
import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union






class     SCREAMING_SNAKE_CASE__      (							enum.Enum      ):

							"""simple docstring"""






							_snake_case       						=					'timm'
							_snake_case       						=					'transformers'





def 		A_	(							snake_case							:				Optional[Iterable[str]]	, snake_case							:				Optional[Iterable[int]]	, snake_case							:				Optional[Iterable[str]]	) ->      Optional[Any]:



						'''simple docstring'''

						if stage_names is None:
												raise ValueError('''Stage_names must be set for transformers backbones'''	)

						if out_features is not None:
												if not isinstance(snake_case	, (list,)	):
																		raise ValueError(f"out_features must be a list {type(snake_case	)}"	)
												if any(feat not in stage_names for feat in out_features	):
																		raise ValueError(f"out_features must be a subset of stage_names: {stage_names} got {out_features}"	)

						if out_indices is not None:
												if not isinstance(snake_case	, (list, tuple)	):
																		raise ValueError(f"out_indices must be a list or tuple, got {type(snake_case	)}"	)
												if any(idx >= len(snake_case	) for idx in out_indices	):
																		raise ValueError('''out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'''	)

						if out_features is not None and out_indices is not None:
												if len(snake_case	) != len(snake_case	):
																		raise ValueError('''out_features and out_indices should have the same length if both are set'''	)
												if out_features != [stage_names[idx] for idx in out_indices]:
																		raise ValueError('''out_features and out_indices should correspond to the same stages if both are set'''	)




def 		A_	(							snake_case							:				Optional[List[str]]	, snake_case							:				Optional[Union[List[int], Tuple[int]]]	, snake_case							:				List[str]	, ) ->      Union[str, Any]:



						'''simple docstring'''

						if out_indices is None and out_features is None:
												__UpperCamelCase       					=     [len(snake_case	) - 1]
												__UpperCamelCase       					=     [stage_names[-1]]
						elif out_indices is None and out_features is not None:
												__UpperCamelCase       					=     [stage_names.index(snake_case	) for layer in out_features]
						elif out_features is None and out_indices is not None:
												__UpperCamelCase       					=     [stage_names[idx] for idx in out_indices]
						return out_features, out_indices




def 		A_	(							snake_case							:				Optional[List[str]]	, snake_case							:				Optional[Union[List[int], Tuple[int]]]	, snake_case							:				List[str]	, ) ->      Tuple[List[str], List[int]]:



						'''simple docstring'''

						verify_out_features_out_indices(out_features=snake_case	, out_indices=snake_case	, stage_names=snake_case	)
						__UpperCamelCase					,							__UpperCamelCase       					=     _align_output_features_output_indices(
						    out_features=snake_case	, out_indices=snake_case	, stage_names=snake_case	)
						# Verify that the aligned out_features and out_indices are valid
						verify_out_features_out_indices(out_features=snake_case	, out_indices=snake_case	, stage_names=snake_case	)
						return output_features, output_indices






class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							_snake_case       						=					None



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				None:





													'''simple docstring'''






													if getattr(self       ,		'''_backbone'''       ,		SCREAMING_SNAKE_CASE_     ) is None:
																			raise ValueError('''self._backbone must be set before calling _init_timm_backbone'''     )

													# These will diagree with the defaults for the transformers models e.g. for resnet50
													# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
													# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
													__UpperCamelCase       					=     [stage['''module'''] for stage in self._backbone.feature_info.info]
													__UpperCamelCase       					=     [stage['''num_chs'''] for stage in self._backbone.feature_info.info]
													__UpperCamelCase       					=     self._backbone.feature_info.out_indices
													__UpperCamelCase       					=     self._backbone.feature_info.module_name()

													# We verify the out indices and out features are valid
													verify_out_features_out_indices(
													    out_features=SCREAMING_SNAKE_CASE_       ,		out_indices=SCREAMING_SNAKE_CASE_       ,		stage_names=self.stage_names     )
													__UpperCamelCase					,							__UpperCamelCase       					=     out_features, out_indices



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				None:





													'''simple docstring'''






													__UpperCamelCase       					=     getattr(SCREAMING_SNAKE_CASE_       ,		'''stage_names'''     )
													__UpperCamelCase       					=     getattr(SCREAMING_SNAKE_CASE_       ,		'''out_features'''       ,		SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     getattr(SCREAMING_SNAKE_CASE_       ,		'''out_indices'''       ,		SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     stage_names
													__UpperCamelCase					,							__UpperCamelCase       					=     get_aligned_output_features_output_indices(
													    out_features=SCREAMING_SNAKE_CASE_       ,		out_indices=SCREAMING_SNAKE_CASE_       ,		stage_names=SCREAMING_SNAKE_CASE_     )
													# Number of channels for each stage. This is set in the transformer backbone model init
													__UpperCamelCase       					=     None



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				None:





													'''simple docstring'''






													__UpperCamelCase       					=     config

													__UpperCamelCase       					=     getattr(SCREAMING_SNAKE_CASE_       ,		'''use_timm_backbone'''       ,		SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

													if self.backbone_type == BackboneType.TIMM:
																			self._init_timm_backbone(SCREAMING_SNAKE_CASE_     )
													elif self.backbone_type == BackboneType.TRANSFORMERS:
																			self._init_transformers_backbone(SCREAMING_SNAKE_CASE_     )
													else:
																			raise ValueError(F"backbone_type {self.backbone_type} not supported."     )



							@property
							def       A__						(					self     )->				Optional[Any]:





													'''simple docstring'''






													return self._out_features



							@out_features.setter
							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase       					=     get_aligned_output_features_output_indices(
													    out_features=SCREAMING_SNAKE_CASE_       ,		out_indices=SCREAMING_SNAKE_CASE_       ,		stage_names=self.stage_names     )



							@property
							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													return self._out_indices



							@out_indices.setter
							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase       					=     get_aligned_output_features_output_indices(
													    out_features=SCREAMING_SNAKE_CASE_       ,		out_indices=SCREAMING_SNAKE_CASE_       ,		stage_names=self.stage_names     )



							@property
							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names     )}



							@property
							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													return [self.out_feature_channels[name] for name in self.out_features]



							def       A__						(					self       ,		*SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     dict(inspect.signature(self.forward     ).parameters     )
													__UpperCamelCase       					=     {k: v for k, v in kwargs.items() if k in signature}
													return self(*SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		)->				List[Any]:





													'''simple docstring'''






													raise NotImplementedError('''This method should be implemented by the derived class.'''     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     super().to_dict()
													__UpperCamelCase       					=     output.pop('''_out_features'''     )
													__UpperCamelCase       					=     output.pop('''_out_indices'''     )
													return output






class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							@property
							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													return self._out_features



							@out_features.setter
							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase       					=     get_aligned_output_features_output_indices(
													    out_features=SCREAMING_SNAKE_CASE_       ,		out_indices=SCREAMING_SNAKE_CASE_       ,		stage_names=self.stage_names     )



							@property
							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													return self._out_indices



							@out_indices.setter
							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase       					=     get_aligned_output_features_output_indices(
													    out_features=SCREAMING_SNAKE_CASE_       ,		out_indices=SCREAMING_SNAKE_CASE_       ,		stage_names=self.stage_names     )



							def       A__						(					self     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     super().to_dict()
													__UpperCamelCase       					=     output.pop('''_out_features'''     )
													__UpperCamelCase       					=     output.pop('''_out_indices'''     )
													return output






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     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=3.6       ,		SCREAMING_SNAKE_CASE_=False       ,		)->				Dict:





													'''simple docstring'''






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

													if self.tokenized:
																			__UpperCamelCase       					=     seq_length * num_of_sequences
																			__UpperCamelCase       					=     '''input_ids'''
													else:
																			__UpperCamelCase       					=     seq_length * chars_per_token * num_of_sequences
																			__UpperCamelCase       					=     '''content'''



							def __iter__(					self     )->				str:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_=1000     )->				int:





													'''simple docstring'''






													return ShufflerIterDataPipe(self       ,		buffer_size=SCREAMING_SNAKE_CASE_     )





def 		A_	(							snake_case							:				Any	) ->      Dict:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Any	) ->      Tuple:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Tuple	, snake_case							:				List[str]	, snake_case							:				Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	) ->      Union[str, Any]:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				int	, snake_case							:				str	) ->      List[str]:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Any	, snake_case							:				Dict	, snake_case							:				Union[str, Any]	) ->      Optional[int]:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Tuple	) ->      Union[str, Any]:



						'''simple docstring'''

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


# Settings
lowercase__							:							Optional[int]													=     HfArgumentParser(TrainingArguments)
lowercase__							:							Optional[int]													=     parser.parse_args()

# Accelerator
lowercase__							:							Dict													=     ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
lowercase__							:							Dict													=     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__							:							Union[str, Any]													=     Namespace(**vars(args), **acc_state)
lowercase__							:							List[Any]													=     accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

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

# Load dataset and dataloader
lowercase__						,					lowercase__							:							Union[str, Any]													=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowercase__							:							int													=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowercase__							:							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 		A_	(							) ->      Optional[int]:



						'''simple docstring'''

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


# Prepare everything with our `accelerator`.
lowercase__						,					lowercase__						,					lowercase__						,					lowercase__							:							List[str]													=     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[int]													=     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__							:							Dict													=     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[Any]													=     int(training_difference.replace("step_", ""))

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




