

"""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       _UpperCAmelCase     (						__a):
			def __init__(						self ,						_A ,						_A ,						_A=False ,						_A=10_24 ,						_A=10_24 ,						_A=3.6 ,						_A=False ,						)      ->      List[Any]:






					'''simple docstring'''







					_UpperCAmelCase      :							Any       =   tokenizer
					_UpperCAmelCase      :							List[str]       =   tokenizer.bos_token_id
					_UpperCAmelCase      :							List[Any]       =   dataset
					_UpperCAmelCase      :							Optional[Any]       =   seq_length
					_UpperCAmelCase      :							Optional[Any]       =   0
					_UpperCAmelCase      :							Union[str, Any]       =   infinite
					_UpperCAmelCase      :							List[Any]       =   0
					_UpperCAmelCase      :							Optional[int]       =   tokenized

					if self.tokenized:
							_UpperCAmelCase      :							Dict       =   seq_length * num_of_sequences
							_UpperCAmelCase      :							int       =   """input_ids"""
					else:
							_UpperCAmelCase      :							Optional[Any]       =   seq_length * chars_per_token * num_of_sequences
							_UpperCAmelCase      :							Tuple       =   """content"""
			def __iter__(						self       )      ->      int:






					'''simple docstring'''







					_UpperCAmelCase      :							Tuple       =   iter(self.dataset       )
					_UpperCAmelCase      :							List[Any]       =   True
					while more_examples:
							_UpperCAmelCase      :							Optional[int]       =   [], 0
							while True:
									if buffer_len >= self.max_buffer_size:
											break
									try:
											buffer.append(next(_A       )[self.content_field]       )
											buffer_len += len(buffer[-1]       )
									except StopIteration:
											if self.infinite:
													_UpperCAmelCase      :							int       =   iter(self.dataset       )
													self.epoch += 1
													logger.info(f'''Dataset epoch: {self.epoch}'''       )
											else:
													_UpperCAmelCase      :							Tuple       =   False
													break
							if self.tokenized:
									_UpperCAmelCase      :							Union[str, Any]       =   buffer
							else:
									_UpperCAmelCase      :							Any       =   self.tokenizer(_A ,						truncation=_A       )["""input_ids"""]
							_UpperCAmelCase      :							str       =   []
							for tokenized_input in tokenized_inputs:
									all_token_ids.extend(tokenized_input + [self.concat_token_id]       )
							for i in range(0 ,						len(_A       ) ,						self.seq_length       ):
									_UpperCAmelCase      :							int       =   all_token_ids[i : i + self.seq_length]
									if len(_A       ) == self.seq_length:
											self.current_size += 1
											yield torch.tensor(_A       )
			def 							__snake_case	(						self ,						_A=10_00       )      ->      Optional[int]:






					'''simple docstring'''







					return ShufflerIterDataPipe(self ,						buffer_size=_A       )
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[int]	)			->     str:
		_UpperCAmelCase      :							Dict       =   args.model_ckpt.split("""/"""	)[-1]
		_UpperCAmelCase      :							str       =   logging.getLogger(__name__	)
		_UpperCAmelCase      :							Optional[Any]       =   Path(args.save_dir	) / """log/"""
		log_dir.mkdir(exist_ok=_lowerCAmelCase	)
		_UpperCAmelCase      :							Union[str, 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(_lowerCAmelCase,					vars(_lowerCAmelCase	)	)
				_UpperCAmelCase      :							Tuple       =   accelerator.trackers[0].run.name
				logger.setLevel(logging.INFO	)
				datasets.utils.logging.set_verbosity_info()
				transformers.utils.logging.set_verbosity_info()
		else:
				_UpperCAmelCase      :							Optional[Any]       =   """"""
				logger.setLevel(logging.ERROR	)
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()
		return logger, run_name
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[Any]	)			->     int:
		_UpperCAmelCase      :							Union[str, Any]       =   {"""streaming""": True}
		_UpperCAmelCase      :							int       =   load_dataset(args.dataset_name_train,					split="""train""",					**_lowerCAmelCase	)
		_UpperCAmelCase      :							List[Any]       =   train_data.shuffle(buffer_size=args.shuffle_buffer,					seed=args.seed	)
		_UpperCAmelCase      :							Dict       =   load_dataset(args.dataset_name_valid,					split="""train""",					**_lowerCAmelCase	)
		_UpperCAmelCase      :							Dict       =   ConstantLengthDataset(
		    _lowerCAmelCase,					_lowerCAmelCase,					infinite=_lowerCAmelCase,					seq_length=args.seq_length,					tokenized=args.tokenized	)
		_UpperCAmelCase      :							Optional[Any]       =   ConstantLengthDataset(
		    _lowerCAmelCase,					_lowerCAmelCase,					infinite=_lowerCAmelCase,					seq_length=args.seq_length,					tokenized=args.tokenized	)
		_UpperCAmelCase      :							str       =   train_dataset.shuffle(buffer_size=args.shuffle_buffer	)
		_UpperCAmelCase      :							str       =   DataLoader(_lowerCAmelCase,					batch_size=args.train_batch_size,					shuffle=_lowerCAmelCase	)
		_UpperCAmelCase      :							Dict       =   DataLoader(_lowerCAmelCase,					batch_size=args.valid_batch_size	)
		return train_dataloader, eval_dataloader
def  UpperCamelCase   (     _lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Dict=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	)			->     List[str]:
		_UpperCAmelCase      :							str       =   [], []
		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  UpperCamelCase   (     _lowerCAmelCase       :  str,					_lowerCAmelCase       :  int	)			->     Union[str, Any]:
		logger.info(f'''Step {step}: {metrics}'''	)
		if accelerator.is_main_process:
				accelerator.log(_lowerCAmelCase,					_lowerCAmelCase	)
def  UpperCamelCase   (     _lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  str,					_lowerCAmelCase       :  List[str]	)			->     Union[str, Any]:
		# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
		_UpperCAmelCase      :							List[str]       =   accelerator.unwrap_model(_lowerCAmelCase	).config
		_UpperCAmelCase      :							int       =   4 if args.gradient_checkpointing else 3
		_UpperCAmelCase      :							List[Any]       =   args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
		_UpperCAmelCase      :							Any       =   24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
		_UpperCAmelCase      :							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))
		)
		_UpperCAmelCase      :							Dict       =   flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
		return tflops
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[int]	)			->     int:
		model.eval()
		_UpperCAmelCase      :							str       =   []
		for step, batch in enumerate(_lowerCAmelCase	):
				with torch.no_grad():
						_UpperCAmelCase      :							Union[str, Any]       =   model(_lowerCAmelCase,					labels=_lowerCAmelCase	)
				_UpperCAmelCase      :							Optional[Any]       =   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
		_UpperCAmelCase      :							Any       =   torch.cat(_lowerCAmelCase	)
		_UpperCAmelCase      :							Optional[Any]       =   losses[: eval_dataloader.dataset.current_size].mean()
		try:
				_UpperCAmelCase      :							List[str]       =   torch.exp(_lowerCAmelCase	)
		except OverflowError:
				_UpperCAmelCase      :							List[str]       =   float("""inf"""	)
		return loss.item(), perplexity.item()


# Settings
lowerCamelCase__			:					Optional[Any]							  =					HfArgumentParser(TrainingArguments)
lowerCamelCase__			:					List[Any]							  =					parser.parse_args()

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

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

# Clone model repository
if accelerator.is_main_process:
						lowerCamelCase__			:					List[Any]							  =					Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
lowerCamelCase__			:					Optional[int]							  =					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
lowerCamelCase__			:					int							  =					AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
						model.gradient_checkpointing_enable()
lowerCamelCase__			:					str							  =					AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowerCamelCase__			:					Optional[int]							  =					create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowerCamelCase__			:					Dict							  =					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCamelCase__			:					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  UpperCamelCase   (     )			->     Any:
		return optimizer.param_groups[0]["lr"]


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

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








"""simple docstring"""






def  UpperCamelCase   (     _lowerCAmelCase       :  int	)			->     str:
	_UpperCAmelCase      :							Any       =   len(_lowerCAmelCase	)
	for _ in range(
	    0,					int(((no_of_elements - 1) / 2) + 1	)	):  # we don't need to traverse to end of list as
		for j in range(0,					no_of_elements - 1	):
			if (
			    lst[j + 1] < lst[j]
			):  # applying bubble sort algorithm from left to right (or forwards)
				_UpperCAmelCase      :							List[str]       =   lst[j + 1]
				_UpperCAmelCase      :							Optional[int]       =   lst[j]
				_UpperCAmelCase      :							Optional[int]       =   temp
			if (
			    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
			):  # applying bubble sort algorithm from right to left (or backwards)
				_UpperCAmelCase      :							Any       =   lst[no_of_elements - 1 - j]
				_UpperCAmelCase      :							Optional[Any]       =   lst[no_of_elements - 2 - j]
				_UpperCAmelCase      :							Optional[Any]       =   temp
	return lst


if __name__ == "__main__":
						print('''enter the list to be sorted''')
						lowerCamelCase__			:					List[str]							  =					[int(x) for x in input().split()]  # inputing elements of the list in one line
						lowerCamelCase__			:					int							  =					double_sort(lst)
						print('''the sorted list is''')
						print(sorted_lst)





