


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					( UpperCAmelCase__							):
					'''simple docstring'''






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




							"""simple docstring"""


							lowercase__      =	tokenizer
							lowercase__      =	tokenizer.bos_token_id
							lowercase__      =	dataset
							lowercase__      =	seq_length
							lowercase__      =	0
							lowercase__      =	infinite
							lowercase__      =	0
							lowercase__      =	tokenized

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

					def __iter__(self					:		Dict	)		->     Any:




							"""simple docstring"""


							lowercase__      =	iter(self.dataset	)
							lowercase__      =	True
							while more_examples:
									lowercase__    ,			lowercase__      =	[], 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__      =	iter(self.dataset	)
															self.epoch += 1
															logger.info(f'''Dataset epoch: {self.epoch}'''	)
													else:
															lowercase__      =	False
															break
									if self.tokenized:
											lowercase__      =	buffer
									else:
											lowercase__      =	self.tokenizer(_UpperCAmelCase   ,      truncation=_UpperCAmelCase	)["""input_ids"""]
									lowercase__      =	[]
									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__      =	all_token_ids[i : i + self.seq_length]
											if len(_UpperCAmelCase	) == self.seq_length:
													self.current_size += 1
													yield torch.tensor(_UpperCAmelCase	)


					def 						lowerCamelCase__  (self					:		Optional[Any]   ,      _UpperCAmelCase					:		Optional[Any]=1000	)		->     Optional[int]:




							"""simple docstring"""


							return ShufflerIterDataPipe(self   ,      buffer_size=_UpperCAmelCase	)






def 				UpperCamelCase      (			__magic_name__  :							Optional[int]  )     ->						Tuple:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							Optional[Any]  )     ->						List[str]:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							int					,					__magic_name__  :							List[Any]					,					__magic_name__  :							int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  )     ->						List[str]:
		"""simple docstring"""




		lowercase__    ,			lowercase__      =	[], []
		for n, p in model.named_parameters():
				if any(nd in n for nd in no_decay  ):
						params_without_wd.append(__magic_name__  )
				else:
						params_with_wd.append(__magic_name__  )
		return [
		    {"params": params_with_wd, "weight_decay": args.weight_decay},
		    {"params": params_without_wd, "weight_decay": 0.0},
		]






def 				UpperCamelCase      (			__magic_name__  :							Tuple					,					__magic_name__  :							Dict  )     ->						Optional[int]:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							Union[str, Any]					,					__magic_name__  :							List[str]  )     ->						str:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							str  )     ->						int:
		"""simple docstring"""




		model.eval()
		lowercase__      =	[]
		for step, batch in enumerate(__magic_name__  ):
				with torch.no_grad():
						lowercase__      =	model(__magic_name__					,					labels=__magic_name__  )
				lowercase__      =	outputs.loss.repeat(args.valid_batch_size  )
				losses.append(accelerator.gather(__magic_name__  )  )
				if args.max_eval_steps > 0 and step >= args.max_eval_steps:
						break
		lowercase__      =	torch.cat(__magic_name__  )
		lowercase__      =	losses[: eval_dataloader.dataset.current_size].mean()
		try:
				lowercase__      =	torch.exp(__magic_name__  )
		except OverflowError:
				lowercase__      =	float("""inf"""  )
		return loss.item(), perplexity.item()


# Settings
A :	Any												=     HfArgumentParser(TrainingArguments)
A :	int												=     parser.parse_args()

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

A :	Optional[int]												=     Namespace(**vars(args), **acc_state)
A :	Any												=     accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
	A :	Dict												=     Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
A						,   A :	Union[str, 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
A :	Tuple												=     AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
	model.gradient_checkpointing_enable()
A :	List[Any]												=     AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
A						,   A :	List[Any]												=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
A :	int												=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
A :	int												=     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      (			)     ->						Any:
		"""simple docstring"""




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


# Prepare everything with our `accelerator`.
A						,   A						,   A						,   A :	int												=     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 :	Union[str, Any]												=     os.path.basename(args.resume_from_checkpoint)
	else:
		# Get the most recent checkpoint
		A :	Optional[int]												=     [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 :	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
	A :	Any												=     os.path.splitext(path)[0]
	A :	List[str]												=     int(training_difference.replace('step_', ''))

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





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					( UpperCAmelCase__							):
					'''simple docstring'''






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




							"""simple docstring"""


							lowercase__      =	tokenizer
							lowercase__      =	tokenizer.bos_token_id
							lowercase__      =	dataset
							lowercase__      =	seq_length
							lowercase__      =	0
							lowercase__      =	infinite
							lowercase__      =	0
							lowercase__      =	tokenized

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

					def __iter__(self					:		Dict	)		->     Any:




							"""simple docstring"""


							lowercase__      =	iter(self.dataset	)
							lowercase__      =	True
							while more_examples:
									lowercase__    ,			lowercase__      =	[], 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__      =	iter(self.dataset	)
															self.epoch += 1
															logger.info(f'''Dataset epoch: {self.epoch}'''	)
													else:
															lowercase__      =	False
															break
									if self.tokenized:
											lowercase__      =	buffer
									else:
											lowercase__      =	self.tokenizer(_UpperCAmelCase   ,      truncation=_UpperCAmelCase	)["""input_ids"""]
									lowercase__      =	[]
									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__      =	all_token_ids[i : i + self.seq_length]
											if len(_UpperCAmelCase	) == self.seq_length:
													self.current_size += 1
													yield torch.tensor(_UpperCAmelCase	)


					def 						lowerCamelCase__  (self					:		Optional[Any]   ,      _UpperCAmelCase					:		Optional[Any]=1000	)		->     Optional[int]:




							"""simple docstring"""


							return ShufflerIterDataPipe(self   ,      buffer_size=_UpperCAmelCase	)






def 				UpperCamelCase      (			__magic_name__  :							Optional[int]  )     ->						Tuple:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							Optional[Any]  )     ->						List[str]:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							int					,					__magic_name__  :							List[Any]					,					__magic_name__  :							int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  )     ->						List[str]:
		"""simple docstring"""




		lowercase__    ,			lowercase__      =	[], []
		for n, p in model.named_parameters():
				if any(nd in n for nd in no_decay  ):
						params_without_wd.append(__magic_name__  )
				else:
						params_with_wd.append(__magic_name__  )
		return [
		    {"params": params_with_wd, "weight_decay": args.weight_decay},
		    {"params": params_without_wd, "weight_decay": 0.0},
		]






def 				UpperCamelCase      (			__magic_name__  :							Tuple					,					__magic_name__  :							Dict  )     ->						Optional[int]:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							Union[str, Any]					,					__magic_name__  :							List[str]  )     ->						str:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							str  )     ->						int:
		"""simple docstring"""




		model.eval()
		lowercase__      =	[]
		for step, batch in enumerate(__magic_name__  ):
				with torch.no_grad():
						lowercase__      =	model(__magic_name__					,					labels=__magic_name__  )
				lowercase__      =	outputs.loss.repeat(args.valid_batch_size  )
				losses.append(accelerator.gather(__magic_name__  )  )
				if args.max_eval_steps > 0 and step >= args.max_eval_steps:
						break
		lowercase__      =	torch.cat(__magic_name__  )
		lowercase__      =	losses[: eval_dataloader.dataset.current_size].mean()
		try:
				lowercase__      =	torch.exp(__magic_name__  )
		except OverflowError:
				lowercase__      =	float("""inf"""  )
		return loss.item(), perplexity.item()


# Settings
A :	Any												=     HfArgumentParser(TrainingArguments)
A :	int												=     parser.parse_args()

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

A :	Optional[int]												=     Namespace(**vars(args), **acc_state)
A :	Any												=     accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
	A :	Dict												=     Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
A						,   A :	Union[str, 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
A :	Tuple												=     AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
	model.gradient_checkpointing_enable()
A :	List[Any]												=     AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
A						,   A :	List[Any]												=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
A :	int												=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
A :	int												=     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      (			)     ->						Any:
		"""simple docstring"""




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


# Prepare everything with our `accelerator`.
A						,   A						,   A						,   A :	int												=     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 :	Union[str, Any]												=     os.path.basename(args.resume_from_checkpoint)
	else:
		# Get the most recent checkpoint
		A :	Optional[int]												=     [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 :	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
	A :	Any												=     os.path.splitext(path)[0]
	A :	List[str]												=     int(training_difference.replace('step_', ''))

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

