



'''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 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					def __init__(	self					:						Any  ,	UpperCamelCase_					:						Optional[int]  ,	UpperCamelCase_					:						Optional[int]  ,	UpperCamelCase_					:						Optional[int]=False  ,	UpperCamelCase_					:						Optional[Any]=1024  ,	UpperCamelCase_					:						List[str]=1024  ,	UpperCamelCase_					:						Optional[int]=3.6  ,	UpperCamelCase_					:						Optional[int]=False  ,	)	->							str:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		tokenizer
						_lowercase       :							Dict  	=		tokenizer.bos_token_id
						_lowercase       :							Optional[int]  	=		dataset
						_lowercase       :							Optional[Any]  	=		seq_length
						_lowercase       :							List[Any]  	=		0
						_lowercase       :							List[str]  	=		infinite
						_lowercase       :							Dict  	=		0
						_lowercase       :							Optional[int]  	=		tokenized

						if self.tokenized:
							_lowercase       :							Any  	=		seq_length * num_of_sequences
							_lowercase       :							Optional[Any]  	=		'input_ids'
						else:
							_lowercase       :							List[Any]  	=		seq_length * chars_per_token * num_of_sequences
							_lowercase       :							Union[str, Any]  	=		'content'




					def __iter__(	self					:						int       )	->							Optional[Any]:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		iter(self.dataset       )
						_lowercase       :							Optional[int]  	=		True
						while more_examples:
							_lowercase	,     _lowercase       :							str  	=		[], 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:
										_lowercase       :							Optional[Any]  	=		iter(self.dataset       )
										self.epoch += 1
										logger.info(F'''Dataset epoch: {self.epoch}'''       )
									else:
										_lowercase       :							Tuple  	=		False
										break
							if self.tokenized:
								_lowercase       :							List[Any]  	=		buffer
							else:
								_lowercase       :							List[Any]  	=		self.tokenizer(UpperCamelCase_  ,	truncation=UpperCamelCase_       )['input_ids']
							_lowercase       :							int  	=		[]
							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       ):
								_lowercase       :							List[Any]  	=		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					:						str  ,	UpperCamelCase_					:						Optional[Any]=1000       )	->							List[Any]:



						'''simple docstring'''





						return ShufflerIterDataPipe(self  ,	buffer_size=UpperCamelCase_       )







def     __UpperCamelCase					(				_lowercase       )			->      Optional[Any]:
	_lowercase       :							List[Any]  	=		args.model_ckpt.split('/'       )[-1]
	_lowercase       :							List[Any]  	=		logging.getLogger(__name__       )
	_lowercase       :							List[Any]  	=		Path(args.save_dir       ) / 'log/'
	log_dir.mkdir(exist_ok=_lowercase       )
	_lowercase       :							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(_lowercase,					vars(_lowercase       )       )
		_lowercase       :							Any  	=		accelerator.trackers[0].run.name
		logger.setLevel(logging.INFO       )
		datasets.utils.logging.set_verbosity_info()
		transformers.utils.logging.set_verbosity_info()
	else:
		_lowercase       :							List[str]  	=		''
		logger.setLevel(logging.ERROR       )
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	return logger, run_name







def     __UpperCamelCase					(				_lowercase       )			->      Tuple:
	_lowercase       :							Any  	=		{'streaming': True}
	_lowercase       :							Union[str, Any]  	=		load_dataset(args.dataset_name_train,					split='train',					**_lowercase       )
	_lowercase       :							Optional[Any]  	=		train_data.shuffle(buffer_size=args.shuffle_buffer,					seed=args.seed       )
	_lowercase       :							str  	=		load_dataset(args.dataset_name_valid,					split='train',					**_lowercase       )
	_lowercase       :							int  	=		ConstantLengthDataset(
	    _lowercase,					_lowercase,					infinite=_lowercase,					seq_length=args.seq_length,					tokenized=args.tokenized       )
	_lowercase       :							Tuple  	=		ConstantLengthDataset(
	    _lowercase,					_lowercase,					infinite=_lowercase,					seq_length=args.seq_length,					tokenized=args.tokenized       )
	_lowercase       :							Tuple  	=		train_dataset.shuffle(buffer_size=args.shuffle_buffer       )
	_lowercase       :							Union[str, Any]  	=		DataLoader(_lowercase,					batch_size=args.train_batch_size,					shuffle=_lowercase       )
	_lowercase       :							List[str]  	=		DataLoader(_lowercase,					batch_size=args.valid_batch_size       )
	return train_dataloader, eval_dataloader







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]       )			->      List[Any]:
	_lowercase	,     _lowercase       :							List[Any]  	=		[], []
	for n, p in model.named_parameters():
		if any(nd in n for nd in no_decay       ):
			params_without_wd.append(_lowercase       )
		else:
			params_with_wd.append(_lowercase       )
	return [
	    {"params": params_with_wd, "weight_decay": args.weight_decay},
	    {"params": params_without_wd, "weight_decay": 0.0},
	]







def     __UpperCamelCase					(				_lowercase,					_lowercase       )			->      Optional[int]:
	logger.info(f'''Step {step}: {metrics}'''       )
	if accelerator.is_main_process:
		accelerator.log(_lowercase,					_lowercase       )







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase       )			->      Optional[Any]:
	# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
	_lowercase       :							int  	=		accelerator.unwrap_model(_lowercase       ).config
	_lowercase       :							str  	=		4 if args.gradient_checkpointing else 3
	_lowercase       :							Tuple  	=		args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
	_lowercase       :							Optional[Any]  	=		24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
	_lowercase       :							List[Any]  	=		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))
	)
	_lowercase       :							Any  	=		flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
	return tflops







def     __UpperCamelCase					(				_lowercase       )			->      Optional[int]:
	model.eval()
	_lowercase       :							Optional[Any]  	=		[]
	for step, batch in enumerate(_lowercase       ):
		with torch.no_grad():
			_lowercase       :							Optional[Any]  	=		model(_lowercase,					labels=_lowercase       )
		_lowercase       :							Any  	=		outputs.loss.repeat(args.valid_batch_size       )
		losses.append(accelerator.gather(_lowercase       )       )
		if args.max_eval_steps > 0 and step >= args.max_eval_steps:
			break
	_lowercase       :							List[str]  	=		torch.cat(_lowercase       )
	_lowercase       :							List[Any]  	=		losses[: eval_dataloader.dataset.current_size].mean()
	try:
		_lowercase       :							List[str]  	=		torch.exp(_lowercase       )
	except OverflowError:
		_lowercase       :							Any  	=		float('inf'       )
	return loss.item(), perplexity.item()


# Settings
_A					:     List[Any]      =HfArgumentParser(TrainingArguments)
_A					:     Any      =parser.parse_args()

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

_A					:     Any      =Namespace(**vars(args), **acc_state)
_A					:     int      =accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
_A    ,      _A					:     Tuple      =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
_A					:     Any      =AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
_A					:     Tuple      =AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_A    ,      _A					:     Optional[int]      =create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_A					:     Union[str, Any]      =AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_A					:     List[str]      =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					(				)			->      Union[str, Any]:
	return optimizer.param_groups[0]["lr"]


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

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

from __future__ import annotations

import sys







def     __UpperCamelCase					(				_lowercase       )			->      int:
	if n < 0:
		raise ValueError('Negative arguments are not supported'       )
	return _fib(_lowercase       )[0]







def     __UpperCamelCase					(				_lowercase       )			->      tuple[int, int]:
	if n == 0:  # (F(0), F(1))
		return (0, 1)

	# F(2n) = F(n)[2F(n+1) − F(n)]
	# F(2n+1) = F(n+1)^2+F(n)^2
	_lowercase	,     _lowercase       :							List[str]  	=		_fib(n // 2       )
	_lowercase       :							Dict  	=		a * (b * 2 - a)
	_lowercase       :							Union[str, Any]  	=		a * a + b * b
	return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		_A					:     Optional[Any]      =int(sys.argv[1])
		print(F'''fibonacci({n}) is {fibonacci(n)}''')
