
from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
					from .data.data_collator import DataCollator
					from .modeling_utils import PreTrainedModel
					from .tokenization_utils_base import PreTrainedTokenizerBase
					from .trainer_callback import TrainerCallback
					from .trainer_utils import EvalPrediction, PredictionOutput
					from .training_args import TrainingArguments


SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					logging.get_logger(__name__)

class      lowerCAmelCase__  (			lowerCamelCase_    ):






		def __init__(	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							List[Any] = None							,    SCREAMING_SNAKE_CASE__	:							List[str] = None							,    SCREAMING_SNAKE_CASE__	:							Optional[Any] = None							,    SCREAMING_SNAKE_CASE__	:							str = None							,    SCREAMING_SNAKE_CASE__	:							Optional[Any] = None							,    SCREAMING_SNAKE_CASE__	:							List[str] = None							,    SCREAMING_SNAKE_CASE__	:							List[Any] = None							,    SCREAMING_SNAKE_CASE__	:							str = None							,    SCREAMING_SNAKE_CASE__	:							Dict = None							,    SCREAMING_SNAKE_CASE__	:							List[str] = (None, None)							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any] = None							,    )  ->							Optional[int]:
						super().__init__(
						    model=SCREAMING_SNAKE_CASE__							,    args=SCREAMING_SNAKE_CASE__							,    data_collator=SCREAMING_SNAKE_CASE__							,    train_dataset=SCREAMING_SNAKE_CASE__							,    eval_dataset=SCREAMING_SNAKE_CASE__							,    tokenizer=SCREAMING_SNAKE_CASE__							,    model_init=SCREAMING_SNAKE_CASE__							,    compute_metrics=SCREAMING_SNAKE_CASE__							,    callbacks=SCREAMING_SNAKE_CASE__							,    optimizers=SCREAMING_SNAKE_CASE__							,    preprocess_logits_for_metrics=SCREAMING_SNAKE_CASE__							,    )

						# Override self.model.generation_config if a GenerationConfig is specified in args.
						# Priority: args.generation_config > model.generation_config > default GenerationConfig.
						if self.args.generation_config is not None:
										__lowerCamelCase					      =			self.load_generation_config(self.args.generation_config						)
										__lowerCamelCase					      =			gen_config






		@staticmethod
		def 				__A (	SCREAMING_SNAKE_CASE__	:							str						)  ->							Optional[Any]:







						if isinstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						):
										return deepcopy(SCREAMING_SNAKE_CASE__						)

						# str or Path
						__lowerCamelCase					      =			Path(SCREAMING_SNAKE_CASE__						) if isinstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						) else gen_config_arg
						__lowerCamelCase					      =			None

						# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
						# This step is required in order to determine config_file_name
						if pretrained_model_name.is_file():
										__lowerCamelCase					      =			pretrained_model_name.name
										__lowerCamelCase					      =			pretrained_model_name.parent
						# dir path
						elif pretrained_model_name.is_dir():
										pass
						# model id or URL
						else:
										__lowerCamelCase					      =			gen_config_arg

						__lowerCamelCase					      =			GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
						return gen_config






		def 				__A (	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any] = None							,    SCREAMING_SNAKE_CASE__	:							str = None							,    SCREAMING_SNAKE_CASE__	:							Any = "eval"							,    **SCREAMING_SNAKE_CASE__	:							Dict							,    )  ->							Tuple:
						__lowerCamelCase					      =			gen_kwargs.copy()
						if gen_kwargs.get('''max_length'''						) is None and gen_kwargs.get('''max_new_tokens'''						) is None:
										__lowerCamelCase					      =			self.args.generation_max_length
						__lowerCamelCase					      =			(
						    gen_kwargs["num_beams"] if gen_kwargs.get('''num_beams'''						) is not None else self.args.generation_num_beams
						)
						__lowerCamelCase					      =			gen_kwargs

						return super().evaluate(SCREAMING_SNAKE_CASE__							,    ignore_keys=SCREAMING_SNAKE_CASE__							,    metric_key_prefix=SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							str = None							,    SCREAMING_SNAKE_CASE__	:							Tuple = "test"							,    **SCREAMING_SNAKE_CASE__	:							Any							,    )  ->							List[Any]:
						__lowerCamelCase					      =			gen_kwargs.copy()
						if gen_kwargs.get('''max_length'''						) is None and gen_kwargs.get('''max_new_tokens'''						) is None:
										__lowerCamelCase					      =			self.args.generation_max_length
						__lowerCamelCase					      =			(
						    gen_kwargs["num_beams"] if gen_kwargs.get('''num_beams'''						) is not None else self.args.generation_num_beams
						)
						__lowerCamelCase					      =			gen_kwargs

						return super().predict(SCREAMING_SNAKE_CASE__							,    ignore_keys=SCREAMING_SNAKE_CASE__							,    metric_key_prefix=SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Optional[int] = None							,    **SCREAMING_SNAKE_CASE__	:							List[Any]							,    )  ->							str:







						if not self.args.predict_with_generate or prediction_loss_only:
										return super().prediction_step(
										    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    prediction_loss_only=SCREAMING_SNAKE_CASE__							,    ignore_keys=SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			"labels" in inputs
						__lowerCamelCase					      =			self._prepare_inputs(SCREAMING_SNAKE_CASE__						)

						# XXX: adapt synced_gpus for fairscale as well
						# Priority (handled in generate):
						# gen_kwargs > model.generation_config > default GenerationConfig()

						if len(SCREAMING_SNAKE_CASE__						) == 0 and hasattr(self							,    '''_gen_kwargs'''						):
										__lowerCamelCase					      =			self._gen_kwargs.copy()

						if gen_kwargs.get('''max_length'''						) is None and gen_kwargs.get('''max_new_tokens'''						) is None:
										__lowerCamelCase					      =			self.model.config.max_length
						__lowerCamelCase					      =			(
						    gen_kwargs["num_beams"] if gen_kwargs.get('''num_beams'''						) is not None else self.model.config.num_beams
						)
						__lowerCamelCase					      =			True if is_deepspeed_zeroa_enabled() else False
						__lowerCamelCase					      =			(
						    gen_kwargs["synced_gpus"] if gen_kwargs.get('''synced_gpus'''						) is not None else default_synced_gpus
						)

						# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
						# (otherwise, it would continue generating from the padded `decoder_input_ids`)
						if (
						    "labels" in inputs
						    and "decoder_input_ids" in inputs
						    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
						):
										__lowerCamelCase					      =			{k: v for k, v in inputs.items() if k != "decoder_input_ids"}
						__lowerCamelCase					      =			self.model.generate(**SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)

						# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
						# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
						# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
						if self.model.generation_config._from_model_config:
										__lowerCamelCase					      =			False

						# Retrieves GenerationConfig from model.generation_config
						__lowerCamelCase					      =			self.model.generation_config
						# in case the batch is shorter than max length, the output should be padded
						if generated_tokens.shape[-1] < gen_config.max_length:
										__lowerCamelCase					      =			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__							,    gen_config.max_length						)
						elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
										__lowerCamelCase					      =			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__							,    gen_config.max_new_tokens + 1						)

						with torch.no_grad():
										if has_labels:
														with self.compute_loss_context_manager():
																		__lowerCamelCase					      =			model(**SCREAMING_SNAKE_CASE__						)
														if self.label_smoother is not None:
																		__lowerCamelCase					      =			self.label_smoother(SCREAMING_SNAKE_CASE__							,    inputs['''labels''']						).mean().detach()
														else:
																		__lowerCamelCase					      =			(outputs["loss"] if isinstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						) else outputs[0]).mean().detach()
										else:
														__lowerCamelCase					      =			None

						if self.args.prediction_loss_only:
										return loss, None, None

						if has_labels:
										__lowerCamelCase					      =			inputs["labels"]
										if labels.shape[-1] < gen_config.max_length:
														__lowerCamelCase					      =			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__							,    gen_config.max_length						)
										elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
														__lowerCamelCase					      =			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__							,    gen_config.max_new_tokens + 1						)
						else:
										__lowerCamelCase					      =			None

						return loss, generated_tokens, labels







		def 				__A (	self	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							List[str]						)  ->							List[Any]:







						if self.tokenizer is not None and hasattr(self.tokenizer							,    '''pad_token_id'''						):
										# If PAD token is not defined at least EOS token has to be defined
										__lowerCamelCase					      =			(
										    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
										)
						else:
										if self.model.config.pad_token_id is not None:
														__lowerCamelCase					      =			self.model.config.pad_token_id
										else:
														raise ValueError('''Pad_token_id must be set in the configuration of the model, in order to pad tensors'''						)

						__lowerCamelCase					      =			pad_token_id * torch.ones(
						    (tensor.shape[0], max_length)							,    dtype=tensor.dtype							,    device=tensor.device						)
						__lowerCamelCase					      =			tensor
						return padded_tensor





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__  (			__lowercase    ):






		def __init__(	self	:							str							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Any=False							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=10_24							,    SCREAMING_SNAKE_CASE__	:							Dict=10_24							,    SCREAMING_SNAKE_CASE__	:							str=3.6							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=False							,    )  ->							Optional[int]:
						__lowerCamelCase					      =			tokenizer
						__lowerCamelCase					      =			tokenizer.bos_token_id
						__lowerCamelCase					      =			dataset
						__lowerCamelCase					      =			seq_length
						__lowerCamelCase					      =			0
						__lowerCamelCase					      =			infinite
						__lowerCamelCase					      =			0
						__lowerCamelCase					      =			tokenized

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






		def __iter__(	self	:							Tuple						)  ->							Dict:
						__lowerCamelCase					      =			iter(self.dataset						)
						__lowerCamelCase					      =			True
						while more_examples:
										__lowerCamelCase      ,					__lowerCamelCase					      =			[], 0
										while True:
														if buffer_len >= self.max_buffer_size:
																		break
														try:
																		buffer.append(next(SCREAMING_SNAKE_CASE__						)[self.content_field]						)
																		buffer_len += len(buffer[-1]						)
														except StopIteration:
																		if self.infinite:
																						__lowerCamelCase					      =			iter(self.dataset						)
																						self.epoch += 1
																						logger.info(f'''Dataset epoch: {self.epoch}'''						)
																		else:
																						__lowerCamelCase					      =			False
																						break
										if self.tokenized:
														__lowerCamelCase					      =			buffer
										else:
														__lowerCamelCase					      =			self.tokenizer(SCREAMING_SNAKE_CASE__							,    truncation=SCREAMING_SNAKE_CASE__						)['''input_ids''']
										__lowerCamelCase					      =			[]
										for tokenized_input in tokenized_inputs:
														all_token_ids.extend(tokenized_input + [self.concat_token_id]						)
										for i in range(0							,    len(SCREAMING_SNAKE_CASE__						)							,    self.seq_length						):
														__lowerCamelCase					      =			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	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							str=10_00						)  ->							Union[str, Any]:
						return ShufflerIterDataPipe(self							,    buffer_size=SCREAMING_SNAKE_CASE__						)





def 				__magic_name__				(     __lowerCAmelCase			:		Tuple							)    ->     Dict:
				__lowerCamelCase					      =			args.model_ckpt.split('''/'''							)[-1]
				__lowerCamelCase					      =			logging.getLogger(__name__							)
				__lowerCamelCase					      =			Path(args.save_dir							) / '''log/'''
				log_dir.mkdir(exist_ok=__lowerCAmelCase							)
				__lowerCamelCase					      =			f'''debug_{accelerator.process_index}.log'''
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,						datefmt='''%m/%d/%Y %H:%M:%S'''	,						level=logging.INFO	,						handlers=[logging.FileHandler(log_dir / filename							), logging.StreamHandler()]	,						)
				if accelerator.is_main_process:  # we only want to setup logging once
								accelerator.init_trackers(__lowerCAmelCase	,						vars(__lowerCAmelCase							)							)
								__lowerCamelCase					      =			accelerator.trackers[0].run.name
								logger.setLevel(logging.INFO							)
								datasets.utils.logging.set_verbosity_info()
								transformers.utils.logging.set_verbosity_info()
				else:
								__lowerCamelCase					      =			''''''
								logger.setLevel(logging.ERROR							)
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				return logger, run_name




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




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




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Union[str, Any]							)    ->     str:
				logger.info(f'''Step {step}: {metrics}'''							)
				if accelerator.is_main_process:
								accelerator.log(__lowerCAmelCase	,						__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		Any							)    ->     Dict:
				# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
				__lowerCamelCase					      =			accelerator.unwrap_model(__lowerCAmelCase							).config
				__lowerCamelCase					      =			4 if args.gradient_checkpointing else 3
				__lowerCamelCase					      =			args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
				__lowerCamelCase					      =			24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
				__lowerCamelCase					      =			factor * (
				    1.0
				    + (args.seq_length / (6.0 * config_model.n_embd))
				    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
				)
				__lowerCamelCase					      =			flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
				return tflops




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


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

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

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

# Clone model repository
if accelerator.is_main_process:
					SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[str]      							=					setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
					hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
					model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE__     :    Tuple      							=					AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[str]      							=					create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE__     :    Tuple      							=					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 				__magic_name__				(     )    ->     Any:
				return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    Tuple      							=					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__     :    Tuple      							=					os.path.basename(args.resume_from_checkpoint)
					else:
										# Get the most recent checkpoint
										SCREAMING_SNAKE_CASE__     :    str      							=					[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__     :    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
					SCREAMING_SNAKE_CASE__     :    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__     :    Any      							=					time.time()
SCREAMING_SNAKE_CASE__     :    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__     :    Optional[int]      							=					model(batch, labels=batch, use_cache=False).loss
					SCREAMING_SNAKE_CASE__     :    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()})
					SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					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__     :    Union[str, Any]      							=					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__     :    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,
										    },
										)
										SCREAMING_SNAKE_CASE__     :    Tuple      							=					time.time()
										SCREAMING_SNAKE_CASE__     :    str      							=					0
										completed_steps += 1
					if step % args.save_checkpoint_steps == 0:
										logger.info("Evaluating and saving model checkpoint")
										SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					evaluate(args)
										log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
										accelerator.wait_for_everyone()
										SCREAMING_SNAKE_CASE__     :    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=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__     :    List[Any]      							=					evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					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")



