerCAmelCase       				=	False
										break
							if self.tokenized:
								__lowerCAmelCase       				=	buffer
							else:
								__lowerCAmelCase       				=	self.tokenizer(UpperCamelCase      ,					truncation=UpperCamelCase  )["input_ids"]
							__lowerCAmelCase       				=	[]
							for tokenized_input in tokenized_inputs:
								all_token_ids.extend(tokenized_input + [self.concat_token_id]  )
							for i in range(0      ,					len(UpperCamelCase  )      ,					self.seq_length  ):
								__lowerCAmelCase       				=	all_token_ids[i : i + self.seq_length]
								if len(UpperCamelCase  ) == self.seq_length:
									self.current_size += 1
									yield torch.tensor(UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase=1000  )  ->      Tuple:
						return ShufflerIterDataPipe(self      ,					buffer_size=UpperCamelCase  )



def 	__lowerCAmelCase       (				lowerCamelCase :    Dict       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	args.model_ckpt.split("/"       )[-1]
	__lowerCAmelCase       				=	logging.getLogger(__name__       )
	__lowerCAmelCase       				=	Path(args.save_dir       ) / "log/"
	log_dir.mkdir(exist_ok=lowerCAmelCase__       )
	__lowerCAmelCase       				=	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       				=	accelerator.trackers[0].run.name
		logger.setLevel(logging.INFO       )
		datasets.utils.logging.set_verbosity_info()
		transformers.utils.logging.set_verbosity_info()
	else:
		__lowerCAmelCase       				=	""
		logger.setLevel(logging.ERROR       )
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	return logger, run_name



def 	__lowerCAmelCase       (				lowerCamelCase :    Optional[int]       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	{"streaming": True}
	__lowerCAmelCase       				=	load_dataset(args.dataset_name_train					,		split="train"					,		**lowerCAmelCase__       )
	__lowerCAmelCase       				=	train_data.shuffle(buffer_size=args.shuffle_buffer					,		seed=args.seed       )
	__lowerCAmelCase       				=	load_dataset(args.dataset_name_valid					,		split="train"					,		**lowerCAmelCase__       )
	__lowerCAmelCase       				=	ConstantLengthDataset(
	    lowerCAmelCase__					,		lowerCAmelCase__					,		infinite=lowerCAmelCase__					,		seq_length=args.seq_length					,		tokenized=args.tokenized       )
	__lowerCAmelCase       				=	ConstantLengthDataset(
	    lowerCAmelCase__					,		lowerCAmelCase__					,		infinite=lowerCAmelCase__					,		seq_length=args.seq_length					,		tokenized=args.tokenized       )
	__lowerCAmelCase       				=	train_dataset.shuffle(buffer_size=args.shuffle_buffer       )
	__lowerCAmelCase       				=	DataLoader(lowerCAmelCase__					,		batch_size=args.train_batch_size					,		shuffle=lowerCAmelCase__       )
	__lowerCAmelCase       				=	DataLoader(lowerCAmelCase__					,		batch_size=args.valid_batch_size       )
	return train_dataloader, eval_dataloader



def 	__lowerCAmelCase       (				lowerCamelCase :    Dict					,		lowerCamelCase :    Optional[int]					,		lowerCamelCase :    Any=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]       ):






	'''simple docstring'''




	__lowerCAmelCase				,							__lowerCAmelCase       				=	[], []
	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 :    str					,		lowerCamelCase :    Union[str, Any]       ):






	'''simple docstring'''




	logger.info(f'''Step {step}: {metrics}'''       )
	if accelerator.is_main_process:
		accelerator.log(lowerCAmelCase__					,		lowerCAmelCase__       )



def 	__lowerCAmelCase       (				lowerCamelCase :    List[Any]					,		lowerCamelCase :    str					,		lowerCamelCase :    Tuple       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	accelerator.unwrap_model(lowerCAmelCase__       ).config
	__lowerCAmelCase       				=	4 if args.gradient_checkpointing else 3
	__lowerCAmelCase       				=	args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
	__lowerCAmelCase       				=	24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
	__lowerCAmelCase       				=	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       				=	flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
	return tflops



def 	__lowerCAmelCase       (				lowerCamelCase :    Any       ):






	'''simple docstring'''




	model.eval()
	__lowerCAmelCase       				=	[]
	for step, batch in enumerate(lowerCAmelCase__       ):
		with torch.no_grad():
			__lowerCAmelCase       				=	model(lowerCAmelCase__					,		labels=lowerCAmelCase__       )
		__lowerCAmelCase       				=	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       				=	torch.cat(lowerCAmelCase__       )
	__lowerCAmelCase       				=	losses[: eval_dataloader.dataset.current_size].mean()
	try:
		__lowerCAmelCase       				=	torch.exp(lowerCAmelCase__       )
	except OverflowError:
		__lowerCAmelCase       				=	float("inf"       )
	return loss.item(), perplexity.item()


# Settings
lowerCAmelCase						:		str						      = HfArgumentParser(TrainingArguments)
lowerCAmelCase						:		Any						      = parser.parse_args()

# Accelerator
lowerCAmelCase						:		Union[str, Any]						      = ProjectConfiguration(project_dir=args.save_dir, logging_dir='''log''')
lowerCAmelCase						:		Optional[Any]						      = Accelerator(log_with=['''wandb''', '''tensorboard'''], project_config=config)
lowerCAmelCase						:		List[str]						      = {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

lowerCAmelCase						:		str						      = Namespace(**vars(args), **acc_state)
lowerCAmelCase						:		Union[str, Any]						      = accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
	lowerCAmelCase						:		Union[str, Any]						      = Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
lowerCAmelCase						:		List[str]						      = 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						:		Optional[int]						      = AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
	model.gradient_checkpointing_enable()
lowerCAmelCase						:		Any						      = AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowerCAmelCase						:		Any						      = create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowerCAmelCase						:		Dict						      = AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCAmelCase						:		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       (				):






	'''simple docstring'''




	return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
lowerCAmelCase						:		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)
		lowerCAmelCase						:		Any						      = os.path.basename(args.resume_from_checkpoint)
	else:
		# Get the most recent checkpoint
		lowerCAmelCase						:		Dict						      = [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						:		List[str]						      = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
	# Extract the step of the checkpoint to continue from there
	lowerCAmelCase						:		List[Any]						      = os.path.splitext(path)[0]
	lowerCAmelCase						:		int						      = int(training_difference.replace('''step_''', ''''''))

# Train model
model.train()
lowerCAmelCase						:		Dict						      = 0
lowerCAmelCase						:		str						      = time.time()
lowerCAmelCase						:		Optional[int]						      = 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						:		int						      = model(batch, labels=batch, use_cache=False).loss
	lowerCAmelCase						:		str						      = 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						:		List[str]						      = 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						:		str						      = get_lr()
		accelerator.backward(loss)
		accelerator.clip_grad_norm_(model.parameters(), 1.0)
		optimizer.step()
		lr_scheduler.step()
		optimizer.zero_grad()
		lowerCAmelCase						:		Any						      = time.time() - t_start
		lowerCAmelCase						:		Optional[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,
		    },
		)
		lowerCAmelCase						:		str						      = time.time()
		lowerCAmelCase						:		Tuple						      = 0
		completed_steps += 1
	if step % args.save_checkpoint_steps == 0:
		logger.info('''Evaluating and saving model checkpoint''')
		lowerCAmelCase						:		Union[str, Any]						      = evaluate(args)
		log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
		accelerator.wait_for_everyone()
		lowerCAmelCase						:		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=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						:		Tuple						      = evaluate(args)
log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
accelerator.wait_for_everyone()
lowerCAmelCase						:		Union[str, Any]						      = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
lowerCAmelCase						:		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='''final model''')



'''simple docstring'''






def 						_A					(lowerCAmelCase__	:int					,    lowerCAmelCase__	:int		) ->	int:




     '''simple docstring'''



     if a < b:
          return gcd(lowerCAmelCase__					,    lowerCAmelCase__		)
     if a % b == 0:
          return b
     return gcd(lowerCAmelCase__					,    a % b		)






def 						_A					(lowerCAmelCase__	:int					,    lowerCAmelCase__	:int					,    lowerCAmelCase__	:int		) ->	int:




     '''simple docstring'''



     if y == 0:
          return 1
     _a         =				power(lowerCAmelCase__					,    y // 2					,    lowerCAmelCase__		) % mod
     _a         =				(temp * temp) % mod
     if y % 2 == 1:
          _a         =				(temp * x) % mod
     return temp






def 						_A					(lowerCAmelCase__	:int		) ->	bool:




     '''simple docstring'''



     _a         =				2
     while b < n:
          if gcd(lowerCAmelCase__					,    lowerCAmelCase__		) == 1 and power(lowerCAmelCase__					,    n - 1					,    lowerCAmelCase__		) != 1:
               return False
          b += 1
     return True


if __name__ == "__main__":
 a_       :     Any 							=     int(input("Enter number: ").strip())
 if is_carmichael_number(number):
  print(f'''{number} is a Carmichael Number.''')
 else:
  print(f'''{number} is not a Carmichael Number.''')

