

"""simple docstring"""






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCamelCase__			:					List[Any]							  =					16
lowerCamelCase__			:					Any							  =					32
def  UpperCamelCase   (     _lowerCAmelCase       :  int,					_lowerCAmelCase       :  Tuple	)			->     Optional[Any]:
	# Initialize accelerator
	if args.with_tracking:
		_UpperCAmelCase      :							Dict       =   Accelerator(
		    cpu=args.cpu,					mixed_precision=args.mixed_precision,					log_with="""all""",					project_dir=args.project_dir	)
	else:
		_UpperCAmelCase      :							Union[str, Any]       =   Accelerator(cpu=args.cpu,					mixed_precision=args.mixed_precision	)

	if hasattr(args.checkpointing_steps,					"""isdigit"""	):
		if args.checkpointing_steps == "epoch":
			_UpperCAmelCase      :							str       =   args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			_UpperCAmelCase      :							Tuple       =   int(args.checkpointing_steps	)
		else:
			raise ValueError(
			    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
	else:
		_UpperCAmelCase      :							Optional[Any]       =   None
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	_UpperCAmelCase      :							Union[str, Any]       =   config["""lr"""]
	_UpperCAmelCase      :							str       =   int(config["""num_epochs"""]	)
	_UpperCAmelCase      :							Any       =   int(config["""seed"""]	)
	_UpperCAmelCase      :							str       =   int(config["""batch_size"""]	)

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		_UpperCAmelCase      :							Optional[Any]       =   os.path.split(_lowerCAmelCase	)[-1].split("""."""	)[0]
		accelerator.init_trackers(_lowerCAmelCase,					_lowerCAmelCase	)

	_UpperCAmelCase      :							List[str]       =   AutoTokenizer.from_pretrained("""bert-base-cased"""	)
	_UpperCAmelCase      :							Tuple       =   load_dataset("""glue""",					"""mrpc"""	)
	_UpperCAmelCase      :							Any       =   evaluate.load("""glue""",					"""mrpc"""	)

	def tokenize_function(_lowerCAmelCase       :  Optional[int]	):
		# max_length=None => use the model max length (it's actually the default)
		_UpperCAmelCase      :							int       =   tokenizer(examples["""sentence1"""],					examples["""sentence2"""],					truncation=_lowerCAmelCase,					max_length=_lowerCAmelCase	)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		_UpperCAmelCase      :							List[Any]       =   datasets.map(
		    _lowerCAmelCase,					batched=_lowerCAmelCase,					remove_columns=["""idx""", """sentence1""", """sentence2"""],					)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	_UpperCAmelCase      :							Optional[Any]       =   tokenized_datasets.rename_column("""label""",					"""labels"""	)

	# If the batch size is too big we use gradient accumulation
	_UpperCAmelCase      :							Optional[int]       =   1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		_UpperCAmelCase      :							List[str]       =   batch_size // MAX_GPU_BATCH_SIZE
		_UpperCAmelCase      :							Union[str, Any]       =   MAX_GPU_BATCH_SIZE

	def collate_fn(_lowerCAmelCase       :  List[str]	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		_UpperCAmelCase      :							List[Any]       =   128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			_UpperCAmelCase      :							Union[str, Any]       =   16
		elif accelerator.mixed_precision != "no":
			_UpperCAmelCase      :							Any       =   8
		else:
			_UpperCAmelCase      :							Union[str, Any]       =   None

		return tokenizer.pad(
		    _lowerCAmelCase,					padding="""longest""",					max_length=_lowerCAmelCase,					pad_to_multiple_of=_lowerCAmelCase,					return_tensors="""pt""",					)

	# Instantiate dataloaders.
	_UpperCAmelCase      :							Optional[int]       =   DataLoader(
	    tokenized_datasets["""train"""],					shuffle=_lowerCAmelCase,					collate_fn=_lowerCAmelCase,					batch_size=_lowerCAmelCase	)
	_UpperCAmelCase      :							List[str]       =   DataLoader(
	    tokenized_datasets["""validation"""],					shuffle=_lowerCAmelCase,					collate_fn=_lowerCAmelCase,					batch_size=_lowerCAmelCase	)

	set_seed(_lowerCAmelCase	)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	_UpperCAmelCase      :							int       =   AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""",					return_dict=_lowerCAmelCase	)

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	_UpperCAmelCase      :							Dict       =   model.to(accelerator.device	)

	# Instantiate optimizer
	_UpperCAmelCase      :							str       =   AdamW(params=model.parameters(),					lr=_lowerCAmelCase	)

	# Instantiate scheduler
	_UpperCAmelCase      :							Any       =   get_linear_schedule_with_warmup(
	    optimizer=_lowerCAmelCase,					num_warmup_steps=100,					num_training_steps=(len(_lowerCAmelCase	) * num_epochs) // gradient_accumulation_steps,					)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	_UpperCAmelCase  ,				_UpperCAmelCase  ,				_UpperCAmelCase  ,				_UpperCAmelCase  ,				_UpperCAmelCase      :							Optional[Any]       =   accelerator.prepare(
	    _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase	)

	# We need to keep track of how many total steps we have iterated over
	_UpperCAmelCase      :							Any       =   0
	# We also need to keep track of the stating epoch so files are named properly
	_UpperCAmelCase      :							Dict       =   0

	# Potentially 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	)
			_UpperCAmelCase      :							Optional[int]       =   os.path.basename(args.resume_from_checkpoint	)
		else:
			# Get the most recent checkpoint
			_UpperCAmelCase      :							Union[str, Any]       =   [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
			dirs.sort(key=os.path.getctime	)
			_UpperCAmelCase      :							Optional[int]       =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		_UpperCAmelCase      :							Optional[Any]       =   os.path.splitext(_lowerCAmelCase	)[0]

		if "epoch" in training_difference:
			_UpperCAmelCase      :							List[Any]       =   int(training_difference.replace("""epoch_""",					""""""	)	) + 1
			_UpperCAmelCase      :							Dict       =   None
		else:
			_UpperCAmelCase      :							Optional[int]       =   int(training_difference.replace("""step_""",					""""""	)	)
			_UpperCAmelCase      :							Union[str, Any]       =   resume_step // len(_lowerCAmelCase	)
			resume_step -= starting_epoch * len(_lowerCAmelCase	)

    # Now we train the model
	for epoch in range(_lowerCAmelCase,					_lowerCAmelCase	):
		model.train()
		if args.with_tracking:
			_UpperCAmelCase      :							int       =   0
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			_UpperCAmelCase      :							str       =   accelerator.skip_first_batches(_lowerCAmelCase,					_lowerCAmelCase	)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			_UpperCAmelCase      :							str       =   train_dataloader
		for step, batch in enumerate(_lowerCAmelCase	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			_UpperCAmelCase      :							int       =   model(**_lowerCAmelCase	)
			_UpperCAmelCase      :							Tuple       =   outputs.loss
			_UpperCAmelCase      :							Tuple       =   loss / gradient_accumulation_steps
			# We keep track of the loss at each epoch
			if args.with_tracking:
				total_loss += loss.detach().float()
			accelerator.backward(_lowerCAmelCase	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

			if isinstance(_lowerCAmelCase,					_lowerCAmelCase	):
				_UpperCAmelCase      :							Dict       =   f'''step_{overall_step}'''
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						_UpperCAmelCase      :							Optional[int]       =   os.path.join(args.output_dir,					_lowerCAmelCase	)
					accelerator.save_state(_lowerCAmelCase	)

		model.eval()
		for step, batch in enumerate(_lowerCAmelCase	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			with torch.no_grad():
				_UpperCAmelCase      :							List[str]       =   model(**_lowerCAmelCase	)
			_UpperCAmelCase      :							List[Any]       =   outputs.logits.argmax(dim=-1	)
			_UpperCAmelCase  ,				_UpperCAmelCase      :							Dict       =   accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
			metric.add_batch(
			    predictions=_lowerCAmelCase,					references=_lowerCAmelCase,					)

		_UpperCAmelCase      :							Any       =   metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'''epoch {epoch}:''',					_lowerCAmelCase	)
		if args.with_tracking:
			accelerator.log(
			    {
			        """accuracy""": eval_metric["""accuracy"""],
			        """f1""": eval_metric["""f1"""],
			        """train_loss""": total_loss.item() / len(_lowerCAmelCase	),
			        """epoch""": epoch,
			    },					step=_lowerCAmelCase,					)

		if checkpointing_steps == "epoch":
			_UpperCAmelCase      :							int       =   f'''epoch_{epoch}'''
			if args.output_dir is not None:
				_UpperCAmelCase      :							str       =   os.path.join(args.output_dir,					_lowerCAmelCase	)
			accelerator.save_state(_lowerCAmelCase	)

	if args.with_tracking:
		accelerator.end_training()
def  UpperCamelCase   (     )			->     Optional[int]:
	_UpperCAmelCase      :							Dict       =   argparse.ArgumentParser(description="""Simple example of training script."""	)
	parser.add_argument(
	    """--mixed_precision""",					type=_lowerCAmelCase,					default=_lowerCAmelCase,					choices=["""no""", """fp16""", """bf16""", """fp8"""],					help="""Whether to use mixed precision. Choose"""
	    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
	    """and an Nvidia Ampere GPU.""",					)
	parser.add_argument("""--cpu""",					action="""store_true""",					help="""If passed, will train on the CPU."""	)
	parser.add_argument(
	    """--checkpointing_steps""",					type=_lowerCAmelCase,					default=_lowerCAmelCase,					help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.""",					)
	parser.add_argument(
	    """--resume_from_checkpoint""",					type=_lowerCAmelCase,					default=_lowerCAmelCase,					help="""If the training should continue from a checkpoint folder.""",					)
	parser.add_argument(
	    """--with_tracking""",					action="""store_true""",					help="""Whether to load in all available experiment trackers from the environment and use them for logging.""",					)
	parser.add_argument(
	    """--output_dir""",					type=_lowerCAmelCase,					default=""".""",					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""",					)
	parser.add_argument(
	    """--project_dir""",					type=_lowerCAmelCase,					default="""logs""",					help="""Location on where to store experiment tracking logs` and relevent project information""",					)
	_UpperCAmelCase      :							Optional[int]       =   parser.parse_args()
	_UpperCAmelCase      :							List[str]       =   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(_lowerCAmelCase,					_lowerCAmelCase	)


if __name__ == "__main__":
						main()








"""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)





