code: 
  
  
  '''simple docstring'''
  
  import argparse
  import os
  import re
  
  import numpy as np
  import PIL
  import torch
  from timm import create_model
  from torch.optim.lr_scheduler import OneCycleLR
  from torch.utils.data import DataLoader, Dataset
  from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
  
  from accelerate import Accelerator
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Any  			=  fname.split(os.path.sep	)[-1]
  	return re.search(R'^(.*)_\d+\.jpg$'	,	lowerCAmelCase	).groups()[0]
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		Tuple						,  _A	:		List[Any]						,  _A	:		Tuple=None						,  _A	:		Tuple=None    )						->				int:
  			__magic_name__ :					Union[str, Any]  			=  file_names
  			__magic_name__ :					Dict  			=  image_transform
  			__magic_name__ :					int  			=  label_to_id
  
  
  
  
  
  
  
  		def __len__(      self	:		Tuple    )						->				Tuple:
  			return len(self.file_names    )
  
  
  
  
  
  
  
  		def __getitem__(      self	:		Dict						,  _A	:		List[Any]    )						->				Dict:
  			__magic_name__ :					List[str]  			=  self.file_names[idx]
  			__magic_name__ :					str  			=  PIL.Image.open(_A    )
  			__magic_name__ :					Tuple  			=  raw_image.convert('RGB'    )
  			if self.image_transform is not None:
  				__magic_name__ :					Union[str, Any]  			=  self.image_transform(_A    )
  			__magic_name__ :					Dict  			=  extract_label(_A    )
  			if self.label_to_id is not None:
  				__magic_name__ :					Optional[Any]  			=  self.label_to_id[label]
  			return {"image": image, "label": label}
  
  
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					int  			=  Accelerator(cpu=args.cpu	,	mixed_precision=args.mixed_precision	)
  
  	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  	__magic_name__ :					Any  			=  config['lr']
  	__magic_name__ :					int  			=  int(config['num_epochs']	)
  	__magic_name__ :					Optional[int]  			=  int(config['seed']	)
  	__magic_name__ :					int  			=  int(config['batch_size']	)
  	__magic_name__ :					Dict  			=  config['image_size']
  	if not isinstance(lowerCAmelCase	,	(list, tuple)	):
  		__magic_name__ :					str  			=  (image_size, image_size)
  
  	# Grab all the image filenames
  	__magic_name__ :					Any  			=  [os.path.join(args.data_dir	,	lowerCAmelCase	) for fname in os.listdir(args.data_dir	) if fname.endswith('.jpg'	)]
  
  	# Build the label correspondences
  	__magic_name__ :					Dict  			=  [extract_label(lowerCAmelCase	) for fname in file_names]
  	__magic_name__ :					str  			=  list(set(lowerCAmelCase	)	)
  	id_to_label.sort()
  	__magic_name__ :					Any  			=  {lbl: i for i, lbl in enumerate(lowerCAmelCase	)}
  
  	# Set the seed before splitting the data.
  	np.random.seed(lowerCAmelCase	)
  	torch.manual_seed(lowerCAmelCase	)
  	torch.cuda.manual_seed_all(lowerCAmelCase	)
  
  	# Split our filenames between train and validation
  	__magic_name__ :					Dict  			=  np.random.permutation(len(lowerCAmelCase	)	)
  	__magic_name__ :					Optional[Any]  			=  int(0.8 * len(lowerCAmelCase	)	)
  	__magic_name__ :					Optional[int]  			=  random_perm[:cut]
  	__magic_name__ :					Any  			=  random_perm[cut:]
  
  	# For training we use a simple RandomResizedCrop
  	__magic_name__ :					Optional[Any]  			=  Compose([RandomResizedCrop(lowerCAmelCase	,	scale=(0.5, 1.0)	), ToTensor()]	)
  	__magic_name__ :					Optional[Any]  			=  PetsDataset(
  	    [file_names[i] for i in train_split]	,	image_transform=lowerCAmelCase	,	label_to_id=lowerCAmelCase	)
  
  	# For evaluation, we use a deterministic Resize
  	__magic_name__ :					int  			=  Compose([Resize(lowerCAmelCase	), ToTensor()]	)
  	__magic_name__ :					Tuple  			=  PetsDataset([file_names[i] for i in eval_split]	,	image_transform=lowerCAmelCase	,	label_to_id=lowerCAmelCase	)
  
  	# Instantiate dataloaders.
  	__magic_name__ :					Dict  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	,	num_workers=4	)
  	__magic_name__ :					Optional[Any]  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	,	num_workers=4	)
  
  	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  	__magic_name__ :					Optional[Any]  			=  create_model('resnet50d'	,	pretrained=lowerCAmelCase	,	num_classes=len(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).
  	__magic_name__ :					Optional[int]  			=  model.to(accelerator.device	)
  
  	# Freezing the base model
  	for param in model.parameters():
  		__magic_name__ :					str  			=  False
  	for param in model.get_classifier().parameters():
  		__magic_name__ :					Optional[int]  			=  True
  
  	# We normalize the batches of images to be a bit faster.
  	__magic_name__ :					Dict  			=  torch.tensor(model.default_cfg['mean']	)[None, :, None, None].to(accelerator.device	)
  	__magic_name__ :					int  			=  torch.tensor(model.default_cfg['std']	)[None, :, None, None].to(accelerator.device	)
  
  	# Instantiate optimizer
  	__magic_name__ :					Optional[Any]  			=  torch.optim.Adam(params=model.parameters()	,	lr=lr / 25	)
  
  	# Instantiate learning rate scheduler
  	__magic_name__ :					Tuple  			=  OneCycleLR(optimizer=lowerCAmelCase	,	max_lr=lowerCAmelCase	,	epochs=lowerCAmelCase	,	steps_per_epoch=len(lowerCAmelCase	)	)
  
  	# 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.
  	__magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__ :					Tuple  			=  accelerator.prepare(
  	    lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  
  	# Now we train the model
  	for epoch in range(lowerCAmelCase	):
  		model.train()
  		for step, batch in enumerate(lowerCAmelCase	):
  			# We could avoid this line since we set the accelerator with `device_placement=True`.
  			__magic_name__ :					Optional[int]  			=  {k: v.to(accelerator.device	) for k, v in batch.items()}
  			__magic_name__ :					Union[str, Any]  			=  (batch['image'] - mean) / std
  			__magic_name__ :					Tuple  			=  model(lowerCAmelCase	)
  			__magic_name__ :					List[str]  			=  torch.nn.functional.cross_entropy(lowerCAmelCase	,	batch['label']	)
  			accelerator.backward(lowerCAmelCase	)
  			optimizer.step()
  			lr_scheduler.step()
  			optimizer.zero_grad()
  
  		model.eval()
  		__magic_name__ :					Dict  			=  0
  		__magic_name__ :					Optional[Any]  			=  0
  		for _, batch in enumerate(lowerCAmelCase	):
  			# We could avoid this line since we set the accelerator with `device_placement=True`.
  			__magic_name__ :					Union[str, Any]  			=  {k: v.to(accelerator.device	) for k, v in batch.items()}
  			__magic_name__ :					Union[str, Any]  			=  (batch['image'] - mean) / std
  			with torch.no_grad():
  				__magic_name__ :					Optional[Any]  			=  model(lowerCAmelCase	)
  			__magic_name__ :					List[str]  			=  outputs.argmax(dim=-1	)
  			__magic_name__			,       __magic_name__ :					Union[str, Any]  			=  accelerator.gather_for_metrics((predictions, batch['label'])	)
  			__magic_name__ :					Union[str, Any]  			=  predictions == references
  			num_elems += accurate_preds.shape[0]
  			accurate += accurate_preds.long().sum()
  
  		__magic_name__ :					Tuple  			=  accurate.item() / num_elems
  		# Use accelerator.print to print only on the main process.
  		accelerator.print(f'epoch {epoch}: {100 * eval_metric:.2f}'	)
  
  
  
  
  
  def lowerCamelCase    (    ):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Tuple  			=  argparse.ArgumentParser(description='Simple example of training script.'	)
  	parser.add_argument('--data_dir'	,	required=lowerCAmelCase	,	help='The data folder on disk.'	)
  	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(
  	    '--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('--cpu'	,	action='store_true'	,	help='If passed, will train on the CPU.'	)
  	__magic_name__ :					Optional[Any]  			=  parser.parse_args()
  	__magic_name__ :					Optional[int]  			=  {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
  	training_function(lowerCAmelCase	,	lowerCAmelCase	)
  
  
  if __name__ == "__main__":
  							main()
style_context: 
  
  
  '''simple docstring'''
  
  import math
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if not isinstance(lowerCAmelCase	,	lowerCAmelCase	):
  		__magic_name__ :					str  			=  f'Input value of [number={number}] must be an integer'
  		raise TypeError(lowerCAmelCase	)
  	if number < 1:
  		__magic_name__ :					Tuple  			=  f'Input value of [number={number}] must be a positive integer'
  		raise ValueError(lowerCAmelCase	)
  	__magic_name__ :					Tuple  			=  [number]
  	while number != 1:
  		if number % 2 == 0:
  			__magic_name__ :					Optional[int]  			=  math.floor(math.sqrt(lowerCAmelCase	)	)
  		else:
  			__magic_name__ :					Union[str, Any]  			=  math.floor(
  			    math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	)	)
  		sequence.append(lowerCAmelCase	)
  	return sequence
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()