


'''simple docstring'''







import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def 						_UpperCamelCase							( ):






				"""simple docstring"""



				def filter_log(UpperCamelCase__		):
								return False if "This IS expected if you are initializing" in record.msg else True

				__magic_name__				:					Optional[int]						       =       datasets.utils.logging.get_logger("transformers.modeling_utils"		)
				logger.addFilter(UpperCamelCase__		)
				try:
								yield
				finally:
								logger.removeFilter(UpperCamelCase__		)


_SCREAMING_SNAKE_CASE       :   Dict        = "\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

_SCREAMING_SNAKE_CASE       :   Tuple        = "\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

_SCREAMING_SNAKE_CASE       :   Dict        = "\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,  _KWARGS_DESCRIPTION		)
class 		_snake_case    (				datasets.Metric		):






						'''simple docstring'''







						def lowerCAmelCase__ (  self:	str     )							->	Dict:
										return datasets.MetricInfo(
										    description=_DESCRIPTION      ,							citation=_CITATION      ,							homepage="https://github.com/Tiiiger/bert_score"      ,							inputs_description=_KWARGS_DESCRIPTION      ,							features=datasets.Features(
										        {
										            "predictions": datasets.Value("string"      ,							id="sequence"     ),
										            "references": datasets.Sequence(datasets.Value("string"      ,							id="sequence"     )      ,							id="references"     ),
										        }     )      ,							codebase_urls=["https://github.com/Tiiiger/bert_score"]      ,							reference_urls=[
										        "https://github.com/Tiiiger/bert_score",
										        "https://arxiv.org/abs/1904.09675",
										    ]      ,							)







						def lowerCAmelCase__ (  self:	List[Any]      ,							__UpperCamelCase:	int      ,							__UpperCamelCase:	Any      ,							__UpperCamelCase:	Tuple=None      ,							__UpperCamelCase:	Tuple=None      ,							__UpperCamelCase:	Optional[Any]=None      ,							__UpperCamelCase:	Optional[int]=False      ,							__UpperCamelCase:	str=False      ,							__UpperCamelCase:	Tuple=None      ,							__UpperCamelCase:	List[str]=64      ,							__UpperCamelCase:	Union[str, Any]=4      ,							__UpperCamelCase:	Tuple=False      ,							__UpperCamelCase:	Dict=False      ,							__UpperCamelCase:	Optional[int]=None      ,							__UpperCamelCase:	Optional[Any]=False      ,							)							->	Optional[Any]:
										__magic_name__				:					Dict						       =       bert_score.utils.get_hash
										__magic_name__				:					int						       =       bert_score.BERTScorer

										if version.parse(bert_score.__version__     ) >= version.parse("0.3.10"     ):
														__magic_name__				:					List[str]						       =       functools.partial(__UpperCamelCase      ,							use_fast_tokenizer=__UpperCamelCase     )
														__magic_name__				:					List[Any]						       =       functools.partial(__UpperCamelCase      ,							use_fast_tokenizer=__UpperCamelCase     )
										elif use_fast_tokenizer:
														raise ImportWarning(
														    "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
														    "You can install it with `pip install \"bert-score>=0.3.10\"`."     )

										if model_type is None:
														assert lang is not None, "either lang or model_type should be specified"
														__magic_name__				:					Optional[int]						       =       bert_score.utils.langamodel[lang.lower()]

										if num_layers is None:
														__magic_name__				:					Optional[Any]						       =       bert_score.utils.modelalayers[model_type]

										__magic_name__				:					Tuple						       =       get_hash(
										    model=__UpperCamelCase      ,							num_layers=__UpperCamelCase      ,							idf=__UpperCamelCase      ,							rescale_with_baseline=__UpperCamelCase      ,							use_custom_baseline=baseline_path is not None      ,							)

										with filter_logging_context():
														if not hasattr(self      ,							"cached_bertscorer"     ) or self.cached_bertscorer.hash != hashcode:
																		__magic_name__				:					Tuple						       =       scorer(
																		    model_type=__UpperCamelCase      ,							num_layers=__UpperCamelCase      ,							batch_size=__UpperCamelCase      ,							nthreads=__UpperCamelCase      ,							all_layers=__UpperCamelCase      ,							idf=__UpperCamelCase      ,							device=__UpperCamelCase      ,							lang=__UpperCamelCase      ,							rescale_with_baseline=__UpperCamelCase      ,							baseline_path=__UpperCamelCase      ,							)

										((__magic_name__)       ,     (__magic_name__)       ,     (__magic_name__))				:					str						       =       self.cached_bertscorer.score(
										    cands=__UpperCamelCase      ,							refs=__UpperCamelCase      ,							verbose=__UpperCamelCase      ,							batch_size=__UpperCamelCase      ,							)
										__magic_name__				:					int						       =       {
										    "precision": P.tolist(),
										    "recall": R.tolist(),
										    "f1": F.tolist(),
										    "hashcode": hashcode,
										}
										return output_dict







						def lowerCAmelCase__ (  self:	Optional[int]      ,							__UpperCamelCase:	Union[str, Any]=None      ,							__UpperCamelCase:	Dict=None      ,							**__UpperCamelCase:	Union[str, Any]     )							->	Tuple:
										# References can be strings or lists of strings
										# Let's change strings to lists of strings with one element
										if references is not None:
														__magic_name__				:					List[Any]						       =       [[ref] if isinstance(__UpperCamelCase      ,							__UpperCamelCase     ) else ref for ref in references]
										super().add_batch(predictions=__UpperCamelCase      ,							references=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							__UpperCamelCase:	Union[str, Any]=None      ,							__UpperCamelCase:	List[str]=None      ,							**__UpperCamelCase:	List[str]     )							->	List[Any]:
										# References can be strings or lists of strings
										# Let's change strings to lists of strings with one element
										if isinstance(__UpperCamelCase      ,							__UpperCamelCase     ):
														__magic_name__				:					int						       =       [reference]
										super().add(prediction=__UpperCamelCase      ,							reference=__UpperCamelCase      ,							**__UpperCamelCase     )



'''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 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						def __init__(  self:	List[Any]      ,							__UpperCamelCase:	str      ,							__UpperCamelCase:	Optional[Any]      ,							__UpperCamelCase:	Optional[int]=False      ,							__UpperCamelCase:	Optional[int]=1024      ,							__UpperCamelCase:	Union[str, Any]=1024      ,							__UpperCamelCase:	Optional[int]=3.6      ,							__UpperCamelCase:	str=False      ,							)							->	Optional[int]:
										__magic_name__				:					Union[str, Any]						       =       tokenizer
										__magic_name__				:					Any						       =       tokenizer.bos_token_id
										__magic_name__				:					Optional[int]						       =       dataset
										__magic_name__				:					List[Any]						       =       seq_length
										__magic_name__				:					Union[str, Any]						       =       0
										__magic_name__				:					Dict						       =       infinite
										__magic_name__				:					Any						       =       0
										__magic_name__				:					int						       =       tokenized

										if self.tokenized:
														__magic_name__				:					Dict						       =       seq_length * num_of_sequences
														__magic_name__				:					Optional[Any]						       =       "input_ids"
										else:
														__magic_name__				:					str						       =       seq_length * chars_per_token * num_of_sequences
														__magic_name__				:					Tuple						       =       "content"







						def __iter__(  self:	List[Any]     )							->	Dict:
										__magic_name__				:					Dict						       =       iter(self.dataset     )
										__magic_name__				:					Any						       =       True
										while more_examples:
														__magic_name__       ,     __magic_name__				:					Any						       =       [], 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:
																										__magic_name__				:					Union[str, Any]						       =       iter(self.dataset     )
																										self.epoch += 1
																										logger.info(f"""Dataset epoch: {self.epoch}"""     )
																						else:
																										__magic_name__				:					Any						       =       False
																										break
														if self.tokenized:
																		__magic_name__				:					str						       =       buffer
														else:
																		__magic_name__				:					Any						       =       self.tokenizer(__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
														__magic_name__				:					List[str]						       =       []
														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     ):
																		__magic_name__				:					Optional[int]						       =       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:	Tuple      ,							__UpperCamelCase:	str=1000     )							->	List[str]:
										return ShufflerIterDataPipe(self      ,							buffer_size=__UpperCamelCase     )






def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					str						       =       accelerator.unwrap_model(UpperCamelCase__		).config
				__magic_name__				:					str						       =       4 if args.gradient_checkpointing else 3
				__magic_name__				:					Any						       =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
				__magic_name__				:					int						       =       24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
				__magic_name__				:					Optional[Any]						       =       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))
				)
				__magic_name__				:					Union[str, Any]						       =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
				return tflops



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



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


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

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

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

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

# Logging
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Dict        = 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
_SCREAMING_SNAKE_CASE       :   Any        = AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
_SCREAMING_SNAKE_CASE       :   Optional[Any]        = AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Dict        = create_dataloaders(args)

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






				"""simple docstring"""



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


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

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