
import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


lowercase__							:							Optional[int]													=     logging.get_logger(__name__)

lowercase__							:							Union[str, Any]													=     OrderedDict(
    [
        ("align", "AlignProcessor"),
        ("altclip", "AltCLIPProcessor"),
        ("bark", "BarkProcessor"),
        ("blip", "BlipProcessor"),
        ("blip-2", "Blip2Processor"),
        ("bridgetower", "BridgeTowerProcessor"),
        ("chinese_clip", "ChineseCLIPProcessor"),
        ("clap", "ClapProcessor"),
        ("clip", "CLIPProcessor"),
        ("clipseg", "CLIPSegProcessor"),
        ("flava", "FlavaProcessor"),
        ("git", "GitProcessor"),
        ("groupvit", "CLIPProcessor"),
        ("hubert", "Wav2Vec2Processor"),
        ("instructblip", "InstructBlipProcessor"),
        ("layoutlmv2", "LayoutLMv2Processor"),
        ("layoutlmv3", "LayoutLMv3Processor"),
        ("markuplm", "MarkupLMProcessor"),
        ("mctct", "MCTCTProcessor"),
        ("mgp-str", "MgpstrProcessor"),
        ("oneformer", "OneFormerProcessor"),
        ("owlvit", "OwlViTProcessor"),
        ("pix2struct", "Pix2StructProcessor"),
        ("sam", "SamProcessor"),
        ("sew", "Wav2Vec2Processor"),
        ("sew-d", "Wav2Vec2Processor"),
        ("speech_to_text", "Speech2TextProcessor"),
        ("speech_to_text_2", "Speech2Text2Processor"),
        ("speecht5", "SpeechT5Processor"),
        ("trocr", "TrOCRProcessor"),
        ("tvlt", "TvltProcessor"),
        ("unispeech", "Wav2Vec2Processor"),
        ("unispeech-sat", "Wav2Vec2Processor"),
        ("vilt", "ViltProcessor"),
        ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
        ("wav2vec2", "Wav2Vec2Processor"),
        ("wav2vec2-conformer", "Wav2Vec2Processor"),
        ("wavlm", "Wav2Vec2Processor"),
        ("whisper", "WhisperProcessor"),
        ("xclip", "XCLIPProcessor"),
    ]
)

lowercase__							:							str													=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)




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



						'''simple docstring'''

						for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
												if class_name in processors:
																		__UpperCamelCase       					=     model_type_to_module_name(snake_case	)

																		__UpperCamelCase       					=     importlib.import_module(f".{module_name}"	, '''transformers.models'''	)
																		try:
																								return getattr(snake_case	, snake_case	)
																		except AttributeError:
																								continue

						for processor in PROCESSOR_MAPPING._extra_content.values():
												if getattr(snake_case	, '''__name__'''	, snake_case	) == class_name:
																		return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
						__UpperCamelCase       					=     importlib.import_module('''transformers'''	)
						if hasattr(snake_case	, snake_case	):
												return getattr(snake_case	, snake_case	)

						return None






class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							def __init__(					self     )->				Optional[Any]:





													'''simple docstring'''






													raise EnvironmentError(
													    '''AutoProcessor is designed to be instantiated '''
													    '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''     )



							@classmethod
							@replace_list_option_in_docstrings(SCREAMING_SNAKE_CASE_     )
							def       A__						(					cls       ,		SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     kwargs.pop('''config'''       ,		SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     kwargs.pop('''trust_remote_code'''       ,		SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     True

													__UpperCamelCase       					=     None
													__UpperCamelCase       					=     None

													# First, let's see if we have a preprocessor config.
													# Filter the kwargs for `get_file_from_repo`.
													__UpperCamelCase       					=     {
													    key: kwargs[key] for key in inspect.signature(SCREAMING_SNAKE_CASE_     ).parameters.keys() if key in kwargs
													}
													# Let's start by checking whether the processor class is saved in an image processor
													__UpperCamelCase       					=     get_file_from_repo(
													    SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
													if preprocessor_config_file is not None:
																			__UpperCamelCase					,							__UpperCamelCase       					=     ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     config_dict.get('''processor_class'''       ,		SCREAMING_SNAKE_CASE_     )
																			if "AutoProcessor" in config_dict.get('''auto_map'''       ,		{}     ):
																									__UpperCamelCase       					=     config_dict['''auto_map''']['''AutoProcessor''']

        # If not found, let's check whether the processor class is saved in a feature extractor config
													if preprocessor_config_file is not None and processor_class is None:
																			__UpperCamelCase					,							__UpperCamelCase       					=     FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     config_dict.get('''processor_class'''       ,		SCREAMING_SNAKE_CASE_     )
																			if "AutoProcessor" in config_dict.get('''auto_map'''       ,		{}     ):
																									__UpperCamelCase       					=     config_dict['''auto_map''']['''AutoProcessor''']

													if processor_class is None:
																			# Next, let's check whether the processor class is saved in a tokenizer
																			__UpperCamelCase       					=     get_file_from_repo(
																			    SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
																			if tokenizer_config_file is not None:
																									with open(SCREAMING_SNAKE_CASE_       ,		encoding='''utf-8'''     ) as reader:
																															__UpperCamelCase       					=     json.load(SCREAMING_SNAKE_CASE_     )

																									__UpperCamelCase       					=     config_dict.get('''processor_class'''       ,		SCREAMING_SNAKE_CASE_     )
																									if "AutoProcessor" in config_dict.get('''auto_map'''       ,		{}     ):
																															__UpperCamelCase       					=     config_dict['''auto_map''']['''AutoProcessor''']

													if processor_class is None:
																			# Otherwise, load config, if it can be loaded.
																			if not isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ):
																									__UpperCamelCase       					=     AutoConfig.from_pretrained(
																									    SCREAMING_SNAKE_CASE_       ,		trust_remote_code=SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )

																			# And check if the config contains the processor class.
																			__UpperCamelCase       					=     getattr(SCREAMING_SNAKE_CASE_       ,		'''processor_class'''       ,		SCREAMING_SNAKE_CASE_     )
																			if hasattr(SCREAMING_SNAKE_CASE_       ,		'''auto_map'''     ) and "AutoProcessor" in config.auto_map:
																									__UpperCamelCase       					=     config.auto_map['''AutoProcessor''']

													if processor_class is not None:
																			__UpperCamelCase       					=     processor_class_from_name(SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     processor_auto_map is not None
													__UpperCamelCase       					=     processor_class is not None or type(SCREAMING_SNAKE_CASE_     ) in PROCESSOR_MAPPING
													__UpperCamelCase       					=     resolve_trust_remote_code(
													    SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )

													if has_remote_code and trust_remote_code:
																			__UpperCamelCase       					=     get_class_from_dynamic_module(
																			    SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     kwargs.pop('''code_revision'''       ,		SCREAMING_SNAKE_CASE_     )
																			if os.path.isdir(SCREAMING_SNAKE_CASE_     ):
																									processor_class.register_for_auto_class()
																			return processor_class.from_pretrained(
																			    SCREAMING_SNAKE_CASE_       ,		trust_remote_code=SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
													elif processor_class is not None:
																			return processor_class.from_pretrained(
																			    SCREAMING_SNAKE_CASE_       ,		trust_remote_code=SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
													# Last try: we use the PROCESSOR_MAPPING.
													elif type(SCREAMING_SNAKE_CASE_     ) in PROCESSOR_MAPPING:
																			return PROCESSOR_MAPPING[type(SCREAMING_SNAKE_CASE_     )].from_pretrained(SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )

													# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
													# tokenizer.
													try:
																			return AutoTokenizer.from_pretrained(
																			    SCREAMING_SNAKE_CASE_       ,		trust_remote_code=SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
													except Exception:
																			try:
																									return AutoImageProcessor.from_pretrained(
																									    SCREAMING_SNAKE_CASE_       ,		trust_remote_code=SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
																			except Exception:
																									pass

																			try:
																									return AutoFeatureExtractor.from_pretrained(
																									    SCREAMING_SNAKE_CASE_       ,		trust_remote_code=SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
																			except Exception:
																									pass

													raise ValueError(
													    F"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
													    '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
													    '''the files of at least one of those processing classes.'''     )



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				int:





													'''simple docstring'''






													PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )






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")




