
import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


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



@dataclass
class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							_snake_case       						=					42
							_snake_case       						=					field(default_factory=UpperCamelCase_      )
							_snake_case       						=					field(default_factory=UpperCamelCase_      )



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





													'''simple docstring'''






													__UpperCamelCase       					=     len(list(m.modules()     )     ) == 1 or isinstance(lowercase_       ,		nn.Convad     ) or isinstance(lowercase_       ,		nn.BatchNormad     )
													if has_not_submodules:
																			if not isinstance(lowercase_       ,		lowercase_     ):
																									self.traced.append(lowercase_     )



							def __call__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													for m in self.module.modules():
																			self.handles.append(m.register_forward_hook(self._forward_hook     )     )
													self.module(lowercase_     )
													[x.remove() for x in self.handles]
													return self



							@property
							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													return list(filter(lambda SCREAMING_SNAKE_CASE_     : len(list(x.state_dict().keys()     )     ) > 0       ,		self.traced     )     )





@dataclass
class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							_snake_case       						=					42
							_snake_case       						=					42
							_snake_case       						=					0
							_snake_case       						=					field(default_factory=UpperCamelCase_      )
							_snake_case       						=					field(default_factory=UpperCamelCase_      )



							def __call__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     Tracker(self.dest     )(lowercase_     ).parametrized
													__UpperCamelCase       					=     Tracker(self.src     )(lowercase_     ).parametrized

													__UpperCamelCase       					=     list(filter(lambda SCREAMING_SNAKE_CASE_     : type(lowercase_     ) not in self.src_skip       ,		lowercase_     )     )
													__UpperCamelCase       					=     list(filter(lambda SCREAMING_SNAKE_CASE_     : type(lowercase_     ) not in self.dest_skip       ,		lowercase_     )     )

													if len(lowercase_     ) != len(lowercase_     ):
																			raise Exception(
																			    F"Numbers of operations are different. Source module has {len(lowercase_     )} operations while"
																			    F" destination module has {len(lowercase_     )}."     )

													for dest_m, src_m in zip(lowercase_       ,		lowercase_     ):
																			dest_m.load_state_dict(src_m.state_dict()     )
																			if self.verbose == 1:
																									print(F"Transfered from={src_m} to={dest_m}"     )





def 		A_	(							snake_case							:				nn.Module	, snake_case							:				nn.Module	) ->      Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     from_model.state_dict()
						__UpperCamelCase       					=     our_model.state_dict()
						__UpperCamelCase       					=     our_model.config
						__UpperCamelCase       					=     []
						for stage_idx in range(len(config.hidden_sizes	)	):
												for block_id in range(config.depths[stage_idx]	):
																		__UpperCamelCase       					=     f"block{stage_idx + 1}.{block_id}.layer_scale_1"
																		__UpperCamelCase       					=     f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

																		all_keys.append((from_key, to_key)	)
																		__UpperCamelCase       					=     f"block{stage_idx + 1}.{block_id}.layer_scale_2"
																		__UpperCamelCase       					=     f"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

																		all_keys.append((from_key, to_key)	)

						for from_key, to_key in all_keys:
												__UpperCamelCase       					=     from_state_dict.pop(SCREAMING_SNAKE_CASE__	)

						our_model.load_state_dict(SCREAMING_SNAKE_CASE__	)
						return our_model




def 		A_	(							snake_case							:				str	, snake_case							:				VanConfig	, snake_case							:				str	, snake_case							:				nn.Module	, snake_case							:				Path	, snake_case							:				bool = True	, ) ->      Optional[Any]:



						'''simple docstring'''

						print(f"Downloading weights for {name}..."	)
						__UpperCamelCase       					=     cached_download(SCREAMING_SNAKE_CASE__	)
						print(f"Converting {name}..."	)
						__UpperCamelCase       					=     torch.load(SCREAMING_SNAKE_CASE__	)['state_dict']
						from_model.load_state_dict(SCREAMING_SNAKE_CASE__	)
						from_model.eval()
						with torch.no_grad():
												__UpperCamelCase       					=     VanForImageClassification(SCREAMING_SNAKE_CASE__	).eval()
												__UpperCamelCase       					=     ModuleTransfer(src=SCREAMING_SNAKE_CASE__	, dest=SCREAMING_SNAKE_CASE__	)
												__UpperCamelCase       					=     torch.randn((1, 3, 224, 224)	)
												module_transfer(SCREAMING_SNAKE_CASE__	)
												__UpperCamelCase       					=     copy_parameters(SCREAMING_SNAKE_CASE__	, SCREAMING_SNAKE_CASE__	)

						if not torch.allclose(from_model(SCREAMING_SNAKE_CASE__	)	, our_model(SCREAMING_SNAKE_CASE__	).logits	):
												raise ValueError('''The model logits don\'t match the original one.'''	)

						__UpperCamelCase       					=     name
						print(SCREAMING_SNAKE_CASE__	)

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name	, commit_message='''Add model'''	, use_temp_dir=SCREAMING_SNAKE_CASE__	, )

												# we can use the convnext one
												__UpperCamelCase       					=     AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name	, commit_message='''Add image processor'''	, use_temp_dir=SCREAMING_SNAKE_CASE__	, )

												print(f"Pushed {checkpoint_name}"	)




def 		A_	(							snake_case							:				Path	, snake_case							:				str = None	, snake_case							:				bool = True	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     'imagenet-1k-id2label.json'
						__UpperCamelCase       					=     1000

						__UpperCamelCase       					=     'huggingface/label-files'
						__UpperCamelCase       					=     num_labels
						__UpperCamelCase       					=     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__	, SCREAMING_SNAKE_CASE__	, repo_type='''dataset'''	)	, '''r'''	)	)
						__UpperCamelCase       					=     {int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}

						__UpperCamelCase       					=     idalabel
						__UpperCamelCase       					=     {v: k for k, v in idalabel.items()}

						__UpperCamelCase       					=     partial(SCREAMING_SNAKE_CASE__	, num_labels=SCREAMING_SNAKE_CASE__	, idalabel=SCREAMING_SNAKE_CASE__	, labelaid=SCREAMING_SNAKE_CASE__	)

						__UpperCamelCase       					=     {
						    'van-tiny': ImageNetPreTrainedConfig(
						        hidden_sizes=[32, 64, 160, 256]	, depths=[3, 3, 5, 2]	, mlp_ratios=[8, 8, 4, 4]	, ),
						    'van-small': ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]	, depths=[2, 2, 4, 2]	, mlp_ratios=[8, 8, 4, 4]	, ),
						    'van-base': ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]	, depths=[3, 3, 12, 3]	, mlp_ratios=[8, 8, 4, 4]	, ),
						    'van-large': ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]	, depths=[3, 5, 27, 3]	, mlp_ratios=[8, 8, 4, 4]	, ),
						}

						__UpperCamelCase       					=     {
						    'van-tiny': van_tiny,
						    'van-small': van_small,
						    'van-base': van_base,
						    'van-large': van_large,
						}

						__UpperCamelCase       					=     {
						    'van-tiny': (
						        'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'
						    ),
						    'van-small': (
						        'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'
						    ),
						    'van-base': (
						        'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'
						    ),
						    'van-large': (
						        'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'
						    ),
						}

						if model_name:
												convert_weight_and_push(
												    SCREAMING_SNAKE_CASE__	, names_to_config[model_name]	, checkpoint=names_to_original_checkpoints[model_name]	, from_model=names_to_original_models[model_name]()	, save_directory=SCREAMING_SNAKE_CASE__	, push_to_hub=SCREAMING_SNAKE_CASE__	, )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    SCREAMING_SNAKE_CASE__	, SCREAMING_SNAKE_CASE__	, checkpoint=names_to_original_checkpoints[model_name]	, from_model=names_to_original_models[model_name]()	, save_directory=SCREAMING_SNAKE_CASE__	, push_to_hub=SCREAMING_SNAKE_CASE__	, )


if __name__ == "__main__":
				lowercase__							:							int													=     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model-name",
				    default=None,
				    type=str,
				    help=(
				        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
				        " currently: van-tiny/small/base/large. If `None`, all of them will the converted."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=Path,
				    required=True,
				    help="Path to the output PyTorch model directory.",
				)
				parser.add_argument(
				    "--van_dir",
				    required=True,
				    type=Path,
				    help=(
				        "A path to VAN\'s original implementation directory. You can download from here:"
				        " https://github.com/Visual-Attention-Network/VAN-Classification"
				    ),
				)
				parser.add_argument(
				    "--push_to_hub",
				    default=True,
				    type=bool,
				    required=False,
				    help="If True, push model and image processor to the hub.",
				)

				lowercase__							:							Optional[int]													=     parser.parse_args()
				lowercase__							:							str													=     args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				lowercase__							:							Union[str, Any]													=     args.van_dir
				# append the path to the parents to maskformer dir
				sys.path.append(str(van_dir.parent))
				from van.models.van import van_base, van_large, van_small, van_tiny

				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






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")




