code: 
  
  
  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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
  						_a     =			fname.split(os.path.sep				)[-1]
  						return re.search(R'^(.*)_\d+\.jpg$'							,     _UpperCAmelCase				).groups()[0]
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=None		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=None		):
  													_a     =			file_names
  													_a     =			image_transform
  													_a     =			label_to_id
  
  
  
  
  
  
  
  							def __len__(		self  :				Any		):
  													return len(self.file_names		)
  
  
  
  
  
  
  
  							def __getitem__(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				str		):
  													_a     =			self.file_names[idx]
  													_a     =			PIL.Image.open(SCREAMING_SNAKE_CASE_		)
  													_a     =			raw_image.convert('RGB'		)
  													if self.image_transform is not None:
  																			_a     =			self.image_transform(SCREAMING_SNAKE_CASE_		)
  													_a     =			extract_label(SCREAMING_SNAKE_CASE_		)
  													if self.label_to_id is not None:
  																			_a     =			self.label_to_id[label]
  													return {"image": image, "label": label}
  
  
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Optional[int]:
  						# Initialize accelerator
  						_a     =			Accelerator(cpu=args.cpu							,     mixed_precision=args.mixed_precision				)
  
  						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  						_a     =			config['lr']
  						_a     =			int(config['num_epochs']				)
  						_a     =			int(config['seed']				)
  						_a     =			int(config['batch_size']				)
  						_a     =			config['image_size']
  						if not isinstance(_UpperCAmelCase							,     (list, tuple)				):
  												_a     =			(image_size, image_size)
  
  						# Grab all the image filenames
  						_a     =			[os.path.join(args.data_dir							,     _UpperCAmelCase				) for fname in os.listdir(args.data_dir				) if fname.endswith('.jpg'				)]
  
  						# Build the label correspondences
  						_a     =			[extract_label(_UpperCAmelCase				) for fname in file_names]
  						_a     =			list(set(_UpperCAmelCase				)				)
  						id_to_label.sort()
  						_a     =			{lbl: i for i, lbl in enumerate(_UpperCAmelCase				)}
  
  						# Set the seed before splitting the data.
  						np.random.seed(_UpperCAmelCase				)
  						torch.manual_seed(_UpperCAmelCase				)
  						torch.cuda.manual_seed_all(_UpperCAmelCase				)
  
  						# Split our filenames between train and validation
  						_a     =			np.random.permutation(len(_UpperCAmelCase				)				)
  						_a     =			int(0.8 * len(_UpperCAmelCase				)				)
  						_a     =			random_perm[:cut]
  						_a     =			random_perm[cut:]
  
  						# For training we use a simple RandomResizedCrop
  						_a     =			Compose([RandomResizedCrop(_UpperCAmelCase							,     scale=(0.5, 1.0)				), ToTensor()]				)
  						_a     =			PetsDataset(
  						    [file_names[i] for i in train_split]							,     image_transform=_UpperCAmelCase							,     label_to_id=_UpperCAmelCase				)
  
  						# For evaluation, we use a deterministic Resize
  						_a     =			Compose([Resize(_UpperCAmelCase				), ToTensor()]				)
  						_a     =			PetsDataset([file_names[i] for i in eval_split]							,     image_transform=_UpperCAmelCase							,     label_to_id=_UpperCAmelCase				)
  
  						# Instantiate dataloaders.
  						_a     =			DataLoader(_UpperCAmelCase							,     shuffle=_UpperCAmelCase							,     batch_size=_UpperCAmelCase							,     num_workers=4				)
  						_a     =			DataLoader(_UpperCAmelCase							,     shuffle=_UpperCAmelCase							,     batch_size=_UpperCAmelCase							,     num_workers=4				)
  
  						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  						_a     =			create_model('resnet50d'							,     pretrained=_UpperCAmelCase							,     num_classes=len(_UpperCAmelCase				)				)
  
  						# 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     =			model.to(accelerator.device				)
  
  						# Freezing the base model
  						for param in model.parameters():
  												_a     =			False
  						for param in model.get_classifier().parameters():
  												_a     =			True
  
  						# We normalize the batches of images to be a bit faster.
  						_a     =			torch.tensor(model.default_cfg['mean']				)[None, :, None, None].to(accelerator.device				)
  						_a     =			torch.tensor(model.default_cfg['std']				)[None, :, None, None].to(accelerator.device				)
  
  						# Instantiate optimizer
  						_a     =			torch.optim.Adam(params=model.parameters()							,     lr=lr / 25				)
  
  						# Instantiate learning rate scheduler
  						_a     =			OneCycleLR(optimizer=_UpperCAmelCase							,     max_lr=_UpperCAmelCase							,     epochs=_UpperCAmelCase							,     steps_per_epoch=len(_UpperCAmelCase				)				)
  
  						# 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     =			accelerator.prepare(
  						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  
  						# Now we train the model
  						for epoch in range(_UpperCAmelCase				):
  												model.train()
  												for step, batch in enumerate(_UpperCAmelCase				):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		_a     =			{k: v.to(accelerator.device				) for k, v in batch.items()}
  																		_a     =			(batch['image'] - mean) / std
  																		_a     =			model(_UpperCAmelCase				)
  																		_a     =			torch.nn.functional.cross_entropy(_UpperCAmelCase							,     batch['label']				)
  																		accelerator.backward(_UpperCAmelCase				)
  																		optimizer.step()
  																		lr_scheduler.step()
  																		optimizer.zero_grad()
  
  												model.eval()
  												_a     =			0
  												_a     =			0
  												for _, batch in enumerate(_UpperCAmelCase				):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		_a     =			{k: v.to(accelerator.device				) for k, v in batch.items()}
  																		_a     =			(batch['image'] - mean) / std
  																		with torch.no_grad():
  																								_a     =			model(_UpperCAmelCase				)
  																		_a     =			outputs.argmax(dim=-1				)
  																		_a ,	_a     =			accelerator.gather_for_metrics((predictions, batch['label'])				)
  																		_a     =			predictions == references
  																		num_elems += accurate_preds.shape[0]
  																		accurate += accurate_preds.long().sum()
  
  												_a     =			accurate.item() / num_elems
  												# Use accelerator.print to print only on the main process.
  												accelerator.print(f"""epoch {epoch}: {100 * eval_metric:.2f}"""				)
  
  
  def 							SCREAMING_SNAKE_CASE					(  )    ->  int:
  						_a     =			argparse.ArgumentParser(description='Simple example of training script.'				)
  						parser.add_argument('--data_dir'							,     required=_UpperCAmelCase							,     help='The data folder on disk.'				)
  						parser.add_argument(
  						    '--mixed_precision'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     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=_UpperCAmelCase							,     default=_UpperCAmelCase							,     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.'				)
  						_a     =			parser.parse_args()
  						_a     =			{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
  						training_function(_UpperCAmelCase							,     _UpperCAmelCase				)
  
  
  if __name__ == "__main__":
  		main()
  
  
  
style_context: 
  
  
  from math import pi
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
  						return degree / (180 / pi)
  
  
  if __name__ == "__main__":
  		from doctest import testmod
  
  		testmod()
  
  
  