



'''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		    =     16
__lowerCAmelCase		    =     32



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Any:
		# Initialize accelerator
		if args.with_tracking:
				_a  :   Any				 =							Accelerator(
				    cpu=args.cpu		,    mixed_precision=args.mixed_precision		,    log_with='all'		,    project_dir=args.project_dir						)
		else:
				_a  :   Dict				 =							Accelerator(cpu=args.cpu		,    mixed_precision=args.mixed_precision						)

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

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

		_a  :   Any				 =							AutoTokenizer.from_pretrained('bert-base-cased'						)
		_a  :   Tuple				 =							load_dataset('glue'		,    'mrpc'						)
		_a  :   str				 =							evaluate.load('glue'		,    'mrpc'						)

		def tokenize_function(lowerCAmelCase_						):
				# max_length=None => use the model max length (it's actually the default)
				_a  :   Union[str, Any]				 =							tokenizer(examples['sentence1']		,    examples['sentence2']		,    truncation=lowercase__		,    max_length=lowercase__						)
				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():
				_a  :   int				 =							datasets.map(
				    lowercase__		,    batched=lowercase__		,    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
		_a  :   Union[str, Any]				 =							tokenized_datasets.rename_column('label'		,    'labels'						)

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

		def collate_fn(lowerCAmelCase_						):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				_a  :   Tuple				 =							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":
						_a  :   int				 =							16
				elif accelerator.mixed_precision != "no":
						_a  :   Tuple				 =							8
				else:
						_a  :   Dict				 =							None

				return tokenizer.pad(
				    lowercase__		,    padding='longest'		,    max_length=lowercase__		,    pad_to_multiple_of=lowercase__		,    return_tensors='pt'		,    )

		# Instantiate dataloaders.
		_a  :   Optional[int]				 =							DataLoader(
		    tokenized_datasets['train']		,    shuffle=lowercase__		,    collate_fn=lowercase__		,    batch_size=lowercase__						)
		_a  :   List[Any]				 =							DataLoader(
		    tokenized_datasets['validation']		,    shuffle=lowercase__		,    collate_fn=lowercase__		,    batch_size=lowercase__						)

		set_seed(lowercase__						)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_a  :   Optional[Any]				 =							AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,    return_dict=lowercase__						)

		# 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).
		_a  :   Optional[Any]				 =							model.to(accelerator.device						)

		# Instantiate optimizer
		_a  :   Any				 =							AdamW(params=model.parameters()		,    lr=lowercase__						)

		# Instantiate scheduler
		_a  :   Dict				 =							get_linear_schedule_with_warmup(
		    optimizer=lowercase__		,    num_warmup_steps=100		,    num_training_steps=(len(lowercase__						) * 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.
		_a ,      _a ,      _a ,      _a ,      _a  :   str				 =							accelerator.prepare(
		    lowercase__		,    lowercase__		,    lowercase__		,    lowercase__		,    lowercase__						)

		# We need to keep track of how many total steps we have iterated over
		_a  :   List[str]				 =							0
		# We also need to keep track of the stating epoch so files are named properly
		_a  :   Optional[Any]				 =							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						)
						_a  :   Any				 =							os.path.basename(args.resume_from_checkpoint						)
				else:
						# Get the most recent checkpoint
						_a  :   Any				 =							[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
						dirs.sort(key=os.path.getctime						)
						_a  :   str				 =							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				_a  :   Dict				 =							os.path.splitext(lowercase__						)[0]

				if "epoch" in training_difference:
						_a  :   str				 =							int(training_difference.replace('epoch_'		,    ''						)						) + 1
						_a  :   Dict				 =							None
				else:
						_a  :   Any				 =							int(training_difference.replace('step_'		,    ''						)						)
						_a  :   Tuple				 =							resume_step // len(lowercase__						)
						resume_step -= starting_epoch * len(lowercase__						)

    # Now we train the model
		for epoch in range(lowercase__		,    lowercase__						):
				model.train()
				if args.with_tracking:
						_a  :   Optional[Any]				 =							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
						_a  :   int				 =							accelerator.skip_first_batches(lowercase__		,    lowercase__						)
						overall_step += resume_step
				else:
						# After the first iteration though, we need to go back to the original dataloader
						_a  :   Optional[int]				 =							train_dataloader
				for step, batch in enumerate(lowercase__						):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device						)
						_a  :   Optional[Any]				 =							model(**lowercase__						)
						_a  :   List[str]				 =							outputs.loss
						_a  :   str				 =							loss / gradient_accumulation_steps
						# We keep track of the loss at each epoch
						if args.with_tracking:
								total_loss += loss.detach().float()
						accelerator.backward(lowercase__						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

						if isinstance(lowercase__		,    lowercase__						):
								_a  :   Optional[int]				 =							f"""step_{overall_step}"""
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												_a  :   Optional[Any]				 =							os.path.join(args.output_dir		,    lowercase__						)
										accelerator.save_state(lowercase__						)

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

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

				if checkpointing_steps == "epoch":
						_a  :   Dict				 =							f"""epoch_{epoch}"""
						if args.output_dir is not None:
								_a  :   str				 =							os.path.join(args.output_dir		,    lowercase__						)
						accelerator.save_state(lowercase__						)

		if args.with_tracking:
				accelerator.end_training()



def   __lowerCamelCase  (   )    ->   Optional[int]:
		_a  :   Dict				 =							argparse.ArgumentParser(description='Simple example of training script.'						)
		parser.add_argument(
		    '--mixed_precision'		,    type=lowercase__		,    default=lowercase__		,    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=lowercase__		,    default=lowercase__		,    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=lowercase__		,    default=lowercase__		,    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=lowercase__		,    default='.'		,    help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		,    )
		parser.add_argument(
		    '--project_dir'		,    type=lowercase__		,    default='logs'		,    help='Location on where to store experiment tracking logs` and relevent project information'		,    )
		_a  :   List[Any]				 =							parser.parse_args()
		_a  :   Optional[Any]				 =							{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(lowercase__		,    lowercase__						)


if __name__ == "__main__":
							main()









'''simple docstring'''

import math



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   list:
		if n <= 0:
				raise ValueError('Only positive integers have prime factors'						)
		_a  :   Tuple				 =							[]
		while n % 2 == 0:
				pf.append(2						)
				_a  :   str				 =							int(n / 2						)
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				while n % i == 0:
						pf.append(lowerCAmelCase_						)
						_a  :   List[Any]				 =							int(n / i						)
		if n > 2:
				pf.append(lowerCAmelCase_						)
		return pf



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		if n <= 0:
				raise ValueError('Only positive numbers are accepted'						)
		_a  :   int				 =							1
		_a  :   Union[str, Any]				 =							1
		while n % 2 == 0:
				temp += 1
				_a  :   str				 =							int(n / 2						)
		div *= temp
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				_a  :   List[str]				 =							1
				while n % i == 0:
						temp += 1
						_a  :   List[str]				 =							int(n / i						)
				div *= temp
		if n > 1:
				div *= 2
		return div



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		if n <= 0:
				raise ValueError('Only positive numbers are accepted'						)
		_a  :   List[str]				 =							1
		_a  :   Union[str, Any]				 =							1
		while n % 2 == 0:
				temp += 1
				_a  :   Tuple				 =							int(n / 2						)
		if temp > 1:
				s *= (2**temp - 1) / (2 - 1)
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				_a  :   str				 =							1
				while n % i == 0:
						temp += 1
						_a  :   Any				 =							int(n / i						)
				if temp > 1:
						s *= (i**temp - 1) / (i - 1)
		return int(lowerCAmelCase_						)



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		_a  :   Dict				 =							n
		for x in set(prime_factors(lowerCAmelCase_						)						):
				s *= (x - 1) / x
		return int(lowerCAmelCase_						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




